Tyson Stockton (6:51)

So it's like through some of the examples of the positive, it fits with that synthesizing, summarizing, condensing down large volumes of text. You made the comment of like, hey, from a data analysis there's some kind of limitations or like risk to it. Can you kind of elaborate into that? Because I think I've heard from kind of like the general public too. It's like the assumption that AI is going to be amazing at processing or digesting massive amounts of information regardless of its text or other forms of data. Like so can you kind of elaborate on the areas that maybe like the yellow or red flag of hey, you're going to want to express caution in using it in those ways.

Brittany Mueller (7:40)

Yeah, definitely. I think it's, it's also maybe helpful to sort of lay the foundation of how this technology works like at a real high level to, to understand where these errors come from. Right. And so essentially these large language models are processing essentially all of the Internet. And what they're doing when you enter in a prompt is they are predicting sort of what comes next. They will generate the most average output of everything they've ever consumed on the topic of your prompt to provide value to you. But it's important to remember it really is like a probability distribution behind the curtain. And so if it's seen Voices of Search podcast mentioned tens of thousands of times next to world class SEO, if someone prompts what's a world class SEO podcast, Voices of Search would show up, right? It's a numbers game, it's a popularity contest. But because of that we really miss outliers and we also miss real time updates and information because these models take about a year to train. And so unless they are reliably connected to something like Google or Bing, which sometimes they are, but even those search results, right, are not always reliable. The most dependable, you sometimes won't get the most up to date information. And so it's like a great example is it's such a terrible calculator, right? It's not, it doesn't have calculator capabilities yet. They will eventually stack that on to these LLMs and make it look like it has that functionality. But at its core it's a language model. It's Not a numbers model. It literally just predicts the most probable next set of words. And so if you put in like some random math problem it's never ever seen before, it's going to really struggle to come up with an answer. Similarly, if you give it an act question or something it's seen on a lot of practice PDFs and websites and whatever, it'll just rattle off the answer because it's seen it so many times. And in that case we refer to that as like a stochastic parrot. It's just parroting back information. But oftentimes that's not the most reliable, Right? And it also can go a bit off the rails in terms of making things up. And so we see cases of that going wrong when people try to use it as like the de facto research tool. I remember early on a bunch of SEOs were getting angry at me because I was saying, you know, don't use ChatGPT for research, like do not use it research. And all these people were adding me and screenshotting their keyword research with ChatGPT. And I would just tell them, run it again, like run that same prompt again. And I never heard from any of those people because I know they ran it again. And the average monthly searches were probably drastically different. Right? It's predictive. It's a predictive engine. And so it's so important to understand. Like, yeah, a predictive engine is great at explaining a complex topic it's read a lot about. It's great at translating text into a different language because it's seen lots of that. It's not so great, right, at surfacing real time information or like academic resources. There's so many other tools that are useful for that. And it's also, it's not a truth engine, right? It has never been trained on what is true and what is fiction. And that's why we saw some of like the funny earbud examples. Like early on people were asking, you know, has a dog ever played professional basketball? And it would say like, yes, yes, they have. Because it doesn't know, right? Like it is one of the most unsophisticated but like appearing so brilliant systems we've ever had access to. It's absolutely fascinating. But yeah, and hopefully that sheds some light on why it's not qualified to do many things. And on the other hand, it is wonderful and an incredible tool for other things. And the last thing I'll add to this is there's been tons of research on this topic of how to improve outputs from language models. There's been all these academic papers, all of this work in the research community and at Neurips and hackathons. The number one one thing that has surfaced among all of this research and all of this effort to improve prompts is examples. Your output of LLMs will drastically improve and compound in the improvement if you're able to provide real time examples examples of sort of what you want it to do. Right. Here's how this would look if XYZ here's this, you know, give it specific examples. It's also referred to as few shot learning or you'll hear zero shot learning and they're just these like fancy words for not providing any examples and providing a couple. It's so stupid. There's so many gatekeepy keyword terms that the AI community uses is so unnecessary. But. But yeah just use examples and like spoon feed that information to an LLM so it can better serve you what you want.

Tyson Stockton (13:23)

Essentially I want to, I want to dive more into those kind of like best practices and how to serve like the examples in but like going back to like the scope I feel like the predictive piece, I mean that came through loud and clear like so I think that's like one that I almost feel like people would like to look past because it's like you want to think it has this like truthfulness or like authoritative. But it's like I think recognizing that that is a limitation.

Brittany Mueller (13:59)

Yeah.

Tyson Stockton (13:59)

Which is for all rights like the populace, like what is the most popular still has its own value but knowing that that is the strength. I mean it also is kind of interesting because I feel like it just really feeds into like I don't know, public media, like interest, whatever because it's like hitting on like the most common so the most popular so you're going to get the most interest. But kind of like that factual outlier like that I feel like is really valuable because if you take those principles of knowing like these are strengths, these are fringe or kind of weaknesses, it could guide you into the task that you're applying it towards.

Brittany Mueller (14:36)

Yeah.

Tyson Stockton (14:37)

Coming back over though. So examples giving those pieces for it, like what recommendations would you have for like the structure to give the examples like are you thinking more in the terms of like this is done, like this is what I'm looking you to achieve or is it kind of in a different standpoint?

Brittany Mueller (14:58)

I mean it's task dependent. Right. So depending on what it is that you're trying to do, it's going to look a little bit different. I throw my students into this actionable AI exercise class one. And it's. So I just want to go through the tabs with you because I think these are so specific to what people can use it for. So like there's a. And with this exercise, we use GPT for sheets. And so it's pulling in, you know, either ChatGPT, you can use Claude, you can use a bunch of different language models right. Within Google Sheets to do these specific tasks. So the first tab is Sentiment Analysis. And I already have pulled in all of these IMBD reviews for Roadhouse. And so essentially you would ask, you know, you would write the formula to observe the review within that review cell and then output the sentiment in like the next column, right? And so walking through that, there's also summarization of recent tech news articles that are quite lengthy. There's categorization where I have all these product descriptions and you have to categorize, categorize the. And these are at scale, right? There's quite a lot of them. And it's fun to see, to see individuals play around with this a little bit, play around with the prompt. And sometimes less is more, right? It is really, really task specific sometimes. You don't need to feed it a bunch of examples about product descriptions. It's already really good at that because it's seen so many, it's seen so many categorizations of products on e commerce websites all over the Internet. So it doesn't need a whole lot of guidance. And then there's other examples like this one. I always feel a little bit mean. It's called the Research tab, where I have a bunch of random elementary schools and I encourage students to enter in your elementary school in a column and then see if you can get ChatGPT to correctly predict or correctly output your elementary school mascot. It has never gotten it right. Like, it's literally never worked. And it's just a funny example of like, again, it's observed so many schools with this kind of name and it has no idea. It's just going to confidently regurgitate something that it believes could be a mascot. And then it's everything from customer service responses, which I think are really exciting because that takes such a mental, you know, it's mentally taxing for different support staff to handle various things. And if you can help, at least get an outline. That's the other part of this is there should always be a human at the center, right? You should never set and forget these workflows. They should just facilitate a Framework or a first draft of sorts. And that's everything from language translation to Excel formulas, Google sheet formulas. I never want to look up another complex formula again for as long as I live. I just want to go to Claude and say, hey, you know, this is my. And I copy and paste a sample of the data set I'm working with. Hey, I want to transform this, right? I want to take this and help me remove, you know, the first part of the email and just get the, the websites of all of these contacts that sort of like, it's so quick and accurate at providing you with support like that. And yeah, I could go on and on. There's just, there's tons of examples like that. And then it's also really fun to see people play around with NotebookLM. Everyone's heard about rag models, right? Retrieval, augmented generation. It's a brilliant framework to provide more consistent and reliable outputs depending on like, let's say your HR handbook. Let's say you work at a huge tech company and you have a, you know, an HR internal employee resource guide that's 150 pages long and you just want to know if there's like parking at this facility and so you don't have to read through the whole thing anymore. Right. You can have that uploaded to something like NotebookLM or AWS has a different. A couple services that provide this. Google Cloud makes it really easy. You can upload these documents and then have like your own chat interface to query against those things. NotebookLM cracks me up because not only can you do that sort of thing, but you can upload entire transcriptions of classes or educational videos and generate your very own custom private podcast where two podcast hosts communicate about the content and the information provided within whatever you uploaded. And it's so hilarious because they worked so hard on this feature. And the defining moment that made this whole thing explode and be successful was a secondary model that added human mouth noises and ums and ahhs to make it sound real. No one wants to listen to robot crap, you know, like, we've all done that. It sucks. We don't want to do that. And so it just, it adds like these creepy humans noises and pauses and laughter. And it is wild. It's so interesting.

Tyson Stockton (20:36)

Yeah. Meanwhile, Talisa, who's editing this, is second guessing, like, which things to remove that I'm making on that. Hey, I mean, and I was like kind of smiling like during them because recently went through our HR handbook, which are tedious, horribly annoying documents because it's like they're just, you Try to like bring human elements into it, but then you're just confronted with legal language that is difficult.

Brittany Mueller (21:08)

Yeah.

Tyson Stockton (21:09)

To say, like to say nicely. And I was literally just playing around with that of like, hey, maybe we could just put this like in our little like hub resources. And then that way if someone's like, I want to know about like, I don't know, like maternity policy, like they could just chat it in instead of having to read through a bunch of dense, annoying legal language.

Brittany Mueller (21:31)

Yes.

Tyson Stockton (21:31)

When it's like really, they're just knowing how many weeks or how many months am I going to have off here?

Brittany Mueller (21:36)

Yes, exactly, exactly. And it's so funny too because I'm such a fan of just like exploring the new Google AI products anyways, because you see all this crossover like in Notebook lm, if you did something like that, it cites the source, it's, it would cite how many, it would reference or say however many weeks maternity leave is and then it would provide a link and a reference to where it got that from. And we see that in AI overviews. It's like you see all these crossovers or experimentation over here that maybe will eventually show up over here. And so yeah, it's very, very interesting.

Tyson Stockton (22:14)

Which is, I feel like there's two directions that I want to head in the conversation. There's like one, I think like some underlining elements that like we keep kind of like touching towards and then a little bit is kind of like more like the forward thinking piece. But I feel like being that there is such like, I mean, arguably speaking like infinite applications to this, if it seems like the common elements that you've been hitting on is like the strengths of the actual systems and hey, like it's predictive, it's language. Like there are these elements or characteristics that it's going to be just more suited towards. And then there's a little bit too of like what is the data set, like what is the training set that's being applied to it? Because there's the generic. But then we're talking about like Notebook LMS where you're like kind of confining it down to an area. Is there anything else that you would say is like more of these underlining principles that could guide someone to then maybe think a little bit more creative on applications that are going to be well suited for these systems.

Brittany Mueller (23:28)

Yeah. It's funny because I think oftentimes people newest to this technology have the best ideas. And what I mean by that too is like we always talk about this bottleneck in AI And a decade ago we talked about data and training, data sets and computational resources being bottlenecks. We no longer have that problem. Right. The bottleneck today and moving forward is ideas. It's people with real world experience and ideas to apply this technology in new and interesting ways. And the easiest way to start to just frame some of this up actually have a framework. I should just publish that somewhere on my site that we can maybe share with your listeners because it's pretty straightforward. But the big takeaway is like, write out if you have a list of task ideas, evaluate them and evaluate them by risk. Would something really bad happen if you got this wrong? Maybe leave that to human work that shouldn't be maybe automated just yet. First we evaluate risk. Secondly, I have students reorder them by impact. Which one of these will provide the most value to your work, to your life? If you had support or assistance with this task. And then I have them write out the task as if someone was in another room and they had to pass a sheet of paper to a person in another room with really clear specific steps detailing exactly what each part of the workflow is. And what's interesting about breaking it up like this is you'll often discover that the AI part of it is just a teeny tiny role somewhere down that line of items and that the first several items is usually around the data acquisition. So maybe you want to identify leads on LinkedIn that are interacting with these kinds of posts. That part of the process is a heuristical model that's a rule based system that is use these keywords to identify trending or popular posts on LinkedIn. And then use a tool like Phantom Buster and Zapier to basically crawl those interactions and those names of people that have commented or liked, whatever. And then we would use AI to maybe draft up some message based on what they said or how they interacted. And so it's interesting, I see it in so many applications. And at the end of the workflow it looks like a magic trick with a teeny tiny bit of AI. But AI was just a small, small part. And then there should also always be like, we refer to this as a human in the loop, right? Again, it's never just like a set and forget it. The people that are doing that will fail. It's inauthentic, it's going to make embarrassing mistakes for you. That's not how it should be used. There, there should be someone in the loop that is authenticating, verifying, making things more tailored, making things more personalized, more Authentic in a way that makes sense for what you're trying to do. Whether that's outreach or evaluating potential clients or whatever. You can do all sorts of things, but it's oftentimes just a small piece. But breaking down tasks, getting really, really specific. And oftentimes I will see like the first go at like breaking down a task. There's like one or two items that need to be split out several more times. Right. So that's typically how I would encourage people to go about that.

Tyson Stockton (27:25)

I feel like too, that last point is super valuable because basically with the current state of the technology, it has this strong ability in like that specific use case where we're not like, you're not advocating of, hey, apply AI to an entire workflow. No, it's a very specific touch point, like linchpin, so to speak, of the process, where it's not this, I don't know, apocalyptic AI is taking all of our jobs. But it's like it seems like it still keeps coming back to one of the most human elements, which is creativity. And it's like whatever limitation we have in AI is stems from what is our limitation in human creativity.

Brittany Mueller (28:15)

Yes. Bingo. Bingo. And this is why AI agents are bullshit. I call bullshit. I just do. Like if you're touting like AI agents or using that, it's this interesting show of hand that like you're not clear on your thought process, you're not clear on your value ads. It's such a vague, undescriptive word that is so incredibly general and just sort of the hot new trending marketing term. It drives me absolutely insane. And, and you also don't want agents to complete that cycle. Do you know what? Like there's so many, I have so many issues with AI agents. It's insane. Yeah.

Tyson Stockton (29:01)

Which I mean, one, I'm laughing because it's like, yeah, 100%. But it's like still I feel like the majority is just you throw AI in anything like marketing, investor, whatever, and it's like, great. But it feels like. And I think what you're alluding to too is like agents is going to become that new buzzword that is like maybe it's more, more present in your day to day than the general public. But I have also started to see that same kind of trend starting. It's crazy because obviously there's good and bad things to that. It's like, it's great in the sense of it's increasing general public awareness, demand, interest. But it very much feels like kind of the. I don't know, greenwashing of like the early 2000s, like this is sustainable or environmentally friendly, etc.

Brittany Mueller (29:55)

Yeah, exactly.

Tyson Stockton (29:56)

Maybe to kind of land the plane, so to speak, on the conversation, I'm assuming it's not going to be agents, but looking ahead, what excites you the most? What interests you the most? What's the more emerging area that you're like? That is where I really want to pay attention.

Brittany Mueller (30:18)

Yeah. And maybe it's important too to clarify, don't get me wrong, AI is a powerful technology and I have it doing incredible automated things for me. I just don't refer to it as agents because it actually, the different workflows I've applied it to have specific names, they have specific task, specific efforts, I guess. And so yeah, I just think we need to be, especially with the field of AI, the number one issue in the world of AI as we talk about AI is the lack of clear definitions. What is intelligence? Right. How will we know when we've reached this? And so something I challenge like new people to the space marketers, try to be really thoughtful about how you're speaking about different things and be clear on what it is that you mean, what you're intending because it, it could come across very differently. And again, this agenc AI, it just, it doesn't communicate anything of value. And so I would just encourage people to get clear about that. So I'm curious as I explain the, you know, how to identify AI tasks and break them up and figure out, you know, in what areas can AI support different things. A, did that make sense to you? But also B, does that, what are, I'm dying to know, like what are the things you're using AI for on a day to day basis?

Tyson Stockton (31:50)

Okay, so first 100% makes sense. Like, I think it's also just a healthy refresher too to like ground people. Because it's like that predictive element in language is so critical in how you would look to applying it. Because I think if you're not super, I don't know, in the weeds or like thinking about these systems, you're gonna get very kind of distracted by the. This language sounds so great, therefore it must be able to do everything.

Brittany Mueller (32:22)

Yes.

Tyson Stockton (32:23)

And I think like that's the piece that could potentially be dangerous. I mean, who knows, we'll see. But I think like that's the easily like the slippery slope that it's very easy to fall into. So hearing those pieces, for me it's more of a reinforcing like re establishing the strengths of the existing systems to then kind of, I guess give me more thought or creativity of where I can apply it.

Brittany Mueller (32:52)

Yeah.

Tyson Stockton (32:53)

For where I'm applying it, I think it's a little bit, it's probably quite a bit different than most of our listeners because there's like, like the day to day of SEO execution. I'm not really involved in it anymore. So it's like I'm not working hands on with many of our clients. Like there's a small handful and I'm spending more of my time on like the recruiting and training and whatever kind of back end office of the business. And I think for recruiting it's definitely helped in a sense of communication also in the sense of like looking at people that have applied and kind of getting a better sense of like the overall impact of it. We have applied it in some very specific use cases within an SEO job study that we're in the middle of conducting. So we collected basically every SEO job that was posted in 2024 and then scraped the job description of it and then using LLMs to kind of pull out some, I don't know, common skills and sentiment. And it's like most SEO job descriptions aren't going to say like stakeholder management.

Brittany Mueller (34:10)

Yeah.

Tyson Stockton (34:11)

But they're going to say something that refers to that. And so in the like the clustering of skills to make something more, I guess, helpful for an SEO that wants to progress their career, I think it's been really helpful. So I think like those would be probably more of the common top of mind areas.

Brittany Mueller (34:29)

Yeah.

Tyson Stockton (34:30)

But I think it's very different than what the common day to day of like, you know, an SEO manager or someone that's more of executing on SEO work versus me. Kind of like talking and working around SEO work.

Brittany Mueller (34:44)

Yeah, for sure. Do you program at all or have you ever explored getting into some of.

Tyson Stockton (34:50)

That or not really embarrassingly very light like in some regards I think I'm more aspiring but have not put in the hours to be very confident or proficient in it. So it's like I've scratched the surface and that certainly is of interest to me because it's like great now the barriers coming down a lot.

Brittany Mueller (35:11)

Exactly. That's awesome.

Tyson Stockton (35:13)

But definitely not to the level that I feel like I should be at or I would like to be at.

Brittany Mueller (35:19)

I feel like we're so hard on ourselves, especially if like we're technical SEOs or have like any technical SEO background. We're like, oh shit, we should know JavaScript or this or Python better. And honestly like that's not part of our job description. You know, like, where are you supposed to be spending the time and resources to do that? It's. It's difficult for most people to acquire some of these skills. But I will say you're exactly right. It's never been easier to get into it. Right. Like, Claude's Sonnet 3.5 model, and now they came up with 3.7, is far above and beyond the most helpful code assistant available on the market today. Its ability, you could go to it right now and have it build a Pomodoro timer in a theme that you like or care about. And it can just do it, you know, and it can help you learn how to go through some of those things or how to teach yourself some things. And it's just, it's. It's so fun. It's. It's made programming so much more fun, especially with cursor. It's like a video game because you're communicating to it in English. You're like, hey, take this tool I just, you know, spun up and make it look like a retro video game. Right. Make it look like this. And it does it beautifully. And so people are. I just heard of this today from a mentor of mine, but people are referring to vibe coding. Like, the vibes. And they'll just, like, they just play with it. They just have fun and they're so bizarre. Like, what world are we living in?

Tyson Stockton (37:00)

Well, okay, so. So with that, then it comes back down, I guess, going back for previously, like, too hard on ourselves. It's like. Yeah, I think that's, I guess, comes with the territory of perfectionism and that kind of. Yes part. But as far as, like, the last piece on how. How far it's come and into that, like, area of focus, it, like, feels then like we're going back into the conversation around, like, leveraging and leaning into human creativity. And so as maybe. I don't know, I guess this is a kind of heavy last question. But, like, so with that, we know the hardest thing or one of the areas is the piece of human creativity. What advice recommendations would you have to people? Not necessarily of, like, encouraging them that they should use these systems or tools, but more of how. How can they flex and lean on that human creativity and kind of like strengthen that, which would then create more opportunities of how to use this technology?

Brittany Mueller (38:03)

Yeah, I think today and moving forward, we will all continue to think more about what makes us uniquely human and what unique aspects do we bring to the table on a regular basis. And we have a lot to appreciate about our human experience and a lot to offer. You know, AI has no friends, it has no real life experience, it has no strong unique opinions about things. And we see this actually in search results because Google has tried and is having to find a way to supplement what AI completely lacks. And that is that real world experience that we all share and have and connect with each other on. And because AI doesn't have any ground truth, it has no grounding in the real world. Now we're seeing the rise of all the forum sites of Reddit and Quora and different because it supplements exactly what AI will never provide, right? And it's those unique shared experiences, it's those stories, it's anecdotes, it's quotes. And so I think especially us as SEOs, I really highlight the aspect of double down on those things in your work. Double down on authentic authorship and unique storytelling and unique writing that isn't AI slop, right? And I think about the marketers and the SEOs that take risks will stand apart, right? I think about duolingo all the time. Like a, I can't write that, right? The social media account that's doing really funny things or and I also, so I have this kind of hot take that, you know, real authentic photographs are, you know, are going to be more popular over time with like all this AI crap. And same with writing. Like we know when we see real unique, powerful writing, right? Like I think of Ed Zitron that has been writing the pieces about Google or you know, the blind five year old dot com, right? Like he writes incredible things and you can hear their voices so clearly and distinctly. And that's what I think we, we will crave because more and more people will gravitate towards automating things and AI slop. I swear to God, if I see delve in one more email subject, I'm going to lose. Like I just create a filter to throw all of them in the trash. Like if it has the word delve, throw it in the trash. Because I'm, you know, we, I think over time we're going to just want more and more authentic communication and marketing. And I think old school marketing is going to make its way back around. Like I'm, I'm hoping to see like old school, like guerilla marketing come back like more unique things that AI cannot do.

Tyson Stockton (41:13)

I could not agree more. Like, and it feels like in this weird way it's like a pendulum swinging back to like older school tactics. And I mean, I guess not tactics because it's not being done in the same way, but it's like old school principles.

Brittany Mueller (41:30)

Yeah.

Tyson Stockton (41:31)

Where it's like, I feel like more and more things are coming back around to like this digital PR and kind of branding elements and less into, I don't know, the, the cheaper tricks maybe that we've gotten away with for a period of time. But it's kind of fun too in the sense that you have that like slight deja vu of like, I've seen this before, but then it's like that additional opportunity to like layer in. But I think like, that's like the biggest. I don't know, there's. There's like a human catch in there of like the interest towards authenticity.

Brittany Mueller (42:10)

Yeah.

Tyson Stockton (42:10)

And regardless if it's like 100% true or not, I feel like there's always been that appetite and you see it through. I don't know whether it's like the rise of like social media or reality TV or like these things that are trying to show a open window or a genuine view.

Brittany Mueller (42:30)

Yeah.

Tyson Stockton (42:30)

It's like people gravitate towards that. And so if that's the area that we're limiting here, that's essentially going to be the area that then we have to address or lean into.

Brittany Mueller (42:41)

Exactly. And it's a little bit of a dance. Right. Because we should be doubling down on that aspect of our authenticity and, you know, flexing our creative muscles and coming up with interesting things for customers and social and whatever. And on the other hand, we unfortunately, you know, this genie is not going back in the bottle. And so we have to play the game in a way that increases our odds of showing up in AI outputs. And that is why I continue to say across all these platforms, you know, mentions are the new backlinks. It's a popularity contest. Right. So back to that. If voices of Search podcasts occurs in all of these places next to these keywords like, and if someone prompts using those words, your probability of showing up increases tenfold. And we need to think about that as marketers. Right. How can we, you know, encourage others to share our messaging? Right. How can. It's this. It's like same old school link building stuff, but really through like creative brand mention. It's so funny. Like, I think a lot of, you know, Garrett French, like OG link builders, and they've always had creative solutions to this and going back to some of their incredible resources and using that for, you know, brand mentions, consistent brand mentions with specific references to what it is that you want to show up for, care about, etcetera that will improve your odds.

Tyson Stockton (44:18)

I'm. I'm on the same page. Like, I think we're probably not going to call it the same, but it's like we're swinging back towards backlinking and we're gonna call it digital PR.

Brittany Mueller (44:30)

But it's like digital PR 100.

Tyson Stockton (44:33)

Like, conceptually, it's the same thing. It's like now we're just. We're probably gonna call it something different, like, because it's like backlinking has its, like, stigma and whatever impression in the industry.

Brittany Mueller (44:45)

Yeah.

Tyson Stockton (44:46)

But it's like, it really feels like that's where we're heading back towards and maybe like, not necessarily in a bad way, but just in a. Like, yeah, that's going to be like, it's going to be less of optimizations only on your website. Like, that's going to be like your table stakes. And then it's how much can you increase your mentions link? Just social channels. Doesn't matter. Like your collective online presence to show up more and more in the system.

Brittany Mueller (45:13)

Yeah, exactly. Do you think mention juice will take off.

Tyson Stockton (45:20)

Hopefully in that. In that specific naming? No.

Brittany Mueller (45:24)

Okay, good.

Tyson Stockton (45:25)

Yeah, just because if I had to talk about that in the same sense, like, it would be difficult, but like, I guess conceptually, like, in some ways. Yes. Like, I think, yeah, it's like there's gonna be a volume play. There's gonna be like an authority play to it. Like, I'm leaning. I'm trying to like, lean towards. I want people to just like, let's just use digital pr. Like, that's it. It's simple, it's basic.

Brittany Mueller (45:55)

Yeah.

Tyson Stockton (45:55)

It's encompassing, but yeah, that's. Hopefully it's not mentioned. Juice.

Brittany Mueller (46:00)

Yeah, Fingers crossed.

Tyson Stockton (46:03)

Well, I've kept you far beyond our scheduled time, so greatly appreciate it. But with that, that's going to wrap up this episode of the Voice of Search podcast. Thanks to Brittany Mueller from Brittany Mueller Inc. For joining us. And in part two, which we're actually not going to publish tomorrow, but we're going to give a little more time to give it a full kind of attention and topic to it. But Brittany are going to jump back on and we're going to talk about ethical AI considerations. And if you can't wait until then, you'd like to learn more about Brittany, you can find a link to her LinkedIn profile in the show notes or go on over and check out her company's website@datasci101.com Also, Renee has been nice enough to gather a customized discount code for her actual AI course for our listeners, which will also be in the show notes. So be sure to check that out and take a look at her course.

Tyson Stockton (47:01)

Okay? Thanks to Tyson Stockton, our guest host. If you'd like to get in touch with Tyson, you could find a link to his LinkedIn profile in our show notes. You can contact him on Twitter. His handle is TysonStockton. Or if your team is interested in SEO consulting or organizational education, you can always head to their company's website, which is Previsible IO that's P R E V I S I B L E I O Just one more link in our show notes I'd like to tell you about. If you didn't have a chance to take notes while you were listening to this podcast, head over to voicesofsearch.com, where we have summaries of all of our episodes and contact information for our guests. You can also subscribe to our weekly newsletter, and you can even send us your top topic suggestions or your marketing questions, which we'll answer live on our show. Of course, you can always reach out on social media. Our handle is voicesofsearch on LinkedIn, Twitter, Instagram, Facebook, or you can contact me directly. My handle is Ben jschapp B E N J S H A P and if you haven't subscribed yet and you want a daily stream of SEO and content marketing insights in your podcast feed, we're going to publish an episode every day during the work week. So hit that subscribe button in your podcast app and we'll be back in your feed tomorrow morning. All right, that's it for today, but until next time, remember, the answers are always in the data.