Watch the full conversation: Data Is Gold — Founders Paradise with Chris Schuring
Charles: Hello everyone, my name is Charles Davis and I want to welcome you to the Founders Paradise broadcast. On this broadcast, we're going to be talking about AI, brands, and data. I'm going to let my co-host introduce himself. His name is Chris. Chris, introduce yourself.
Chris Schuring: Good morning everybody. My name is Chris Schuring and it's great to be on with you, Charles. I look forward to a great conversation. I've been involved in data and computers for close to fifty years. And as I had mentioned before, I started out in this little journey back in nineteen eighty at a little company called Atari and built that up through running data centers internationally for a lot of corporations, and here we are today with a new forefront in artificial intelligence and we're now in the data age.
Charles: I've been really looking forward to this, Chris, because I very rarely get to talk to people that has the longevity in the industries like yourself and myself. I started in the industry in 1978 working, and I've been programming computers since I was sixteen. So I've been involved in the IT space since before personal computers.
Chris Schuring: Yep. I started the systems — I was doing the HR system on card decks. Human resource card decks. It was a card deck that was about four feet long and a bunch of little punch cards.
Charles: Hollereth cards. That's what they — I remember those things. In high school we were using an RJE terminal connected to IIT, and I learned programming in Fortran at Limbloom Tech High School.
Chris Schuring: I did COBOL.
Charles: Okay, so we have a lot of similarities in the background. Our first topic — we're gonna open it up with AI. What you got?
Chris Schuring: AI is a wonderful thing. For me and probably for you as well, I look at AI as just another version of COBOL or Fortran programming. I remember using COBOL programming to go into the dot matrix printer to make images, right? So you could tell the dot matrix printer to make an image on paper, because at that time we didn't have the ability to create images on a CRT terminal. It was all single-line edit kind of stuff. But I think artificial intelligence is upon us, and if we embrace it and use it wisely and thoughtfully, I think it's gonna really drive technology going forward. And yes, there's a lot of concerns around it, but there's a lot of concerns around the first computer. There's a lot of concerns around going from a horse-drawn carriage to a car. It's just a matter of adopting these changes and allowing them to drive technology. Not that it's a good thing, it's not a bad thing.
Charles: My first introduction to artificial intelligence came when I was in high school. There was this movie called The Forbin Project. He made a computer called Colossus. And then there was a Russian scientist who created an equivalent, and they connected over the internet and then they took over the world. And I said, I want to do that. I want to get involved in that. And I never thought I would see it in my lifetime. I really didn't. So it's as big as the introduction of the internet itself.
Chris Schuring: Yeah, it's a phenomenal thing. It truly is.
Charles: It is that huge. So what are you seeing on your side?
Chris Schuring: When I first started really playing with the current version of artificial intelligence, it was all about doing images and documents and doing research in a quicker fashion than I was able to do before. I could create a question, and that question allowed me to create research and documentation information that drove my businesses forward. I did a talk last year at a conference and it was really all about AI is a great tool if you understand how to ask the question, right? It's all about asking better questions. So if you ask better questions, you get better results. Now what I'm using AI for — we have a chief technology officer in our company who uses AI on an hourly basis, and we use it to create tools and do programming and to create what we now call bots, in order to facilitate business in a faster and cleaner way. So we're using AI to create back-office tools, our own CRM, outreach, marketing campaigns, videos, lots of different things that enhance our business, and it doesn't reduce our need for headcount or talented people. It just allows those talented people to work better and have better resources. So we're seeing it as a great addition. It's like any other thing in technology or changes in industry — we can use it for good or we can use it for bad. Everybody has that option. We use it for good, and we see it being able to lift up people that aren't necessarily in technology today, because it can be easy to use. It's not hugely complex at the surface level.
Charles: What I'm seeing it as, as a developer — because I'm from the development background, I have learned to use Claude, Gemini, and Grok in concert together, and I've created a SaaS product because there's a looming problem, and this is going to lead into our next topic. In 2008, Eric Schmidt of Google said the internet is a cesspool of misinformation and that brands are the solution. So what I'm seeing is how are they using AI on a brand level? We're talking about it on a business level. There's a bigger brand battle going on that was similar to when they released the internet. There was a brand battle going on between IBM, HP and Sun, the various software vendors, the database vendors. So this is playing out on a higher level. And I think that's going to affect how this whole thing plays out, such as which AI models are going to be endorsed by the US government. They're the ones that's gonna decide it. What do you think?
Chris Schuring: Well, I think there's some of that, but it's also — when everybody started first using AI, there was maybe one or two venues for it, right? There's ChatGPT or maybe DeepSeek or something. But now, because everybody has been doing development and using it for development, we're seeing a lot more branding of the tools of AI, and those tools are allowing corporations to fine-tune and grow their brands, not only domestically but internationally. And there will take a turn where there are major organizations, whether it's the government or other major organizations, that put their stamp on "this is the one we're going to use." And that's going to drive those brands of AI-developed applications to a higher level. When Elon comes out and does Grok bots and those kinds of things, the venue that he puts that out in allows for a great branding of that particular developed tool. And I think we're gonna see some more of that. And it's like anything else — it's gonna take another year or two, maybe more, for everything to kind of settle out. And then it'll be, okay, let's all do COBOL from here on out, right? Those kinds of things. And the — I don't wanna say lesser developed brands, but that's the terminology I'm gonna use — will be absorbed. In our business, at Data Revenue, we like to say the big fish will eat the smaller fish.
Charles: That's the right term though. That's true.
Chris Schuring: And that's what's gonna happen. So we'll see maybe three or four emerge. I see the same thing happening in cryptocurrency — at any given time there was forty thousand different crypto coins out there. Well now you only hear about the top ten, because the rest of them are just not big enough or impactful enough to really hear about. So you hear about the top ten, and those are the ones who have risen to the surface. And the rest are either playing catch-up or being absorbed by the top ten. And it's going to be the same thing in AI, and I think that's a good thing for the basis of continuity. What'll happen with Google and all the other companies out there who are creating their own branded development applications is the richness of the data that sits behind all the things they're doing is just gonna get wider and deeper. And when it gets wider and deeper, when we use those AI tools, we're gonna have better results, and there'll be less of what they call hallucinations in our AI product.
Charles: That's an interesting point that you brought up, because I got a story about that. I wanted to tune up a car — it was a Chrysler LeBaron — just to change the spark plug wires and the spark plugs. Something real simple. So I go to AutoZone, I buy the spark plugs and the wires, take it out to my parents' house, and I'm gonna just change the spark plugs. So I pull out all the spark plug wires, then I pull out all the spark plugs, I put the spark plugs back, but then I recognize I didn't mark where the spark plug wires were supposed to go. So I said, no problem, I go to AutoZone and I buy the book, and it's got the diagram there.
Chris Schuring: Yeah, if you don't put it in the right order, you're not gonna have the right results.
Charles: I took the diagram and wired it according to the diagram, and it didn't work. This is a true story. So I pulled it apart and did it again. It still didn't work. So now I gotta call the tow truck and put it in the shop. I take it to the shop and the mechanic said, let me see the book. He looked at the book, he laid it on the engine, and he pulled the wires in — off the same diagram. And I asked him, this is a true story, Chris, I'm not kidding — I asked him, what did I do wrong? He said the diagram is upside down. That's a Japanese diagram. Only a master mechanic would know that. So that feeds into your point where — we're absorbing information off the internet, but some of it's not correct. And that's gonna be a problem. So brands have to be concerned about that.
Chris Schuring: Correct. Well, and it's gonna get cleaned up. It takes time. Like the first school books that came out — maybe they weren't quite right, but after eighty or a hundred years they start to get it right. And like you say, it is about how you place the book when you read it. I was in a situation where somebody did the same kind of thing, but it was on the floor plan of a building, and they read the floor plan west to east when they should have been reading it east to west, and they made about a million-dollar mistake in placement of things within the building, because they really were reading the blueprint upside down. So it's about that trial and error and getting the information correct. And it's kinda like when I used to be a Unix system administrator — you could write in all kinds of command-line prompts, but if you didn't put in the right prompt, it wasn't gonna do what you thought it was gonna do. Same thing here. We all learn. My analogy that I use when I talk about AI was — imagine you went to your grandma and said, "Hey grandma, can I get a cookie?" Well, you don't have any idea what kind of cookie you're gonna get or when you're gonna get it, right? But if you went to grandma and said, "Grandma, in the next hour can you give me a chocolate chip cookie like you baked last week, that's perfect, that you made in your own oven" — if you get more granular and specific about the question you ask, you will get the results that you want. That's all AI is. Ask the right question, and you'll get the results that you want.
Charles: You brought up a very relevant point. What I've been trying to point out is I'm calling it AIY2K. Because it's out there. There's some assumptions that we've made that are gonna haunt us in the future. It's just an assumption that we thought was fine. Later on we're gonna find out, no, that was a mistake. Because you brought up the Unix administration environment — that is the heart of this technology that we're using right now. What's your best horror story?
Chris Schuring: I got a few — when you brought up the Y2K thing, I was working for a company at the time, and there was three months of prep work. We're gonna go into Y2K, the computers don't recognize leading zeros, blah blah blah. There was all kinds of stuff going on, and we sat there up until midnight, the whole crew watching everything. We thought we'd prepped everything, and it wasn't necessarily a horror story, but the next morning came and nothing happened. It was just — wow, that was a tension-lead-up story to, we were expecting chainsaws and Halloween and massacres and everything else, and midnight struck, and nothing happened.
Charles: It was a very stressful moment. I know, I was there.
Chris Schuring: Yeah, well we had pizza and Mountain Dew for fifty people, and we were sitting there waiting — we were gonna have to work all night long thinking we were gonna have to recover everything, and we were at the forefront of some internet-based credit card transaction processing, and we thought we knew that if the system went down we were gonna lose about five million dollars a minute in revenue. So we were sitting there going, if this goes down and we lose five million dollars a minute in revenue and we don't recover it right away — we're all out of a job. Not that anybody else could have solved the problem — they would have brought other people in, and it would have been terrible. But midnight struck and nothing changed. We ate all the pizza though, but nothing changed.
Charles: Well, my story would be — I was working as a Unix administrator on a high-availability cluster that processed the Chicago Board of Options trades overnight. They had been experiencing a cluster failure for two years. One of the clusters would fail, nobody could figure it out, the other two just took over and finished it. However, coming into Y2K, this had to be fixed. They brought me in, and I tracked it down to a hardware device where the microcode had not been updated to stay in sync with the cluster. It took me two months to figure that out, but they had been living with it for two years. So those are the kind of problems that I'm concerned about, because we're running AI in a Linux/Unix environment. That's my concern.
Charles: So where are we gonna go from here? You got one you wanna bring up, a topic?
Chris Schuring: No, it's all good. Back in those days too, prior to Y2K, we actually had to check things sometimes at the binary level of code that nobody does anymore these days. We had programs that would fail and couldn't figure out why they failed, and at the end of the code somebody had actually hit the space bar. And the space bar, although visually it didn't show any character set, internal to the code it showed a character set equal to space bar, and it crashed the system every time. So we actually had to do binary code reviews to see — wait a minute, there's a character here but there's no visual character, because people had hit a space bar. And I think we'll see some of those types of things in AI simply through this term now that they use called hallucinations — things that we don't expect. Like when I do an image and it comes up and it says — that's not how I asked it to spell that, right — it puts some sort of weird word in there, or six fingers on something, right? And when it does that, that is code being augmented in not the right way. So when we see an image and it doesn't quite correlate to what we think it should be, that image is being created by code, right? And that code, as it gets smarter and smarter, will learn to start correcting itself, or will start correcting it. But in the meantime, we're gonna see those errors, whether they're visual or not. And that is where the human side of things needs to stay connected. Because if the human side of it stays connected, we can review those things before they get, quote unquote, out into the wild, and we can look at those things and say, okay, we need to correct this because the code did not correct it. So there's always going to be a need for human intervention, because the code is only what the code is — it doesn't have forethought, it doesn't have intuition. It doesn't know to turn on the binary code and notice that there's a space in there that there shouldn't be. So we're gonna still be engaged, we're gonna still be part of the entire process. And in that we'll all learn more and get better, and frankly, we'll be able to earn more money as well. We'll be able to have higher incomes because we'll be the ones that can solve the problems.
Charles: Absolutely. That corporate troubleshooter in the technical infrastructure, what's going on — they're gonna be in high demand. But that brings me to the next topic, which is the meat of our conversation. You have a product called Data Revenue dot io, if I got that right. Because the internet is all about data. You go first.
Chris Schuring: Data is king. Data is king, and it underlies everything we do, both in life and in business. When I was in operations, I was considered an operations specialist — I was that troubleshooter during the dot-com phase. I was the guy they brought in to say, "solve this problem," and they'd write me a big check, and then I'd solve the problem and go do it for the next company. I did that both domestically and internationally. Data has always been the underlying component. But when I was in all of that, data was a cost item. It was a liability on the balance sheet. It was something we had to pay for — the hardware, the personnel, all the things, right? And as we built that, that liability was in operations. Now data is an asset. So now we're in the data age, right? And because we're in the data age, we now can look at how does data create revenue and how does data allow us to create a stream of income for the company. So in datarevenue.io, what we're able to do, and what has actually been done for quite a while, is to take a look at the underlying data within a corporation and harness it as an asset. And then once it's harnessed as an asset, like any asset within a corporation, we can then turn that asset into a revenue stream. We like to call it newfound money. Because we've seen organizations now that have data they've collected over the last ten or fifteen years of business, that they're spending money to maintain and house that data. We can take that, and that data now becomes actually a value, and that value is actually greater than the entire enterprise value of the corporation. We have a customer now whose enterprise value of their corporation was sixty-eight million dollars. Their data value was one hundred thirty million dollars worth of data valuation, because they were not using that data to create revenue. We now can take that data and turn it into that asset and then create revenue for the company. And that revenue then can be used for whatever the company's needs are. But the nice thing is, it's 100% revenue. There's no margins, there's no overhead — it's pure revenue coming into the company on an asset that they already own.
Charles: Right. Well I could piggyback on that. There's a lot of stories, but the one that's most important was I was on the International Harvester to Navistar rebrand, when they sold International Harvester and they had to rebrand the company, and it became Navistar International Trucks. The thing that we learned — we supplied a system called System Twenty-One and TOPS in the manufacturing sector. There's a thing called mean time between failure, your data, right? And so we devised a system that when a truck came into a dealership, we knew what was gonna fail and cost the owner of this quarter-of-a-million-dollar investment money that they could prevent, and keep lowest cost of ownership. That system was actually pioneered by Navistar, and if people can recognize, that's the same system you experience when you take your car into a dealership. They're using AI to analyze the cost of your vehicle and what's coming in the future. But here's the other flip side of this too — there's another aspect of data. Are you familiar with Blair Enns, The Business of Expertise?
Chris Schuring: I've heard of it. I can't say that I'm completely educated on it.
Charles: You and I are experts at what we do. That is data, okay? And now what I'm seeing is that our real moat of authority is gonna be based on our expertise, the knowledge we have, and that will be a defensible moat for people like you and I. And so I've developed a SaaS platform that allows us to build that expertise moat on the internet, because AI is now looking at your expertise. Because remember back at the beginning, I said Google said misinformation — they're now using AI to drain the cesspool, because there's a lot of people out there making claims that they can't support. So what are you finding, where is this going in the future for you?
Chris Schuring: You know, it's gonna drive a new economic model, bottom line. It's gonna drive businesses into situations that they were completely unaware of. It's like when — here recently, Spirit Airlines filed bankruptcy. And what happens in mergers and acquisitions and receiverships is that people don't grasp the value of data. So when Spirit Airlines filed bankruptcy, they were all concerned with selling the planes and where the people gonna work, all that stuff. Well, they ended up selling their data, lock, stock and barrel, to Google for ten million dollars. Well, now that Google got that data — Spirit Airlines as an organization will never be reborn, because they sold all of their data to a non-airline-driven entity that did nothing more than to take all that information and dump it into their AI engine. That's all they did. But we know, from our point of view, we were actually trying to get into that conversation, because we know that their data at Spirit Airlines was actually worth one and a half billion dollars. Google bought it at three cents on the dollar. We knew that their data was worth one point five billion dollars. Now, if they had been looking at the fact that their underlying data could have brought them out of bankruptcy and recovered their company, Spirit Airlines would still be alive today. But because they didn't look at the fact that their data had a value, the company went away. We see that as a marker in the merger-and-acquisition, receivership business, those kinds of things. I've had at least three conversations in the last ninety days with companies who were on the verge of bankruptcy, but when we had a discussion about their underlying data, we are now helping them to get out of bankruptcy by valuing their data and putting it on their balance sheet in a way that we do that. And when we put it on their balance sheet, they now can look at that as a recovery out of bankruptcy. So data is gonna change the final financial picture in a lot of different sectors of corporate industry, both domestic and international. This is something that's an international spectrum. We recently just signed a contract, and I'm gonna pat our team on the back — we now, at Data Revenue, have over ten billion dollars worth of data valuation under stewardship. So we are now stewarding over ten billion dollars worth of data valuation within our corporation for our clients. That's gonna change the picture for a lot of things, right? Because that ten billion dollars of stewardship turns into revenue over time. If I were to come to a company and say, yeah, we can add ten million dollars to your bottom line, and they're a five-million-dollar company, their data is now worth more than their actual company.
Charles: Chris, you just brought up a really, really important subject. I have several YouTube channels, and the other one is called The Serio Factory. What I do is I analyze crisis-to-revenue opportunities. And I'm listening to this — I can see revenue opportunities coming out of this crisis. When you were talking about this recent one with Nike, when Nike lost all that money — that guy they put in as chief technical officer, he pulled out their tentacles into Foot Locker, and it collapsed the brand, because they have no understanding of the data. None.
Chris Schuring: That's correct. And we could go into them, and their data is worth more than the money they lost. And all I need is a conversation with the right players, and we're off to the races. And I do that on a daily basis. I'm in twelve hours a day of Zoom calls, my friend. So that's exactly what it is — a company, so when Nike, an example, has those problems — did they go to the operations guy and say, "by the way, take fifty percent of the data and trash it, get rid of it"? No. The data is still there. It didn't go anywhere. Now their revenue changed and their model changed, and the hierarchy changed, and their distribution and penetration to the marketplace changed. But the data didn't change. It's still there. It's the same data that was there yesterday. That data has value in the marketplace. It may not have intrinsic value within Nike, but the outside world — I mean, there's groups right now who are buying rare and out-of-print books just so they can digitize them and put them into AI models, and then destroy the books so that nobody else can do it. Companies like Google are buying data purely for the purpose of putting it into AI models. Did you know that in the medical field — let's say you're gonna go and get a grant for twenty million dollars from the federal government to develop a new wonder drug. Well, in order to get that grant, you've gotta have research material, and you've gotta have data to back up your thesis and your philosophy on why you need that grant. Where do you think they get that data? They get it from other companies who have created the data, and they buy it from those companies. They didn't do it themselves. So we have medical firms that sell snippets of their de-identified data — they de-identify the data, they take all the patient information out, they sell that data to large pharmaceutical companies for a very high value. Those pharmaceutical companies coalesce that all together, create the statistics needed to create research papers, in order for that pharmaceutical company to get a grant. It's all data.
Charles: So Chris, this is really interesting conversation. Have you ever heard of Infere — Information Resources Inc.?
Chris Schuring: Yep.
Charles: I used to work for them, in my system administration days. They collected all the point-of-sale data from all the grocery stores all over the country, and they sold the data to manufacturers. So I'm very familiar with what you're talking about. So we have all of this data, and the money is in the data. It really is.
Chris Schuring: It is. And corporations are spending money to house it, but they're not looking at it as a potential revenue stream to bolster the company. If you look at — there's a case study out there for Caterpillar, the big yellow iron guys — they did this entire process, they created Digital Cat. You can look it up, I think it's at digitalcat.com. And when they did that, and they took all their digital assets and put it into Digital Cat, it bolstered the company three hundred percent overnight. They are now, from a revenue and impactful statement, being able to look at their valuation on their stock portfolio — there is nobody in that world that can catch them. So if they want to go buy another company, they can trade that stock and buy John Deere, or they can buy any company they want, because they are now the nine-hundred-pound gorilla in the room, simply because of data.
Charles: Wow, that's a huge statement, Chris. So really?
Chris Schuring: Blackstone's been involved in it, airlines have been involved in it. So what we're doing is not new. How we're doing it is new. The way we approach it at datarevenue.io, nobody else is approaching it the way we are. And we are the only ones on the planet doing what we do currently in the data space, bar none. We do our research, we have great research and great tools that we've built, and right now, the way we view data and how we allow a company to be able to leverage that — we're the only ones on the planet doing it. That's why we were able to put ten billion dollars worth of asset valuation under stewardship. That's B with a B — with a billion. Not million, that's ten billion.
Charles: When you say stewardship for our audience, tell them what that means.
Chris Schuring: I'm gonna use a house analogy. If I have a house and I do an appraisal on the house, that's fine — I can say my house is worth half a million dollars. But if I don't do anything with that house, it was really a waste of time. There was nothing really that we can do with that. It's just, yeah, I got a house that's worth half a million dollars, but if I don't do anything, there's nothing to be done there. So we basically value people's data under that same premise. And then stewardship is we take that value and we generate revenue from it. So not only do we appraise the house at half a million dollars, now we're gonna get people to come see the house, we're gonna get people to do an Airbnb, rent the house, sell the house, leverage the house, maybe eventually get a mortgage on it, maybe eventually use it in an equity pool to buy more houses, maybe eventually take cash out to be able to buy other houses. But once we value it, then we can use proper stewardship to create that revenue to actually create real value. And the two founders of our company — I'm not one, I'm just the vice president of business development, I drive everything revenue coming in — but the two founders of the company, each one of them have over thirty years in high finance with companies like Morgan Stanley and UBS, doing those kinds of things, right? So when we do stewardship, our clients now can talk to probably the two most brilliant financial minds currently operating in today's market. They can talk to them, and they can say, they're much better than that. And they've worked at the billionaire level in financial engineering and architecture. And so when somebody says, "I'm gonna add a hundred million dollars to my balance sheet" during a data evaluation, when they talk to Martin and Robert, Martin and Robert can then take that and spin it fifteen different ways to Sunday to get — basically their question is, "Hey Charles, we just put a hundred million dollars on your bottom line. What do you want to do with it?" And we can spin that a hundred different ways for mergers and acquisitions, business expansion, all sorts of ways. That's what I mean by stewardship.
Charles: Wow. Well, I'll tell you what I wanna do with it after you wake me up with some smelling salts after I faint. We've been going — let's wrap this up, we have the forty-minute mark. It's been a great conversation. What do you want our viewers to know?
Chris Schuring: I want them to know that, number one, AI is not something to be feared. It's not something to shy away from. AI can improve our lives and do a lot of different things. And the other is that through that, and through technology, data drives our world. I don't care what part of the world you're in, data drives it. Whether you're using the GPS on your phone, or whether you're talking to Google on your Google Home device or your smart TV — everything is collecting data. We did a study, and there was a report that came out — do you know that at any given time there's seventy-five billion devices connected to the internet at any given time of the day? Seventy-five billion devices. Do you know that we're creating eight million terabytes of data every hour, on this planet? And what we really need to understand, from a corporate level, is that data will drive your business. Elon Musk came out with a news release that said his robotaxis are going to generate a trillion dollars a year in revenue within ten years. He also said that eighty-five percent of that trillion dollars of revenue will be driven by data.
Charles: I saw that video of those things, of them getting in one. I was impressed. How does — you're working with enterprise-level clients — where's the opportunity for SMBs?
Chris Schuring: There is a huge opportunity, because they're being driven by data just as much as anybody else, right? And there is no reason why they can't be a client, and we can't help them achieve their goals as well. We're working with real estate organizations, developers, pharmaceuticals, pharmacy companies, everybody. And the other nice thing is the way that it's done — let's say, Charles, you've got four companies and they're all considered SMBs, small businesses. Well, we can take all four of those companies, we can aggregate all their data together, and that aggregated data can be put into one situation, and that one situation can drive data to support all four of those companies. So there's different ways to make it all happen. And we're all about — I mean, I've been a small business and startup consultant for thirty years. I've owned several of my own startup companies, both in energy and other things. I had my own algae company for a while, did carbon capture and storage, we were looking at making biofuels. I've owned my own solar company. I've got five books on Amazon, so I have my own little publishing thing I'm doing, all that kind of stuff.
Charles: Really?
Chris Schuring: And it really all starts, Charles, with a conversation. If somebody wants to reach out to me, it's really easy — Chris at datarevenue.io, we try to keep it simple. You reach out to me, let's have that conversation, because it all starts with that conversation. It starts with a conversation like this, where we can start to share ideas, and we can start to put the scenarios together, and those scenarios will help benefit us all.
Charles: Absolutely, absolutely. Chris, it's been a pleasure talking to you, and I've enjoyed this, and what I will do is we'll discuss this outside of this conversation. I want you to stay online.
Chris Schuring: Same here, Charles.
Charles: I'm gonna click stop the recording, but we gotta make sure it gets uploaded to the internet before I disconnect, okay?
Chris Schuring: No problem, it's all good.
