AI You Can Trust, Audit and Keep with Russell Moore, Co-Founder & CEO of Amotivv | Episode 494

Welcome to the Leaders in Payments Podcast, where we talk to sea level leaders from across the payments landscape. We'll be discussing the products and services that impact the payment space today, as well as trends and predictions for the future of payments. We will also hear stories from our guests about their journeys to the topic. Hello, everyone, and welcome to the Leaders in Payments Podcast.

I'm your host, Greg Myers, and today's special guest is Russell Moore, co-founder and CEO of Emotive. So, Russell, thank you so much for being here and welcome to the show. Thank you, Greg. It's an absolute pleasure being here.

So before we dive into talking more about the company, can you give us a quick snapshot of your personal background, maybe where you grew up, where you call home today, a few things like that? Oh, yeah, absolutely. I'm in Auburn, Alabama, in a little community right outside called Beauregard on my farm. I like to say that I'm 15 minutes from my farm gate to my tailgate, which is actually true.

It takes me longer to hook my camper up than it does to actually get there. Yeah, I've been living here with my wife of 25 years pretty much our whole lives, it feels like at this time. I've got two awesome, amazing daughters. My oldest is 18.

She's she's also at Auburn, and my wife is also a graduate of Auburn, as am I, so War Eagle to everybody. And then my youngest daughter is 11. Love doing jujitsu and mixed martial arts and hanging out on the farm and doing tractor things like bush hogging. Nice.

Well, War Eagle, as you know, I'm Auburn grad as well. So we got a lot of Auburn in the room today. All right. Well, can you walk us through your professional journey a little bit and then how and why you started Amotive?

Yeah, absolutely. So started off in my early career in the late 90s. That's actually where I met my founder, the co-founder of Amotive, Jason Smith. We had our first big boy jobs together.

We were network engineers and network architects later on at an ISP called Knowledge in good old West Point, Georgia. So there's been technology in the South for a long time. People just don't realize it. Worked my way through that.

Later on, really closer to 2010, decided to kind of move away from the networking engineering aspect and get into more of the innovation side. And I found a little tiny company in Columbus, Georgia called Teesus. And the reason I picked TSU, people say, You're doing innovation at TSUS, what are you talking about? But if you really look at the payments world, it touches every aspect of technology that I like.

So even back then it was AI. You know, later on it was crypto, which I actually learned about crypto in 2010. You know, go figure, OG crypto guy here. But kind of work way through the payment space, both the merchant acquiring and the issuing side.

I started off on the issuing side, of course, real heavily at Teesus, and then the merger with global payments really pushed me into the merchant acquiring side. Uh, did a really wonderful stint, probably one of the funnest times of my career, had one of the best bosses ever for Cap One TSIS. So was director of innovation for those guys, did some really amazing patents. That's where I actually met the third party of the emotive crew, Don Riddick.

We did lots of patents together. He was one of the top lawyers at Thesis. And when I started doing patents, he's like, Yeah, come here and let me show you how to do this stuff. And then later on, of course, we really got heavy into crypto and the metaverse and all those buzzwords before generative AI took off.

And then back in June of 23, this was really global payments had kind of taken over all the brands. I became head of AI for global, uh, was head of the Center of Excellence for AI for them and did that for the next couple of years where we launched some massive programs. The first big one was doing it for 6,000 developers with paired programming. And when we started rolling that stuff out, I mean, I love global payments.

I still talk for them and about them quite a bit, but they're not an AI company. And a lot of the issues that that I was seeing was these were things that an AI company needed to go solve. And that's where emotive came about and the kind of the concepts of doing that. So you had these issues with compliance and and memory and all the things that you'll hear me talk about from an emotive perspective.

You know, that's where it became very obvious that if you don't solve for these things, you will not get the return on investment. There will be no ROI. The business cases will fail. And really 2024, 25, we saw we saw that.

We saw where everybody was doing POCs and you know they work great. And then all of a sudden they pushed them into production and they just didn't work. And compliance was coming back saying, hey, we've got to audit this stuff, just like we've always audited stuff. You couldn't really do it.

So that's that's how a motive got its start. June 2025, I officially left Global Payments, had you know some great parties and some some tears because it's been my family for 15 years and you know, still love those guys, like I said, and then you know, jumped over into a motive. And like what I like to say is when I was when I was at Global, I did lots and lots of meetings. And you know, it's a large company, so I was doing lots and lots of business.

But now I'm doing lots and lots of AI and I'm super happy. Is it fun? And is it a startup? And are there the startup woes?

Of course. But man, is this fun? And I'm doing it with, you know, people I not only, they're geniuses, Greg. They're so much smarter than me.

Thank goodness. Never do I want to be the smartest person in the room. They are definitely the smartest people in the room. So yeah, it's great.

That's kind of how we got here. All right. Well, that's great. So tell us exactly what a motive does.

So there are three layers to a motive. First, and where it all started was memory. So we want AI that can actually remember it actually remembers your business, you across every session that you own and you control. That is so important that you as an individual and you as a company own and control the memory of all the things that you're doing with AI.

You want a workspace, which is the second layer. It's one governed place to use any AI model with all of the right of controls that you need. And then we have our whole platform, which is our product, and it's called Atexa A-T-T-E-X-A, which does come from attestation. And all that is, is it's the verification layer, which is the heart of all of this stuff.

You've got to be able to trust and verify all of AI that you're using, Greg. It is so important. Yes, we are selling this as a product, but it is, if you do not have this in any environment, much less the regulated environments that I'm talking about, payments, healthcare, all of the things, government. If you're accepting a payment and you're using AI, you're gonna have to be able to trust and verify.

And then you have to produce tamper-proof, like all these things are gonna be big words, but they have to be tamper-proof, independently verifiable, which is another huge thing that we're gonna see coming out that you know you can't, it will be regulated into existence, and it already is, and we can talk about some of those regulations. There they exist today, that your tech vendor, just like today, where you can't verify your own things in audit, you have to have someone else verify those and products.

This will be no different. But you have to have independently verifiable for certificates of what your AI did, who did it with AI, what policies allowed it, who approved it. And yeah, that is going to be imperative. So you're you're seeing, and that's what a motive does.

So it's it's memory with a workspace, and then it's verifiable proof with math. Like, so people are like, well, how do you verify all this thing with math, with cryptography? Like it's it exists today. We've been doing it for years and years and years with regulated data.

We just need platforms that are specific to AI. That's the only and generative AI, or now agentic AI, which I don't know, Greg, if you've seen this, but at the end of 2025, everything I spoke about was AI and uh generative AI. So come speak about, and now in 26, everything I speak about is agentic AI. I don't know why that happened.

And if you would like my definition of agentic AI, it's AI that has memory and it has it has the ability to use tools on its own, right? You don't ask it to email, it emails on its own, but it has the tool sets. And really the driver of all that was when MCP, which is one of the the major protocols, which is that's happened, you know, all through 25 and 26, all these protocols that allow us to do things. Everyone who uses it kind of has the the same map to be able to do it.

So if you build it to this spec, this protocol, you can use these tools. So yeah, that's what a motive does, and uh we're super proud of it, at least I am. We got all kinds of, I said earlier, Don Riddick, he's an extremely good lawyer. He's our chief strategy officer, but he's also we've got lots of patents, so lots of patent pending stuff going on, so we're super proud of that.

Yeah, so we're like most AI companies that are true AI native, we're spending about two. Well, I know exactly how much we spend because I pay close attention to it, but we're doing 2.2 billion tokens a week now. And that went from last July, so almost a year ago, we hit our first month spending a billion tokens.

And now we're up to 2.2 a week. And we can talk about how much that actually costs because it's going to be a thing and it sounds like a lot, and it kind of is, but in the scheme of things of how you build stuff, we're in a place, Greg, that we have never been before, ever in the you know, the history of humankind. This is the kind of the cusp of it.

We're just scratching the surface. You know, of course, and you know, the audience will know. I'm very optimistic about this. And the outcomes in all of my scenarios just about are are utopian as opposed to the the although I can go Black Mirror very quick, and I get I get blamed for doing that.

And I don't really know why. I'm just you've got to put rules and guardrails around this stuff. So talk about, so I I think for the audience, obviously companies within the payment space are using AI to do a lot of things, right? They're using it in marketing and fraud and pricing and a lot of different areas that we've talked about on this show.

And but you also have the agentic commerce side, which you talked a little bit about agents talking to agents, agents buying on behalf of people. So maybe talk about how the Emotive fits into those two different scenarios. So what a Motive really excels at, especially for our larger clients, which you know a lot are the you know, the the listeners of your podcast and the the the guests you have on here, it's around the compliance aspect of it. So you have to be able to prove unequivocally in an appending ledger that is hash, which that's cryptography, what has happened, especially the more removed humans are out of the loop, which I do not recommend, especially now, right?

That humans should be if if you completely have taken humans out of the loop, man, you you better have some great guardrails. You should be talking to a motive like months ago if you've done that already. But where a motive plays the best role is it's that memory. So what a lot of people don't really think about is we have been paying for services with our data for years.

Google is not free, nor is Gmail. You pay for that with your data. They take your data and the things you're doing and they make money off of it, and that's how you pay for those services. AI is no different.

Even if you're paying for it, you're not actually that's not what makes them money. It's the access to your data and information. And these larger Fortune 500 companies, especially, once they get past POC stage, so you there's there's four layers of this. And most companies are in the second layer pushing into the third.

So you've done POCs, you've done those all through 2023, 24, 25. Most of those are going to be internal. You're gonna have a few outwardly facing, because you know that's a that's a precipice. When you go past, when you start talking to your consumers and something goes wrong, way bigger implications than if it's completely internal.

But having access to all the things you do, the memory of it is so important for this more than anything else. For example, Greg, if I ever decide to step back into the enterprise world, which maybe I will, maybe I won't, I don't know. I will negotiate my digital data rights harder than anything else, harder than my 401k, harder than my stock options. And that is something new, right?

And what a motive does is allows you to do that as a company and as an individual. So if you control all the memory, so the contextual memory, all the memory that you're doing, all the things that you do with AI, and you own and control that, you can go to any platform. We should strive to be as platform agnostic as possible. For example, the platform, the node that I use that a motive builds and makes, I have no clue what AI, what LLM, what large language model it's actually using.

I have to go ask it because it's using, it has access to hundreds, literally, right? But it typically uses five to ten, some in an adversarial way. But as a human that's doing work every day, yes, I can geek out and I can tell you why 4.8 is better than 4.

6, or maybe it's worse because they don't always get better, trust me. Sometimes they get worse because they're tweaking them, right? And Anthropic, for example, has came out with three new models in the last, what, six weeks, maybe even maybe a little longer, but they're coming out left and right. So even me who does this every day, day in and day out, I don't have time to keep up with it.

So I have my AI keep up with it and I control it. That is so, so important for all these companies to really realize that, especially the leaders of it. You might not know what the best model is as the CTO, but your AI should. And then if you want to talk about it to your board, you go ask it.

Hey, I'm about to go talk to the board. Tell me why we're using these models and tell me the cost savings we're getting from it. Because if you ask the latest, greatest model, so you know, like for us, we're spending 2 billion plus tokens a week, we would be out of business really quick if we used the latest greatest model for every single question. And if we wasn't cat, if we weren't caching some of the answers and some of the questions, and it's the difference between spending, you know, $10,000 or $20,000 a week on inference to $1,000.

Because when I ask lesser questions, for me, it's just dumb questions, right? Russell's gonna ask lots of dumb questions, so we're gonna use this model that's free because it can answer them. And if we need to check it, we can. But then the the rest of it is all about trust and verification, especially when you get to the consumer face and products, which is where we all want to get to.

We want to make money off of AI. You know, everybody typically starts in phase one where you're building out these agents and they're great, and they're helping you do, you know, they're um scribes, which do I need to explain what scribes are? Probably. All right, so a scribe is is where it's taking like this, it gives you a condensed version of it, tells you what your takeaways are.

Those are scribes. There's I've been using, I'll do a plug for one, it's called Otter. I've been using that one forever, although I don't need it anymore, but I just need, you know, I've come compliant with it, right? Our system does it just as well, if not better, but I've been using it for so long anyway.

That's typically where a lot of these things start and impaired programming and stuff like that. But being able to independently trust the answer, and then when your compliance team comes back and they will, because especially if you're a publicly traded company, you do anything in any way, shape, or form with any type of regulated data. And what most people think is, well, I'm a yoga studio. I don't have, yeah, you do.

You have personal information of your clients, you have their payment methods, you know, you're if you're a business and you're taking money, at some point you you might have to show what you did with AI. And if you're using third-party software like your CRM tool, you know, your your tool that keeps up with your clients, or it's almost gonna be all these tools are gonna be using AI. So having the ability to show what you've done with it, how it's touched you, or how it's not touched you, right?

We're gonna have to give that proof as well. So I know that was a long-winded answer, which I know you're used to getting long-winded answers for me, but yeah, from a payments perspective, that's where that's where they, you know, all of the leadership, everyone should be thinking about that. If they don't think about it, it's just as equivalent as not architecting these things properly, right? So if your idea of using agentic AI is putting it, the first thing you do is just throw agentic AI at it, it's gonna fail.

It's probably the 37th thing you need to do, right? Where is your data? What kind of data does you do you have? It has has it been massaged?

Have you touched it? Is it clean? Is it scattered? Is it siloed?

Do you have vector databases? Is it in a warehouse? All of those things matter. Junk in, junk out has been a motto from for every data scientist since the beginning of data science.

You give me junk, I'm gonna give you junk back. This is no different. It is a little better at getting some of the junk out, but it's still not perfect. So, you know, being able to architect it properly and then being able to tell what it does, when it does it, and it's gotta be tamper-proof.

And the way you tamper proof it is you put it in a pending ledger that if anything's changed, because AI, to let's just say that you have the best AI in the world and has the best intentions, it will still, to make you happy, go back and try to change things that it did. And then when it changes it, it forgets it. It basically erases its own memory. We've seen that happen like 10 times.

Um, and a lot of times we'll get brought in after that's happened. And the question is, hey, can you make sure that this never happens again? And the answer is yes, of course we can. And uh here's how we do that.

So, yeah, let's talk a little bit about the regulation side because I know there's a lot going on in that space, obviously directly affecting what you guys do, and then obviously affecting where the future of all these AI implementations go. So, talk a little bit about maybe the state of the industry when it comes to kind of the regulations around this. Yeah, so I do. This is not a plug for them, but it can be.

I do chair the ETA Electronic Transaction Association. And I do that, one, because I love everybody that's doing it. I've been doing it for a long time. I was the crypto guy before I was the AI guy, because I want to stay as close as possible to the regulation.

We'll be up in September in DC having these conversations. And I want to say, and don't quote me on this, I want to say it was 700 bills that were in play, both state and federal. So there's lots. Lots of people are talking about, lots of people are thinking about it.

In our industry, the Colorado Act is probably one that's that's leading. But if you truly want to go and look at what regulation will look like, you should go look at the EU AI Act. You know, everybody on this this listening knows that the EU is gonna have stricter regulations than the than the Americas. That's always been the case.

And we're usually about seven years behind. Do we ever get to that strict of regulation? Unless there's a black swan event, probably not. But if you want to build things that can be bomb-proof when it comes to AI, guardrails, and regulations, go look at the one that is the strongest out there, that's the strongest now, and build it to that spec.

I cannot express that. Now, do you have to use all of those things? No. You should leverage what the laws are, and we're very good at that.

But playing by the strictest rules and building and architecting to the strictest rules is your safest bet. The worst thing that can happen, and this is not with with generative AI or AI or agentic AI, this is with any technology, is spending a hundred million dollars to a billion dollars on something, and then regulation came come out, you know, six months later that now you can't do that. And we've seen that happen, by the way, many times. So keeping your finger on the pulse of that, having somebody that is that is really watching that is very important for this because it's still not set in stone by no means.

And I do think it will it will get set a little bit quicker than, say, like crypto. Crypto is still to this day up in the air, right? But generative AI, gentic AI, especially as we roll into 27. So we're 26 is gonna be the prep year for doing a gentic AI.

There'll probably be a digital asset aspect to it because that's the nature of how it works. But that is coming and it's gonna come faster than a lot of these other technologies, and you can see that with the adoption rate. Never before, never before has there been an adoption rate faster than a generative AI ever. So that if you just if you just use that as your guidepost, it's gonna happen way faster.

And in payments, we have not been particularly good at being the cutting edge. We're usually a not even a fast follower, but a follower. So that's gonna have to change. Our mindset's gonna have to change.

And having those rules, regulations, guardrails in place, it's gonna be so important. And regulation will tighten. Like I said, when things go wrong, that's usually when things get tight, of course. And you know, you've even got you know, anthropic themselves.

They just came out with an article, which we're writing an article about that article saying, hey, we've got we've got to put the brakes on some brakes on this. It's moving too fast. And I don't think that's the right term. I think guardrails is the right term.

You're not gonna slow it down, but yet you can put guardrails up in place, not only from these large tech vendors that that are providing these amazing services, but you as a as a consumer and you as a tech leader in your business, you can right now start doing these things. These things, you know, that's why a motive exists, is to is to put these things in place. So when you do start having a true agentic interactions with your your merchants and true agentic interactions with your acquires and ISOs and ISVs and PayFACs and all the things that we do.

This ecosystem will change, yes, but. But there's a lot of moving parts. And there's only three companies on this planet, by the way, right now that can have all the pieces and do everything. And uh they choose not to because they need to spread the liability, and AI is the same way.

You've got to be able to make sure you're spreading that liability and you have the guard rules and the rails in place and you're owning and controlling it, and you're as agnostic as possible. That's the tool sets that this gives you, Greg. Like you can be as agnostic as you want to be with these things if you build it the proper way. Okay.

Well, when you kind of step back and look at the company, what does success look like for you over the next, say, three to five years? Bringing in those clients, being able to shift away. Because right now, a lot of our clients have no idea even to where to start. Like they'll come to us and say, we can't get this to work at all.

Memory's great, Russell. We love it. We love hearing you talk about it and we think it's going to be great, but we can't get it to work at all over the next year. That'll start going away.

Like the days of you building out an agent and selling it, my 11-year-old has agents that she's built and they're amazing. Like it just, I'm like, wow, what? Okay. So that'll go away.

And then it'll be all around the verification, the third-party verification, especially for the medium-sized to larger clients, that it really, really matters. Like, you know, when stuff goes wrong for a large Fortune 500 company in payments, you can go to jail for that. And you don't want to go to jail. So that's where we're looking at going.

We've got this beautiful pipeline. Beautiful. I'm not going to say who I'm imitating, but it is beautiful. Uh but yeah, for us, it's it's all about it's an amazing adventure.

It's going to be even more of an amazing adventure because it's we're at the cusp and the cutting edge of all this stuff. And sometimes, so for example, this past week, the whole emotive crew went down to a vet conference and it was we had so much fun. It was such a great time. But you know, if you look at the veterin space, which is healthcare, right?

They still have they're still extremely regulated. And but I had to, I had to kind of take a step back and and think, man, they're really just getting started. And a lot of industries are that. I would say programming and payments, if you're in a payments and you have developers, I guarantee you they're all touching AI.

And that's a that's something too. Everybody's using it. They're just not using yours and they're not using what you have control over. That's that's the thing.

And you can't stop them, right? People like if you if you allow someone to have their cell phone on them at all times, and you're not taking that from them when they walk in the door, they're they're using AI. Like it's on their phones, and it's been on their phones. We've had access to Alexa, you know, for as a matter of fact, my 11-year-old Greg has never known life without having AI in it, you know, because we had it when she was born and she's been using it.

I used to say when she was like two, I would, you know, I would speak about this. Does she think Alexa as like a a relative that's just not here, like, you know, my great aunt who she talks to on the phone every now and again? And now that she's she's older, she's 11, she's like, no, no, I I always knew that it was it was a computer, that it was, it was, it was intelligent, but it was still a tool. And to hear a child say that, we need to take note of that.

Because yes, agentic AI, generative AI is amazing and probably one of the most powerful things that we've ever created as a as a species, but it's still a tool, and you've got to treat it like a tool. So yeah. That's where we're going with it. Yeah, I love it.

I love it. So curious, like you've talked a lot about enterprise companies, and obviously they probably have the most to lose, but our industry is made up a lot of medium and small companies, right? So, where does this play? Because I've talked to tons, we've read a lot about like everyone's launching something with AI.

Where does your kind of product and service fit into kind of that from a size perspective? Is it just as important for the little guys as it is the big guys? Kind of talk through that maybe a little. Yeah, it's to be able to trust and verify what you're doing with AI is just as important, maybe even a little bit more important because you know, you have a catastrophic event at a large company, they write it off.

You have a catastrophic event at a small company, you go out of business. So you measure what's more important from you know my perspective as a small business owner now. We've got to make this stuff safe and secure because I do not want to go out of business because of a mistake that AI makes for me. But you know, it's it's the same thing as you put things in place for all the code, or you know, you should be.

Those are best practices. So having those best is so important. And a big difference between this, it matters. Like if you're using one, let's say that you use uh Microsoft or Google or what, and that that is your vendor of choice, and you're just using their product and you're not controlling the memory and the interaction that you're having with it, you're now locked into them.

And if uh you want to go and renegotiate a price, which we have to, we have to do this to stay in float as small businesses. You know, we need the best price we can get and the most leverage we can get. So having that leverage of being completely agnostic when and where you want to be, super important. And make sure, like, well, what happens if a motive goes away?

Well, we make sure that this lives on forever. So, you know, it's in a repository that never dies that you always have access to. So if a motive goes away, and it won't, in seven, eight, ten years, you can still go back and look at because it's always appending. That's what's the beautiful thing about these, you know, cryptographic hashed ledgers.

They don't go away if you don't want them to. So the the businesses could be long gone and you can still trust, attest, and verify that these things happened or didn't happen. Because that's another thing. As a small business or the medium-sized businesses, you're gonna have to attest that no, we don't use it here.

Well, how do you do that? You gotta prove it. How do you prove it? Math.

At the end of the day, if you can't mathematically prove that something was done or not done with AI, I don't believe you at all. Just like I'm not 100% sure that you're actually Greg. You could be Greg's AI. I don't know.

Like, I can't prove it anymore. And that's the level of proof that we need because it's so good at making, you know, we've all seen it. Like it, you know, you can go in and in in a couple minutes, I could take this exact thing and have me speaking French or saying stuff that I didn't say instantly, right? Like, so that's the age that we're at, and it's only gonna get better at it, it's only gonna get trickier at it.

Like, you you you've got to have guardrails. If you unleash these things when they did open claw, I was like, oh, oh, please don't do that. You know, you don't want these things not to have control. And you know, anthropic's right.

We need to put guardrails around this. We have to. So there's so many different angles that we we could continue the conversation. And I definitely, definitely want to have you on again and keep this conversation going because I think it's it's so important, especially to our industry.

I mean, the last thing we need, right, is something major happening around this topic, and then the regulators come in and make it incredibly difficult to do business, which hopefully will never happen. But I think that's why this is obviously a very important topic. So, what to continue the conversation? But before we wrap up today, if you can kind of boil it down, like what's the one thing?

So, a lot of payment leaders are listening. Like this is deep into fintech and payments. They're they're listening to this. What is like the one takeaway that you want them to take from this?

So that's the first part of the question. And second, what's the first step? What do they do? Well, I mean, obviously reach out to you makes sense, but what should they really be thinking about?

And then what do they do as kind of the first step? The first step of all of this is you got to start using it, right? Like pick small projects, low-hanging fruit, things that if you mess it up, are not gonna have large ramifications. That's the first step, period.

The big takeaway is you have to control this. You have to have the ability to control this. If you are just letting a hyperscaler take all the information and work you're using and it's sucking it into a black box, and you have, and you're like, well, how do I get that out? And they're like, you really can't.

And they're I'm not saying they're doing that on purpose, it's just not their business strategy, right? They they're not worried about that. You have to worry about that. You have to be able to control it, trust it, and verify it.

I have done these projects, Greg, at the most massive scale that you can possibly do them. And I've watched, they had that article come out that MIT wrote. It said basically that 95% of all AI projects failed, which was clickbait, by the way. That is not actually what that was.

It was 95% of these projects were not meeting the ROI, the return on investment that they said in the beginning. And that's because they did not architect them properly. That was the problem. The AI actually did exactly what it was supposed to do.

But if you're relying on, say, RAG, you know, just a retrieval architecture, once you scale out, it is going to fail or not, not it won't give you the ROI that you have in your business case. So you have to be super mindful of that. You have to have memory. So the the term for that is memory enhanced in context learning.

I know that's a big mouthful, but you can play it back. That is where you should be in your phase two, phase three. And if you don't control that from a personal level to small business, to medium business, to large business, if you don't have control of that, you're you're putting yourself at a massive disadvantage. And the companies that do have control of that, so there's a race of using AI, and then there's a race of who controls their own destiny, really, by controlling the information, and then the guardrails you put around it.

So really it's guardrails, verifying, owning that memory. Like, and and I and a lot of people are gonna be like, I don't even know what he's talking about. How do you own the memory? Like, that's that's what you know a motive does is make sure that those things happen.

And it's our fiduciary responsibility. Got that word in there. That was my that was my word to say. But it truly is, it's true.

We the as the leaders that are gonna listen to this, is their responsibility to make sure that their businesses are doing these things and putting these steps and they're aware of it. So yeah, trust and verify. Okay, love it, love it. Well, Russell, this has been been a great episode, and thank you so much for sharing all your wisdom today.

And like I said, want to have you on the show more often. And I know your time's valuable, so I'm gonna let you get back to work, but I really appreciate you being here today. Thank you so much. We will talk soon.

Thank you. Always a pleasure, my friend. Always a pleasure. Okay, and to all you listeners out there, I thank you for your time as well.

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