SEO Mark Williams-Cook on AI vs Google Search....Behind the Scenes

If you stand in the way of Google making money, they will flatten you. We're told make good content. That's not what Google's trying to rank. That's what they require, one of the ingredients for their system goal. And their system goal is to make ungodly amounts of money.

Welcome humans to the Neuron Podcast. I'm Cory Knowles, Joined as always by my trusty co-host and good friend, Grant Harvey.

What's up?

Today we're joined by Mark Williams-Cook, who is a, frankly, a prolific influence in the direction of modern SEO. He recently discussed how AI is creating what he calls a leaky bucket problem for search. And today we're going to learn more about that and a variety of other things. Mark serves as director at Kander, Norwich-based SEO agency, and he's also the founder of Also Asked and the co-owner of Top Dog Harnesses. Mark, welcome to The Neuron. It's great to have you.

Thank you so much for having me. Really excited to be here.

Awesome. So opening question here, and this is probably the trillion-dollar question. According to who you follow on social media, AI has either destroyed or resurrected SEO. What are your thoughts?

I mean, this is the running joke in the industry, right? So I've been working in SEO 22 years and I think I've lived through, this is maybe my 8th death of SEO now. Someone actually made a really lovely graph of the value of the SEO industry over the last 20 years, kind of going up and then marked every time it had died. So for me, there's a debate we could maybe get into later of a lot of people renaming what we do, SEO, as GEO, generative engine optimization. For me, if we have to call it GEO, which I hate, it would be a subset of SEO.

Yeah.

So I'm still doing pretty much the same things I was doing 2 years ago, 5 years ago. Um, just how I do them has changed slightly and that, and that's, that's always happened throughout the years. So no, I'm still very much in the people are asking me for SEO. We're doing stuff. People are getting results. Um, we, we, you can't get rid of us just yet, despite what public opinion may want to.

Despite every core algo update trying.

Yeah.

That's hilarious. Well, uh, Corey mentioned the leaky bucket at the top, and this was from a podcast that you did recently that we put, put you on our, uh, radar and we got really excited to talk to you about this. So can you explain the leaky bucket, uh, for our listeners and what you meant by that? And, um, do you see the bucket getting less leaky over time or more leaky?

Yeah, it's, I think it's really You can apply that leaky bucket analogy, which you're probably going to regret saying off the cuff now on that podcast, now I've been cornered about it. But it's really just about how a lot of systems that are becoming reliant on large language model-based technology, and I say that specifically rather than just the general term AI, is Because the output of a lot of these systems is non-deterministic, you know, most of the time you'll get this answer, but sometimes you'll get something a bit spicy and wild. It creates systemic issues for chains of things that need to be correct over and over. And that leads, in my opinion, to all kinds of trouble. with search engines because firstly, the scale. I think Google said recently they did 8 trillion searches last year. Like, I mean, that's how many searches they're doing, right? So even if you had a very small percentage of the results they were giving as wrong, that's still an awful lot of people getting incorrect or faulty in some way information. And tied to this, and it's tied to how the user interface, I think, of a lot of the, whether you want to call them like chat assistants or AI search surfaces work, is there's a strong user feedback loop. Even with traditional search, they watch very closely how people interact with their search result pages, what they're scrolling, what they're hovering on, what they click on when they come back. And that works quite well when you have a kind of static page of results. There's a lot of parity between searches and the user input is still messy and noisy, but you've got some control over it. I think the issue with providing things like AI overviews in Google Search is that users, for a variety of reasons, maybe won't realize the information is incorrect, but it looks like it's correct and it looks enticing and they're satisfied with it. And we start getting this impact of we're reinforcing incorrect information or bad information, and then that starts to grow and then someone else starts picking up on how that works. I mean, we already see it in the industry in terms of there was a lot written about generative engine optimization, which people basically were just guessing, right? Because as soon as anything changes in SEO, there's an ultimate guide to it like 2 days later before anyone understands it. But then of course, all of the AI search services, you know, drink this up as the answer. And then a new wave of people like, how does this work? And then they just get a definitive answer read to them with 100% confidence, and then they learn that and go and reciprocate that. And before we know it, I'm being told, no, this is how this works. How do you know that? Well, ChatGPT told me. Okay, well, how did it know? Oh, it cited this website. This website looks like it's written by AI to me. And you can follow it back. So that's one aspect of that problem that exists, and I don't think there's a good solution for it at the moment.

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That we, we had that problem like at our parent company where we went and we looked up a search result and it was conflating 2 different websites. So we, it seemed like it was taking reviews about a completely different website that was like a SaaS tool. And they were conflating it with our website, which was like a media publication. And it was taking the bad reviews from the SaaS tool and it was applying it to our media. And that was just right there in the AI overview. So that's like a huge problem, right? Because now people could then read that review and then they could think that it's talking about a different company just because it had similar names.

Absolutely. I mean, I highlighted, so our agency, as you said, is called Canda. And there is in the UK another company called Candid Talent, which is a recruitment company. And I was Googling, just checking out Perplexity results for kind of SEO agencies. And they had a kind of rich company listing result of agencies and it had pros and cons. And then it listed our cons as something like high staff turnover or something like that. And I was like, that's weird. Because we haven't lost anyone for ages. And then I dug into it a bit and it was pulling in reviews from the Candid talent agency and stitching them together.

That is such a problem.

And this is because as well, I think when people talk about AI, LLMs, and the training data that goes in, and this is related as well to a lot of people saying, oh, you should use structured data. You should use schema. So this structured markup that traditionally we put on webpages for search engines to explicitly label connections between things. So you would say, this is this website, this website is part of this company. This is the author. He or she works for this company. And you map out those connections, right? The idea is it's explicit and it removes ambiguity. Now, the way the large language models are obviously working with their training is they get given all this data, or they get given, they take whatever they like, it seems. But when that goes through the process of tokenization and such, the actual training data is not saved within the model, right? The text isn't there. All that happens is they, you know, it's broken down into these tokens, these components, and the model is the relationship between those, you know, all the different combinations of tokens, how it produces text. So the schema side of things, the structured data can't survive that process. It can kind of generate structured data because it sees those patterns together, but the fact that it once saw that the organization was Canva for this website, because that's statistically such a drop, not even drop in the bucket, that's forever gone. So it doesn't, that's not encapsulated in, I guess what you could call like the language graph, which is the way that they're storing knowledge. So that's one of the reasons why you get those issues. And I've had this with clients as well, where it thought that 2 different websites that were named similar were the same entity, if you want to call it that. And that's another pervasive problem. Google is in a much better position to guard itself from this because obviously they have 2 decades of infrastructure of web crawling metrics. They've got a knowledge graph so they can kind of ground their AI Gemini against their knowledge graph. And it's still not perfect, but they have that data. I don't believe, as far as I know, for instance, like OpenAI has a knowledge graph of these are all the things and how they're connected to ground against. So what do they do? They YOLO it till they get more money.

Pretty much. I mean, do you think this gets better with reasoning models, like better reasoning models, or like, well, will reasoning models be able to like parse that problem or is that just—

Or reinforce it.

Yeah.

I think so. For me, I think reasoning is a very interesting term to pick. And as far as I understand, it's not reasoning in any way you or I would consider that term in the human context of reasoning. It's essentially a loop of Trying to desperately fact-check whether we've hallucinated stuff, trying to recheck what averages are and find verifying information, do things like query expansion, which for me still, at the moment, feels quite basic. And I'm never going to be one to say, oh, well, this technology doesn't work because it does this, because technology is forever moving.

Right.

And I've always been, And because I always come off very negative about AI, I have been an AI maxi for years in terms of I've been telling people AI is going to be amazing. And I still think it's probably going to, well, you know, it's one of our last hopes of solving all of the general mess we've got ourselves into, right?

What a mess we have.

Yeah.

So, you know, I'm really hopeful in terms of that, you know, and there were people in the '50s being like, Oh, AI will never be able to defeat a human at chess because it's a strategic game. And it's like, I've got more than enough power in my watch to defeat 99.9% of players on the planet Earth at chess, right? It's like a super simple thing to do now.

Yeah, totally.

And then we've come on and built very specialized models. So I'm never going to be like, oh, reasoning doesn't work because it's not like human. But I think that before things get significantly better, We need a new type of technology. I don't think, from what I've seen, LLMs are going to get us to that stage we need to get. ChatGPT-5, I think, was— the response was kind of a bit meh, you know, compared to the—

Especially early on. Yeah.

The progress we'd had. And the interesting thing that I've observed from GPT-5 is, It seems more keen, because I'm very interested in when these models are doing stuff like grounding, when they're going off and doing web searches, or they're using other tools. Because the early models that came out didn't have internet connection, tiny context window, were pretty dumb. GPT-5 seems to be positioned more as a, ask me anything, okay, let me find the right tool to sort of go and use to get the answer or generate the answer or quietly write Python scripts in the background so I can get you the right answer.

Yeah.

Which I think is the right way to go. And that's, I guess, veering more towards the kind of more agentic side of things, which is, I understand the task you want me to do, and then I just need to learn how to use the tools to do that. And we've got all the MCP stuff coming now, the Model Context Protocol. So you So that's allowing these agents to interact with various sets of data and tooling just by chatting to them. That, to me, is all exciting. And I think we'll get to a better place because they've realized that knowledge cutoffs, massive problem. Hallucinations, huge, huge problem. Even the technical card for GPT-5, they have the various stats on the model release for the hallucination rates. And even under pretty good conditions, we're still in the were above 1, 2% in a lot of cases, which again, apply that to if it became Google size, trillions of searches a year.

Yeah, that's still, that's a lot when you're looking at it over a year.

That's catastrophe levels.

Yeah.

If I went into a business and I was like, look, let me run all your stuff for you. There's a 1% error rate. They'd laugh me out the room. So yeah, I'm, you know, I'm super excited about all of this. all this technology, but I just, I'm cautious through experience about how much of it as well by the AI tech industry is posturing for funding for various stakeholders that are invested. It's why they call it hallucinations, right? Because it sounds nice in a boardroom. Nobody wants to say, oh yeah, it just makes up shit once in a while. Like, you don't wanna hear that when you've invested $5 billion in it.

There was a great paper on that about a year and a half ago on, um, whether what, uh, that we should quit calling it hallucinations and start calling it bullshitting because that's what it's doing. It's doing the same thing we do when we don't know what we're talking about. Like, you know, you'll be in a conversation and you're, you're there and you know you're on your toes and, and you make these little guesses. You make these little guesses. And I've always thought that that's one of my favorite scientific papers ever, just because it's so funny. But it's also, it makes a lot of good points.

And there was the other one recently too, right, Corey? There was the one where OpenAI basically went in and said, look, we figured out why hallucinations happen. It's systemic based on the reward function. Like just for people who haven't, haven't read it, essentially they're like, we don't, we, we basically incentivize it to BS, like what Corey said. Um, because we don't give it the, we don't give it points for saying, I don't know, or we don't give it partial points for like, you know, trying, but then like realizing it's wrong.

Yeah.

It's scored just like we are on a test where like, oh, well, you know, if I can leave it blank, but, but if I put anything there, I'm going to get one of those 5 points.

Yeah. I mean, that's how I passed A-level biology on that exact principle.

Exactly.

I need to write something.

Yeah, exactly.

I was actually thinking about that paper, Grant, when you said it, because I saw that, I think it was this morning or actually just yesterday where they were like, oh, we figured it out. But it's like, and as you rightly say, it's like the fitness function, whatever you want to call it, of what you're rewarding for. But how on earth do you fix that if, you know, because you need to test it, it needs to have this. And people have, you know, when I had conversations with them about Oh, they reward the correct, you know, correct answers, just correct answers. And then the question comes up with, how do you know it's correct?

Right?

Because you need pretty much a bank of human people to verify that. Because this is a really— correctness is for me a really interesting topic from an SEO point of view, right? Because Google has said before previously, like, we don't know what is correct or not. And they rely very heavily on consensus, which I'm assuming is how a lot of the models decide whether they need to ground answers or not. So if you ask, what do red blood cells do? When they see that string of tokens, the bell curve of answers that they've seen is very steep. It's like almost everything says it's high consensus. Whereas if you say, what happened in the news today, every time they're going to come across that in training, it's going to be different. So it's going to be, I guess, flat. And they're like, okay, we need to go look for an answer. But there's all the really interesting things, right? Of arguably things that don't have an objectively correct answer, which is a lot of questions.

Oh, it's a lot.

It's a lot of questions. So then how do you train an AI to deal with that without making it The most vapid experience ever where it just replies like, oh, there's many different viewpoints to this. You know, those type of answers you get every time.

Yeah.

And it's just like, oh, like, I know, just tell me which one's best. And it's like, there's lots of ways to define best. Like, just tell me what the best one is.

Well, that actually reminds me, uh, Corey, if you don't mind, like, uh, there was the, the article that I just read recently that was, uh, from 12 Grams of Carbon talking about the problem, which is the very scientific term, uh, of ensheddification. Which is basically how over time our platforms get worse and worse and worse. And he basically broke it down where it's like there's competitive reasons for that, you know, like, and both sides, both the platforms and the users force that mechanism. But then there's the alternate take to that, which was Max Levchin, who basically said, hey, we believe that over time AI will make people make better decisions because it basically looks at all the options and it's able to parse out and pick, you You know, like let's say our credit card or our, they do, what's it called? Buy now, pay later. So basically like it's like point of sale loans. Like we think point of sale loans are actually better for the consumer than credit cards. So over time, the AIs will point you to the thing that's actually better for you. So there's this like dichotomy, which is basically, is AI gonna make everything worse with like slop onslaught, or is it gonna make us actually choose, like you said, Mark, the best option?

Yeah.

I don't know if you guys can both share your thoughts on that, but wanted to point that out.

Well, I did a talk recently and I asked the audience who regularly shops on Amazon and pretty much everyone put their hand up. And then I said, keep your hand up if you think Amazon is kind of a good ethical company you should support. Maybe surprised or not, lots of people put their hands down, right? And then we went into, well, why are you shopping from them? And it came down to essentially convenience and speed, right? You don't have to make an account. I know I get next day or same day delivery. I know the price isn't gonna be wildly more expensive than elsewhere because of the internal competition. And the talk I was doing then was actually just generally about AI and about agents and search. And the point I was making was that if you look at any technology essentially throughout history, when you can provide someone with a similar reward, a similar output for less effort, that is what is going to win because people want to spend their time doing other stuff. And the talk was looking at, okay, the future is most likely going to be Personalized agents who understand you, your preferences, your budget, your parameters. You prefer buying from companies like this. You like the color yellow. You've got size 11 feet. You know you're a man. Okay, you want trainers. This is how much like cash you have. These are the brands you like, and it will do the legwork for you, which is arguably good in terms of I get the. the product that I want and it's very efficient, but it does maybe cut out some of that discovery process. And I think it does also open us up to more, I guess, monopolies just in terms of when things become convenient, they, you know, the natural kind of way of things is they become centralized and bigger companies eat smaller ones. And then the before you know it, you can't compete. And then we end up in the alien scenario of 5 companies running everything, right?

Yep.

It's an interesting question and something I think may help this over time. I don't know if you saw this, Grant, and you may actually have, it may have even been in the newsletter. I apologize if it was. Mira Murati's company yesterday, Thinking Machines, has done this. They're, they're publishing some information and kind of finally disclosed what they're working on. And what they're doing is trying to solve determinism in models. The idea that, that, you know, if we're gonna ask the same question the same way 50 times, we're going to get 50 at least slightly different answers. And, you know, you don't want that for all occasions. There are areas where that would be really bad.

Yeah.

But there are some of these things I think it will help with. And, you know, at its core, we're still very early and we're at a time where companies are just absolutely dogpiling on, where am I in AI? What is my role? What am I going to do? Let's tack it on to every product we have and everything we touch and rebrand as an AI company. I don't know. I'm not sure where we go from there. I'm just kind of remaining confident in the idea that, you know, the researchers that we talked to and the people we've been chatting with along the way who are, who are building this stuff, they seem to be really confident that we've still got a lot of road ahead and that it's going to improve.

I mean, from a, from a search point of view, the thing that always concerns me is the, if you are a publisher online and you guys will know this, right? You have a certain responsibility in the information you publish. Like we work with clients where in regulated industries where if they're giving financial advice, like they have to be super careful. Now, somehow I'm guessing because of expensive lawyers and such, the people that are generating answers seem to not be responsible for that. You can get financial advice generated for you. And because of the way it's made, it's not classed as they have published it. That irks me from a point of view of Google especially, I feel has a responsibility in that they have 20-plus years of trust in their brand. Big company, everyone knows Google. It's the verb, right? still pretty much Google it.

Yeah.

And I've seen my parents do this where they will search for something, they will get an AI overview, and they will confidently tell me, this is the answer. And I'm like, no, actually that's wrong. Like, let's look at the site. Oh, okay. It's wrong. And they have the smallest disclaimer about AI-generated answers may be wrong, to use the polite phrase we're not going to use. And I think that's so problematic because if you Googled something and you went to a website and you saw, you know, Mark Williams-Cook or whoever wrote this, you are a human and you intrinsically understand that humans can be biased, that they can be wrong, and you have some kind of defenses up against this. Oh, you know, Mark has this political persuasion, so I know what he's writing is tinged towards this. I can see that in his writing.

Yeah.

The difficulty is when you say AI made this answer, if you went out onto the street, I'd be really interested to know how many people could tell you even roughly how AIO views are generated and about predictive text and stuff like this. I don't think many people would. And I think lots of people therefore just trust that, wow, it's AI, it must be smart. It told me it was reasoning, and it's Google. So there needs to be more education to people of like, you need to check this. And part of the talk I did on AI was all the kind of basic stuff you've seen where we get these world-class models that because of how tokenization works, can't count how many Rs are in strawberry, was like the famous one we had.

Yeah.

academic papers, you know, and academic papers are meant to be like the source of our knowledge and truth, right? Did you see the, um, the term vegetative electron microscopy coming up in these papers?

Yes. You remember that one, Corey?

No.

Basically it, it, so there was like 2 paragraphs that were next to each other and it took it as like one word, even though they're part of 2 completely separate paragraphs. And then it like spread everywhere, right, Mark?

Yeah. And you go onto Google Scholar and this has been This is now in academic papers because they've been AI-generated. And it's just this spiral. This is kind of what I mean. This is a spiraling problem that— and then we heard Google recently say, oh, we're trying to just train our models on what we identify as human content. Because that's another huge problem, right? Again, if you start training models on hallucinated data, incorrect data, and we just where it becomes like ink in the water. You can't ever then separate what's correct and what's not. So that's always irked me from a safety point of view from search. And I feel there needs to be more responsibility for them labeling, look, this is high, you need to check this. And again, I don't know, you may have covered this on previous episodes about the studies that have come out about how developers have actually been slower in a lot of cases using AI. Yeah.

We definitely talked about that in the newsletter. I don't know if we covered it in the episode, but yeah, it's like 20% slower or something like that.

And I've seen that, right? Because you write the code and you don't really, because you haven't thought it out yourself, you don't really know quite how it works. Then you've got to maintain it. Then something breaks. And then my experience of coding with AI, if you use it to debug, is it gets progressively worse, essentially. It will do a great job on that first script. You kind of add to it and then you get a problem and then stochastic programmer thing of, there's 99 bugs in the code, fix one, there's 100 bugs in the code kind of thing, but really fast with AI, right? So yeah, this is the thing that makes me nervous, like how long we're going to stay in this stage before we get to what Corey said, like this golden road, this promised land, which is just a bit further down the road, just another billion dollars down the road. Yeah.

And pretty quickly, we as humans have proven that We will absolutely trust anything pretty quick and easy. We do that in every aspect of human life. And I don't know why I thought humans would be more discerning about AI copy as far as when they're working with it and using it and making sure to give it a good overview before they're, you know, running out and publishing things or whatever the case is. And, but boy, we really don't.

Yeah.

As people, we've kind of just accepted it. Unless you're in your specific field where you have the knowledge, where you are aware of those nuances, it can be really convincingly wrong if you have a cursory knowledge of something.

Absolutely.

As opposed to being an expert in your field. And like, I find that people who are— with that said, people who are really good at a thing tend to be better at it when they understand how to use AI in a, in a smart capacity, like where it can be strong and understanding where it is not. Um, you know, and it's one of the reasons, like, I love to bounce between models and companies, but the truth is when I'm doing my work, I'm doing most of that in ChatGPT just largely because I know where it's going to fall short. Uh, I, you know, it's, it's what I've used for, if it's an important task, I'm going to use the tool that, that I know what I need to watch in as opposed to one where I'm maybe less sure. Like, I mean, it's probably just as good. You know, the truth is all of these models are, are good now by comparison to when you look back at what we were working with 2 and a half, 3 years ago. It's, uh, it's, it's—

One year ago.

One year ago.

Pre, pre, pre-reasoning. I mean, with all its flaws.

Yeah, we didn't have reasoning models.

Yeah. Yeah. Yeah. Um, with all its faults, they're still quite good.

Yeah. There's still, there's still a major leap and, uh, fingers crossed we'll have a bunch of major leaps ahead of us. I have a, I have another, another SEO question I'd like to bounce off you and get your take on. So you mentioned that as people move to AI search, that traditional link graph that Google has relied on for all of these years is going to probably rot. And I was wondering if you could walk us through why that matters. What are the impacts of that? Sure.

So I think from an AI search, AI service point of view, one of the challenges they have is understanding the source of where bits of information come from, right?

Yeah.

Because I could give you some instructions on how to fix your car engine, right? That stopped working. And I have no idea about that, but I could tell you some stuff that probably sounded right. You would much rather listen to someone who is like an experienced qualified mechanic, right? And the beautiful thing about the link graph, and this is like the foundation of why Google set itself apart as a search engine, was this PageRank algorithm, which is essentially trying to judge initially the popularity of different documents on the web based on how often they're linked to because it's a very good proxy for popularity. When people find a resource helpful, they will link to it and those votes, if we want to call them that, accumulate. So if not all votes become equal, so if everyone's got their vote and everyone say votes for me and I get 10 votes, my vote then is worth even more. Because I've already got 10 votes. So it's really interesting how this lines up. And then there's other layers to this, such as topicality. So we know, for instance, if we're looking for information about cars, there's this interesting network of sites, and these 3 sites are always linked to when people talk about cars, so they must be really good. So if I'm looking for a document about cars, while there might be 10 million on my very small internet, you know, the 10,000 that are in this little cluster, I'm going to look at first because everyone references them. And that's in a very, very, I guess, not doing it justice, but that's the basis for how I think Google really became what it is today in terms of building on top of that. Now, AI search doesn't have that. They have the text from everywhere, but working out which is the best text apart from the most common, which is the issue we came to earlier, because the consensus answer isn't necessarily the best answer when you need expert information, right? Now, it's not widely spoken about, but the large language model companies are actually buying link graph data from the companies, the SaaS companies that spend a lot of time and money making their own independent crawls of the web. And they have their own scores, which are similar to Google's in some ways. And they are selling that data, which I assume is being used during pre-training to turn the volume up on those sites because we know they are, for want of a better word, good.

None good. Yeah.

So, um, and this, this has worked really, really quite well for a long time. Um, notwithstanding, as I said before, Google uses a lot of interaction and kind of click data to, to refine what it shows, but as a core, we can trust these documents. That's still the most impactful thing you can do, I think, in terms of SEO, once you've done all the other basics is, is is, you know, earn this coverage and links from people. Now, why I said it's going to rot and what kind of worries me is a lot of the AI search tools will do grounding, i.e., they will do a search, they will do a web search. And this is happening almost exclusively through like Google and Bing. So they're doing, they're saying, okay, you've asked me this question. I know I need to check the answer. I'm going to quickly go and do 3 searches on Google, look at the first 10, 20, 30 documents, check what my model output and make sure everything's kind of hunky-dory, and then give some very scant citations to who I just nicked all the content from. Now, those results are powered by that link graph. That link graph survives because people are out there journeying the open web, clicking on links, looking at websites. But if we change to a paradigm where, well, I don't need to go to Reddit anymore to get this advice, I don't need to read this very specific guide on how to optimize my Dwarf Fortress building because I can just put it into ChatGPT, right? Therefore, I'm not going to link to it. I'm not going to even visit it. I'm not going to link to it. I'm just going to read the guide, right? So the actual living expression, which it kind of is, you know, of what is popular on the web, just starts to fade away, which will have an impact on the search results. And actually the user data, which is hugely important, also dries up because if the agent is doing the search instead of the user, you've lost that vital feedback loop of, oh, Well, everyone's skipping the first result and actually going to the second because the AI is just going, I'll take it all.

It's digesting 30 pages a second.

Yeah, I'll just take it all. So then how, as a search engine, do you firstly determine which documents on the web are reliable? That becomes very challenging. And then secondly, which ones are relevant? Which ones do people like? Because that amount of data is dying. And then if that happened, it's going to have the upstream effect on the quality of the results in the AI search is also going to deteriorate. And then I don't know where we go from there. The only thing I can think of is we've seen, and they didn't get forced to do it, obviously Google, there was possibly they might have to sell Chrome. Perplexity has launched Comet, its own browser. is that they could harness information or how people are interacting through the browser. Which I think, again, talking about earlier, the simplicity of Google results made, I think, the crunching of that user data possible. I think the variability of people asking a million different questions a million different ways, and then trying to figure out what's good and what's not based on the browser interaction. And you have to account for the fact that people might give positive signals to incorrect answers because they think it's correct. As you said, Corey, you know, you're only really asking the AI basically questions that you know the answer to, and it's helping you and inspiring you. But then it's like, what's the point in a system you can only ask questions you know the answer to?

It's almost like you need to know the identity of the person who's asking the question and their level of expertise. to be able to weight their interactions appropriately. And then that's a whole can of worms, right?

And that's contrary to all the changes we're seeing in privacy, right? Because privacy is moving the opposite direction. It's like no cookies, browsers now stopping the letterboxing, the fingerprinting with browsers. So they're adamantly trying to push, the UK, everyone's on VPNs now because we've got these sweeping online safety laws, which means I can't even send direct messages on Bluesky anymore unless I have government ID that proves I'm 18.

Wow.

So, of course, I'm just going to use a VPN. Like, yeah, but that further obfuscates who I am. So, I wouldn't say I'm worried because it's not my problem, but I'm really interested in how it's going to pan out because there's the big cheese, right? All the money is at the end of that tunnel. And it's like, who's going to blink first? Because, you know, there's a lot of people, you know, way smarter than me working on this, right? So I really hope they've thought about it. And I say hope because I've, I spoke recently to, um, uh, so this April to, uh, uh, someone working at Google and I was talking to them about Search Console and the data we get through there. And what would be helpful. And they were, they were saying like, well, why don't we just give them like this score, you know, like from, from Google about this? And he's like, no, no, no, you don't understand what happens if we tried it with like toolbar PageRank. Like it will create chaos if you give them any data like that. It becomes, you know, there was a whole market setup for link selling based on PageRank of individual pages. So it's like, it's a bad idea to give SEOs specific information.

Give them vague guidelines.

And you're an SEO person saying that. So you know that like, it's a, it's like a, you know, both sides problem.

Yeah, absolutely. I mean, we found them. We found a Google exploit last year that gave us like thousands of parameters they use in their, in their search ranking, how they classify queries. It gave us quality scores for websites. And in the end, after having a good poke around that, We did report it to Google and get their bug bounty because I was just like, if we released this, it's just, it's not going to be any good. You know, it's not going to make the web a better place. Um, you know, and it's not going to help people that are actually trying to, you know, because although I, you know, SEO has a, depending who you ask, a varied reputation. I am, you know, in Kanda, we are generally trying to help people make the best business decisions in the search environment. We're not actually interested in how I can trick the search engine or how we can manipulate it because they're short-term things. Generally, the overriding goals of these systems will be in line with making a good business, and there's lots of ways to go about that. So even if we get insight into how Google's working specifically, while that's super interesting and answers, you know, fills in a bunch of blanks, giving away or publishing that data is only going to aid people who are trying to take shortcuts and then, you know, not the types of businesses, to be honest, I want rewarded.

You know, that leads really well into another discussion we've had lately. Um, I don't want to name any names and jump on any companies about it, but we've seen A variety of AI tools that are claiming to have all of this great data about SEO and about— not about SEO specifically, but yeah, GEO, you know, to do with, you know, firsthand prompt information and these type of things, and that they're really able to get to the nitty-gritty of what they're seeing. And it I struggle to even understand how it's possible because like, I know we're in a situation where most of these labs are pretty competitive and they're, they're, and, and, and an ugly privacy situation would not be a win for them.

We saw that kind of with, um, some of the, uh, situations where like the, the chats that people were sharing was showing up in search results, right?

Yeah. That too. Um, I guess where I was going with that is, do you have any idea how that works? What, what kind of data is being used to assess these? And, and, you know, I know there are a lot of companies spending money now trying to find out, well, what should they be doing? And that's, that's kind of an interesting direction.

So you're talking about the tools that are kind of trying to look at AI visibility, right? Yeah.

Yeah. That kind of stuff.

Yeah. So I've, uh, I've been grilling these companies. So when they email me to say, oh, we've got this brilliant tool, and I'm like, great, come and explain to me how it works. And then I ask them all the questions that they don't want people to ask. And I'm like, okay, but what data and where does that come from and how are you getting that? Um, and, and it's, it's, it's a real mix. So I think you've got the, uh, traditional SaaS tool companies that have worked in the SEO space a while that have existing huge databases of keywords and search volumes and stuff. And some of them are just pumping traditional search terms like straight into AI tools to, and then seeing who gets cited, which according to them, it gives very similar results to if you ask like a natural kind of question.

Yeah.

But again, I'm skeptical just because they have a financial incentive to tell me that. And like a lot of the work we've been doing is, so without going off from your question here, when we are taking traditional kind of search terms and trying to do research for AI surfaces, if you tell ChatGPT you're a vegan one day and ask for a vegan recipe, and then you ask it for running shoes 2 days later, It's going to go, oh, I know you're a vegan. Here's cruelty-free brands and shoes without leather, which is a completely different result to if you fresh out of the box were like, give me running shoes, right? So like—

You can't prepare for that. Yeah.

Well, so what we've been doing is we will write 1, 2, 3, 4, 5, depending how many different like personas line up to that company, take the traditional search terms, use the ChatGPT API and say, okay, you are middle-aged male beginning running health-conscious vegan guy, you're after, you're looking for this information, and then you give it the traditional search terms and you say, what would you ask ChatGPT? Now, I would never recommend anyone do keyword research on an LLM for a variety of reasons, but this is not that. This is actually the perfect question that's encoded in their language graph because the language graph has been hoovering up hundreds of millions of conversations from very specific websites where people say, hey, I am this old and I'm interested in this and I'm looking for this, whether it's a running forum or it's the vegan subreddit, it's the perfect thing to ask. Then we get those prompts and then we can, there's other tools we can work further down the conversation with. So I think that's missing from that classic SaaS approach of just dumping the keywords cold into it. Some of the other tools have been taking very interesting— I think this is more the funded route where they've generated queries like that. They call them synthetic queries.

Ah.

Synthetic. They're like synthetic CDOs. They didn't cause a problem. So they take those queries and basically they just hammer them into these search into these AI search platforms to try and get the most common answer. Because that was my question to them of, well, how do you know they're being cited? Because I might get a different result. And they're like, oh, we just keep like banging our head against the wall. And look, it's like, isn't that like really expensive? They're like, yeah.

And that's why those platforms are expensive. Yeah. Right.

And there's Some very like hand-wavy clickstream data, which gets a little bit murky whenever, you know, you talk about clickstream data. That's the usual stuff of people sacrificing privacy for functionality. Like the whole free, you know, VPN game is basically, if you're using a VPN for free, it's probably because you're an exit point for people that are paying somewhere for a residential proxy that you didn't read the T's and C's about.

Yeah.

Yeah. Free VPN. Bad idea.

Yeah. Yeah, absolutely. So, um, my view, which isn't massively popular in the SEO world, is at the moment, I think the cost in terms of time, literally energy and money for the value you get from some of this tracking is too high. For me at the moment. For some of our clients, we're just setting them up with their own tracking because it's actually not too difficult. As long as you're not doing, you know, like 20,000 prompts daily, you get a kind of toe dip into, are we even showing? There's other really cool tools like What AI Knows About You, WayK, which allows you to explore some of the base models as well. And what that will do, it's really clever, is you just put in your website And it will basically start asking bunches of questions based on your website to the model to find out what it thinks about you, which I think is actually one of the most valuable things because—

That's cool.

If in like the 1930s, say, right, brand was basically what you told people, what you would stick on billboards and what you ran on television. And then, you know, as we got the web, brand became more what people say about you, what's alive on, you know, if you did something awful, it would get around the web and people would know.

Yeah.

And I think we're approaching an age where brand will become essentially what's imprinted in AI models about your company. And that's how people will ask, because finding reviews of companies and products is hard work. You know, I chose my house conveyancer by using AI this time around. I was like, go and fetch me a bunch of reviews. I want summaries of pros and cons. what they've said, you know, price ranges. And I did verify, you know, I got the top couple and I went and sort of did some fact-checking myself, but it did a good job.

You told it to make you a buyer's guide. Yeah, yeah, right. Essentially, it made you a custom buyer's guide.

Yeah. So that's awesome. I think tools like that for visibility are more useful. So I want to know what does the model kind of think about me? What shape has it got? Does it know that this thing is important to us? And they're the differentiators, right? That, so rather than the, yeah, we are this company and we sell this product, the AI needs to understand the why. Why should people buy from you?

What do you offer?

Who do you appeal to? That's more interesting to me than just hammering the AI about, oh, what does it return for this query or this one? Because as well, there's been studies that show they don't even seem to stick that long, the answers. They change quite regularly. So I just feel like it's a lot of time, money, and effort chasing something down the road when, and it's a classic SEO problem, we could be spending all that money in making a better company and making our product better and making our customers happier and working on why they should buy from us. And that will surface itself in the model, which is what I was saying with the, if we understand the goal of the model, let's aim for that. Rather than try and aim at where the model is now and how we can kind of exploit that. Manipulate it. Yeah. Yeah. Yeah. It's a good long-term strat.

I, I feel like what we're kind of talking about here is that the idea is there's a lot of noise right now and maybe the best course of action is, is focusing on doing your best darn work you can and, and, and adhering to your SEO fundamentals, kind of, is that?

Yeah, so there's definitely things that need to be done differently. So as I said, like, we've got a whole, like, new keyword research process we go through to understand how those queries might look. We map out, we've got data that maps out, like, where the conversations go. So if someone's buying running shoes, right, after they investigate the price, it's really common for people to ask something like, how much faster will expensive running shoes make me run? So they're trying to justify the purchase, right? They're like, what will this shave off my 5K time? So understanding all of that is way more important than understanding kind of the keywords. In terms of what we do day by day, some of the technical aspects are more important. So we know a lot of large language models don't do the expensive rendering of websites with JavaScript. So even more important than it has been previously to make sure all your main content links load just, you know, as it's served in the HTML. And I think digital PR, which has always been, you know, part really of SEO, or at least very adjacent, is way more important because the visibility side of things has moved on from becoming, this is our website and we must get it to rank, to we want to be seen in all of, as many of the results as possible. So if AI is doing that search and we've got our running shoes, the first one is a review website. The second one is someone's blog, whatever.

Yeah.

Like, let's send them samples. Let's get on there. Let's have them talk about us, link to us. Let's have good sentiment about us. Um, so it's like Reddit, Reddit comes up a lot at the moment in discussions about SEO, right? I like my Reddit account's old enough to drink now. It's like 19 years old, right? In the UK at least. Um, so I've been hanging around Reddit a long time and it's a very interesting place on the web.

It sure is. I love it.

A lot of internet elders on there. And it's almost awful for me now, all these marketers being like, oh yeah, we need to be on Reddit. And it's like, no, stay away.

Don't come ruin my favorite website.

Yeah.

And so I've been talking to clients about, because they're like, oh, we see Reddit's using the train, Reddit comes up a lot. And it's like, yeah. So what you need to do is become the story that the Redditors talk about, provide the data that they find interesting and then go and join in the conversation. I was like, the strategy a lot of people seem to be taking is like, oh, there's a party going on there. I'm going to climb in through the window and then just talk loudly to everyone that will listen. It's like—

Start dropping links to blog posts and—

Yeah.

Like it's, and you can see them straight away, right? On their brand new fresh account. And you look at, you know, people look at post histories on Reddit, right? one of the first things they do. It's like, you seem a bit astroturfy. Let's see. Okay.

Yeah.

Even the Reddit bots will eat them alive.

Yeah. So yeah, it's, it, that to me is like all part of what SEO is becoming, the education we've got to help clients with. And again, it's that strategic thing of you're aiming for what the system goal is, not what it's doing right now. So just because, yeah, Reddit's coming up quite a lot doesn't mean you have to like be there. Like, yeah, but there's ways to become that, that, that thing people are talking about.

Yeah.

So what, what is then in your view, technically the system goal? It's to surface the best information for the user's query? Like, is that, or is that wishful thinking?

So I had this as a, as like a slide when, um, we're going through the presentation about the Google exploit, which was understanding You know, um, Google's, um, like system goals, which there are many. And we're told as SEOs, as webmasters, as business owners, you know, make good content or make quality content, whatever they want to call it this year. And people do that and then they don't rank.

Right.

And it's because that's not what Google's trying to rank. That's what they require. One of the ingredients for their system goal and their system goal is to make ungodly amounts of money. Like that's the nowadays the overriding system goal, right? And the reason that organic results exist is because people prefer clicking on them to adverts. So you need good organic results to get people to come back to click on the adverts. And that is your place in the ecosystem. It's a, it's a nice side effect that that means Google is trying to reward good content that makes people come back. You know, but there's other contradicting goals to that, which is Google wants people to stay in their ecosystem. They don't want to leave the SERP. They ideally want them to go to YouTube because, you know, I used to make a living running AdSense websites, right? Because people go to websites, they click on the ads, brilliant. Google can make more money without the middle person, right? If you just click on the ad on the SERP, They're not giving anyone else a cut. So they are motivated to do that. So system goals is really understanding your ingredient in their picture of how do you, basically, how do you fit into their business model? Because if you—

Basically, how do you make Google money?

Yeah, it is. Because if you stand in the way of Google making money, they will flatten you and you will not be a speck on their tire and they'll be down the road. before you know it. And we've seen that happen lots. And I've advised—

Google has killed many a publisher.

Absolutely. And even before that, there are specific business types that have always been very vulnerable. It's happened in flights, hotels where Google, and they've been found guilty of having a monopoly and they've been ordered with the DMA and stuff to do various things. But certainly that's things I've spoken to clients about before, which is literally like the structure and strategy of their business and how they make money and having tough conversations with, well, I think it's likely Google's going to move into this space. So we need to think about how you can adjust your business model to make it work with Google because you can't beat them. You know, it's not going to happen. They hold all the cards. They're the gatekeeper.

Are you, are you old enough to remember when Google really took over. Like, yeah, like what a very plain, plain system it was. Like, the idea was Yahoo has got all this junk. There's, there's news and finance reports and weather. You don't want that. You want this clean, sleek system that we have. And, uh, you know, over 25 years, they've, they've kind of become that Yahoo almost.

Yep.

I think I even know there's a specific article. It's like a little thing that I think is quite famous where it says like, oh, let's reward them with a click because it's just like a basic search box.

Yeah.

And that's almost where ChatGPT is now, right? In that I think that's how people say it's very clean and it's like, you just type the thing and you get the thing. Great. But I think they're on course to lose, what is it, around $10 to $15 billion this year, OpenAI?

So yeah, something like that, $125 or $130 between now and '29.

Now that's, that's of course not— that doesn't factor in growth, I'm told.

The revenue that they're generating the whole time.

Yeah, increased revenue, uh, increased cost of, I guess, owning their own power plants and such at some point, which it appears all of the AI companies are going to be doing in the near future.

Yeah, it seems like they have the most distributed, uh, infrastructure so far, and that could make them the next monopoly. So I guess the question is, okay, number one, how do you make Google money? But number two, how do you make OpenAI money then? Because they're like, what's their incentive? Is the incentive of OpenAI and Perplexity different from Google at this point? I mean, obviously they both want to make money, but—

I think it'll come down to how they end up funded. So my understanding is at the moment, even paid users lose OpenAI money for the cost of the training inference. That's true from what I know too. It's not close. It's a lot of money. So my mind would be that they're going to have to sell ads to be able to make that profitable. I just can't see how else they're going to do that. And again, that leads me back to Google in that They have a lot of experience with ad platforms and serving, which I think is a lot harder than people give it credit for. You know, it's been joked over the years about the Google kind of ads platform, but I think it's— and a lot of people think stuff like, you know, their phones are listening to them. And it's actually just a testament to how good targeting is now with a lot of ads.

Yeah.

It's not like your phone is not on all the time, at least for ads, you know, listening to you. But, you know, it's a testament to how good that predictive kind of analytics and all that remarketing and stuff we can do is. And OpenAI again are going to have to buy their way in terms of experts into that. So I don't know what their kind of reward mechanism will be and even if there will be one.

Yeah.

Because I think publishers, particularly in this era, so that is people that earn money through page views basically. Happened, left high and dry. I think, you know, you just had rug pulled, right? What is the benefit to them to producing content? And I think— It's a good question. Yeah, my hope is we'll see maybe a slight reversal of user behavior in terms of well-written, very humanized content will really increase in in demand. It sounds really silly, right? When I do my LinkedIn posts, I, to begin with, was using AI imagery because I was like, this is cool, right? I can just illustrate my point visually really quickly. And after about 3, 4 months, I just became sick of it and everyone's posting similar stuff. So I was like, I'm going to draw stuff in Microsoft Paint, the Windows XP version, Really badly to illustrate my point. And it was wildly popular. People are like, I love this. And it looks like I'm terrible at drawing. And all of them take less than 10 minutes. It's just like a 4-year-old, right? But it was really resonating with people. And I think the underlying point was there's almost, you know, like ad blindness where you just automatically ignore ads. I feel I'm doing that with, if I did realize something is AI content or it's like an AI image, I barely look at it anymore. It's not interesting to me. Whereas if I saw something, even badly, that someone's thought about and drawn, and maybe there's an Easter egg in there, it gets my attention. So I'm hopeful this is where we'll come round to and we'll have pockets of the internet that are very, almost like authenticated or verified, sorry, verified human. You know, so it's like a no bot. I always felt bad, right, in the original Star Wars when Luke walks into the cantina and the, uh, C-3PO and R2, they're like, hey, none of your kind in here. And I was like, oh, that's so harsh. They're lovely robots. I get it now. Like, if I was a bar and ChatGPT was walking in, I'd be like, none of your kind in here. Humans only. So yeah.

Oh, you're talking to the right people. We love Star Wars. Yeah. Well, that begs a question that's kind of out there. You mentioned something about how basically like we think that our tech is listening to us. Pretty soon it will be all the time, right? Like whether you have smart glasses, whether you have, you know, whatever AirPods with cameras or whatever Jony Ive and Sam Altman are cooking up.

Or whatever Meta's announcing right Now, right now.

Yeah. As of for this recording, I feel like then at some point search engine optimization or generative engine optimization is going to be literally listening to people's word of mouth. Is that out there or is that possible?

Well, anything's possible. Um, where I see that going, and there's a fantastic SEO that I'm lucky to be friends with. And they're called Myriam Yessier. They're on LinkedIn as well. And Myriam has been working a lot on multimodal search and looking at how companies can optimize everything from packaging to logos to be surfaced for this whole new wave of visual technology. Because we've got stuff like Google Lens now that I use casually. Lots of people I don't even think know it exists.

It's that annoying thing in the bar that you click every time you're trying to do something else and you're like, no, not that.

But like, I think that's good, Mark.

It's been clutch for me so many times when it's just like, oh, you know, what flower is this? Or where, what, what brand of thing is this? Or what's this weird bug here? You know, is it dangerous or something like And when I haven't been able to describe what I'm seeing, um, and to me, you know, I don't know if you saw the, um, tech test of the guy really creepily doing live facial recognition with people with the Meta glasses. I think it was in the Netherlands.

I wear the Ray-Bans every day. The Meta Ray-Bans. Oh, I know which one you're talking about.

Uh, yeah, you're talking about the one where he basically had like, uh, uh, it could look someone up based on just looking at them, right? Yes. Yeah, exactly.

So like that level is where I think, you know, we'll end up in terms of you can just look at stuff and you'll be able to pull information about it from the web in context. But in terms of optimization, what we do at the end of the day, there is more than ever going to be a layer of whether you want to call it robots, automation, AI between the user and the thing they want. Therefore, there is an algorithm, whether it's a machine-learned algorithm or hand— I don't think we're going to have much hand-coded classic programming in the future, but there will therefore be a way to optimize for it. And my hope actually is the optimization moves away from trying to understand how the algorithm works and more to the— it's rewarding companies that are good or, you know, and by good, I could mean good price or, you know, it fits the person. Because I think what we're essentially going to see could be good for smaller companies, at least for a while. Lots of things, you know, come in, you know, are cyclical in nature. But if you think about, say, take my lame running shoes example, the majority of the search traffic would go to the top 3 search results, which normally nowadays is big companies because Who's got the money to force them there? I think search will become more fragmented as the understanding of the user increases. So you'll get less traffic even if you are those big companies, but it's because they wouldn't have bought from you maybe anyway. And it's becoming easier to connect you to companies that are a bit more niche or closer to you that you didn't. So it's opening up the field of discovery. So my hope is that will have better targeted but more spread out kind of sending off of people, connecting them to the right place sooner, which will be less effort for them. And it's just whether, again, they can work out the reward system to make that happen and not be exploitable. Because don't forget, when Google came out with their PageRank, they confidently, Larry and Sergey on stage, It's unexploitable, this PageRank system. There's no way to spam it. It's literally what they said, you know, and it's been proven wrong many, many times. And the AI tools are in their infancy. Um, you know, I've talked about on our podcast before, and there's so many things that are happening at the moment too. There's like specific Gemini attacks where, uh, you can put prompts in, you can hide prompts in images because the images are downscaled to a specific size.

Mm-hmm.

And then it reconstructs the text very small in the image, which then Gemini will execute. And the example attacker's got Gemini to send calendar information to an email address just by getting Gemini to look at an image. And it's like all these spongy AI surfaces, I think, are going to create a lot of these risks because they're not coded in a way that's very predictable, it's not great for security because you don't know how the system's going to react. And I'm part of the mail group that talks about like the agent-to-agent protocol development. And someone had mentioned about using AI and immediately someone came back and was like, this creates a huge surface for security risks. we don't want to do this in this case. And it's interesting seeing those conversations happen because we always belt forward, right, with technology. And lastly, I'd just say on the LLM agent stuff, it was quite late in the day that Windows started having those user access control popups where if you want to change something on your system that's important, it kind of gave you a popup and we're like, oh, Computer's about to do this. Is that okay? And it's kind of annoying, but it was needed because viruses and Trojans and everything were rampant for a long while in Windows. And I wonder if we're going to have to have something like this within LLMs and within agents, or otherwise they can just be sent off to go do stuff, buy stuff, and you're unaware. They need to be, it's about to do this. Is that okay?

Grit and I have this discussion about true agentic AI all the time. And I'm like, it's not true agentic AI until I'm able to holler at my phone and tell it, hey, order me a pizza. And it knows that that means Corey wants Domino's. He wants a medium hand-tossed sausage or whatever the case is and knows where I live and my account information and can just deal with it simultaneously. I want it able to do that, but I want to be able to not lose sleep over it doing For sure, right?

For sure.

Yeah. Yeah, you almost have to give them like thresholds, like purchase under $30 you could do, but you can only do one per day or something like, you have to get really granular with your control system.

Maybe banks will give us virtual debit cards, you know, that aren't actually tied to your bank account. Yeah, like a ramp system. Ramp cards is a good example. Yeah, that's—

yeah, they're growing fast.

Matter of fact, that might be something I try soon is just, just to see what, what attach it to something that absolutely can't grab money without my permission and, and go from there. Uh, yeah, I was gonna say, you know, before we close here, one last, just one question of what's one piece of AI SEO advice that both made you cringe the most And that you feel like would actually be smart for folks?

So 2 different pieces of advice. Yeah.

Yeah. I guess that's 2 pieces of advice. Question 1.1 and 1.2.

Yeah, I guess I haven't enjoyed seeing people talk about writing using semantic triplets for AI.

I don't even know what that is.

Yeah, it's basically a really posh way to say just write a good sentence. Um, so it's like, you know, if you're saying like, Canva does, you know, SEO for such and such, it's explaining what the thing is, what it does, and who it's, who it's for. It's about just how you deliver the information. But all of these principles just align with the most basic, good best practice copywriting, content writing that's been around for, for so many years.

Yeah.

And there's a lot of that reinventing the wheel that Yeah, it is literally cringy when, you know, it sounds great and then someone asks you to actually explain it and they're kind of like, isn't that just what we're doing?

I think you just invented the toaster.

Yeah. Good advice, I think, would be to always aim for— well, I'm trying to not say aim for the system goal because that would confuse people. I think When, when I approach SEO or AI search optimization, it's always about doing what's best for the company in the most search-friendly way possible. So it's very, very rare nowadays I say we need to do this initiative for SEO. There's always another reason. So we are doing digital PR. If search engines didn't even exist, we would still be doing it because it's building equity. We're getting seen in these places. Um, so my golden rule of SEO for years was always there's nothing you should do for SEO that has a negative impact on the user at all. Absolutely nothing. If you, if it does, then you're doing it wrong. So if you're changing your writing for SEO and it's making it worse for the reader, wrong.

Yeah.

It's not the thing that's going to be rewarded long-term. And I think just those 2, it's kind of 2 things like can steer you away from so many suggestions, which are ultimately harmful because there's stuff People telling people to do, um, you know, the geo advice I've seen, when you get down to it and say, okay, fundamentally, what do you do for geo that you do not do for SEO? All of the things I've seen listed are pure manipulation, like trying to trick the, the, the AI search platform. And it's like, you, you will get punished for this eventually. You know, like you said, when, when Google started, it was like people went, oh, I can put white text on a white background. You know, and it's like, yeah, you can, and then they'll work it out, and then you'll be in a hole you have to dig yourself out of. There's a great saying, uh, I always see, uh, one of my colleagues, Lily Rae, saying it, which is that it works until it doesn't, which is a great way to describe a lot of these tactics.

Absolutely.

Yeah.

Well, Mark, thanks so much for joining us today. This has been fantastic. If, if you own businesses, if you own websites, Uh, just a lot of great information today that I hope you've been able to, to follow and keep up with. Mark, where can people go to best keep up with what you're doing with Candor and everything else?

Sure.

So I'm, I'm pretty active on LinkedIn. Um, and if you wanted more of a view into my random thoughts, what I'm doing day to day, I'm also on Bluesky. So B-Sky. Um, both of them you can find me if you search for Mark Williams Cook. I think I'm actually the only Mark Williams Cook on the internet. Oh, so it's really, it's a blessing and a curse. Yeah, when I got married, I hyphenated the name and it's kind of blown my cover now. So if you just Google my name, you can see pretty much everything I'm involved in website-wise.

Come on, you're an SEO. This was on purpose, wasn't it? Yeah, Mark Williams Cook.

I had to, I did, I chose my spouse based on the search term volume. Oh my gosh, that's amazing.

Oh man. Well, thanks to everyone who watched today. If you don't mind, please take a minute to, to like, subscribe, leave a comment. We really appreciate the discussion we have around these and make sure you take a minute to pop over to theneuron.ai and sign up for our newsletter. Join, I think it's 580,000 this week now that we're at who read it every morning. So. Please stop in, check it out. And that's it for this time. Farewell for now, Gibbons. Bye.