E130: Kurt Stein On The Impact Of AI On Mergers And Acquisitions In The Tech Industry

[00:00:00] Ronald Skelton: Hello and welcome to the How2Exit Podcast. Today I'm here with Kurt Stein. He's the president of DCT Strategy and a technology expert. We're gonna have an exciting show for you today. We're gonna talk about technology and mergers and acquisitions, artificial intelligence, and the future of tech when it comes to our world of doing deals. Thank you for being on the show today, Kurt. 

[00:00:18] Kurt Stein: Thank you so much for having me, Ronald. 

[00:00:20] Ronald Skelton: I always start with origin stories, and you've been in this industry a long time. You understand technology as how it applies to mergers and acquisitions. You, and I've had a chat before the show today. Cause I've been looking for somebody that can have this conversation and not everybody can. Could you give us your origin story? Kinda let people know your background, how you got into this space, and,so we can build a little bit of rapport. Then we're gonna get into some really cool stuff. 

[00:00:42] Kurt Stein: Sure, sure. Happy to do so.I'll tell you, it wasn't an organic path to get to where I am. So I'll go through it. I started out at UPS when I was 18 years old. I think it matters because of, what I learned there.

[00:00:56] So I came out of high school, started at UPS and said, this is great. I get to work for a company that will pay me by the hour. I have to work internally within the, the operation side, which is unloading and loading trucks. And then, there's a certain shift that allows you to work it and they give you, provide you tuition reimbursement. To me I was like, this is great. I was in a predicament where my parents couldn't afford to pay for my college education. So I had to work in order to pay for college. That put me on a graduation timeframe of 13 years. And I know when people first talk about, yeah 13 years, the first thing everybody thinks is what happened?

[00:01:36] So what happened was, I started out, I did my first, two years and got my associate's degree, and then as I, I had to go all in. I was basically working part-time at UPS on full-time hours. Everybody understands that in, in the world. They'll try to get more from you. And I worked in the union unloading those big 18 wheeler trucks, dripping sweat, doing all that. And then I moved into, it was called sorting packages up on an aisle. Come out to you, you sort 'em to different, zip codes. And then I chose, look, I have an opportunity to go into management. So I was gonna leave the union and go into management.

[00:02:09] And it's interesting on that side, it was almost like you were leaving the dark side. You're going to the dark side and you're going from a union to management. But the reason I did it at such a young age is I wanted to get that corporate, that corporate training in terms of being in management. And that's what I did from a part-time perspective. And then from there I realized that I can work a midnight shift and they would provide me as an incentive to come on to midnight shift because nobody wants to work at midnight, was to provide you with a little bit more from a salary perspective and then they would give you tuition reimbursement.

[00:02:38] So let's talk about tuition reimbursement. This is 1990 to, I think I graduated in 2003.That's 13 years. So Hoffer University was about $8,000 a year. I think it's $4,000 a semester or something to that nature. So tuition reimbursement covered it. I ended up leaving when I was done with my education. I had $25,000 in student loans, which I was able to pay off quickly. That's not like everybody. Now people come out with a hundred thousand in student loans. It's like a mortgage. And that's, that's pretty difficult. That's another conversation, right? How do you afford to get a house to get married when you have this big debt hanging over your head? So getting that management background under high intensity stress environment, because it is a high intense stress environment.

[00:03:25] If you ever seen that movie with FedEx, it's all about time. It's how quickly you get things out, don't damage packages, scan packages, and you have to work with people as well in the union and get them to do a great job. What did I learn there? I learned management, I learned how to work with people, how to motivate people without threatening them or, getting 'em in trouble. It's how do you motivate people without, putting any undue pressure under them because that doesn't work in that environment. So I got that under my belt. Especially working with a lot of executives there in terms of what their expectations were of a manager and to run a shift within a certain amount of time.

[00:04:01] So I understood management of people. I understood work within a certain timeframe, and you had to get it done within three hours. If not, you got a lot of pressure on you. So I learned that aspect of it, managing time, managing people, managing the environment, and managing my own stress. They put a tremendous amount of pressure on you to do that, to do that job. And I was able to control myself. Next step was actually my brother-in-law said, I'm working at, AT&T, let me get you an interview at AT&T because, he saw I'm working my butt off, right? I'm working nights, I'm going to school, I'm putting the time in. And he got me an interview at AT&T in sales.

[00:04:35] And I was at 26 years old, I got an interview there and they hired me. But that's not before I went through another challenge. Life's full of challenges. So I'll tell you, I was never in sales, obviously. I told you I was working at UPS in operations. I guess I was selling to union employees to get them to do a great job or to, incentivize them. But what they wanted me to do to get the job was, I had to leave UPS at six in the morning, had to drive to New Jersey to a hotel, and AT&T rented out two hotel rooms, and they did sales role playing. So they took, employees and said, this is a C F O and this is the CEO. And what you have to do, Kurt, is you have about a half an hour to read this script and you have to go walk in there and you have to go sell them.

[00:05:20] So I never sold in my life. There's so many things you don't know about. You don't know that you're supposed to read it. And how do you ask certain questions and how do you understand, the objections and how do you solution sell? It's not just pushing a product. It's how do you solution sell? So I went through that again and more extreme stress. Never been through it before and I passed. And that's how I got the job there. And then, my career started at AT&T and I spent the next 15 years there. And gradually moved up to when I was, when I left, I had five accounts that I was actively managing myself as a client business manager.

[00:05:53] It was BlackRock, which we all know who BlackRock is. It was Mediterranean Shipping Company, which is this, the largest cargo shipping company in the world. And I worked with them from 99 to 2013 and watched them grow when I was, basically the sole provider in the US for them. I worked at two law firms, Sherman and Sterling, Skadden, Arps. And, two white glove law firms, even white in case at one point in time. And then Atlas Copco is a, global mining and equipment, manufacturer out of Europe. So those are the companies I was in front of. And those are the companies I worked with throughout my career.

[00:06:25] Had a lot of sales accomplishments, top one, top 2%. And from there, I then chose to continue it by moving outside of AT&T and I started a company that was a partner of AT&T's. And then I eventually got into consulting. So as you can see, it wasn't an organic, where I knew since high school that I wanted to be in technology. I loved building computers. I loved, looking at things. I did not have that pathway. So for anybody that's listening, everybody has a different path in life. And mine got me to where I was because of my hard work, my dedication and the amount of time I put in some into my job. Somebody saw that. And provided me with an opportunity and I took it from there. 

[00:07:05] Ronald Skelton: It's interesting. We have a similar, almost parallel path, but in different, some aspects. I think we graduated high school about the same time I graduated in 90. And then, I spent two years thinking I was gonna save some money for college, working for my father and running a paint,I worked at a paint manufacturing company during the day, and then the afternoons and weekends we painted houses.

[00:07:24] And I got to realize my parents can't pay for my college and I'm not very good at saving money. I would pay for my friends and I, to do whatever fun, fun stuff we wanted to do. And most of the time it was, parties and doing stupid stuff. That said, I had figured out I had, I wanted to go to college. I didn't wanna do manual labor anymore, and the military seemed like a viable option. So I joined the Air Force. They don't tell you when you join the military and for anybody listening, thinking you're gonna follow my path and join the military to go to college, understand you gotta do a couple years of their work first.

[00:07:53] You can't even start going to college until you've done their training. Like I was military intelligence, satellite imagery. My training school was six or eight months, it was a long time. And then you got OJT. When you get on site, you have to get to a certain skill level before they can let you go to college. I was at my second duty station. I was stationed in Hawaii before I was allowed to even take my first class. And, unlike you on the 13 years, I'm a glutton for punishment. I took the night shift like you did, and they had tuition assistance. They would pay, I think like 75% of all my tuition. No matter how much I took, they pay 75% of it.

[00:08:26] I had to come up with the other 25%. And, I did everything I could do. Then mathematically I could finish my bachelor's degree before I had to reenlist. Which means I needed to do a four year degree in about just under two and a half years. And not only did I do it, I did a dual major. I had to switch my major, of course I was gonna do mathematics as once and I ended up taking a calculus and linear algebra and something else in the same quarter.

[00:08:49] Ended up with management technology stuff. But I got out in the technology center and I didn't enter computers cause I liked them. I don't know if I've ever liked computers, but, I was just happened to be really good at 'em. So when I got to any duty station, even though I was trained as a satellite imagery guy, they realize how good I was at computers, and it's really hard to get somebody into the rooms where I was in with the top secret level clearances that we had, to be able to fix a computer.

[00:09:15] There's top secret and there's caveats laid on top of it. There's little code words that go on top of it. We had some of the really highest ones, so basically we could wait a week for someone to find somebody cleared enough to be in the room to work on our computer. I could just fix it. I got my college degree in Hawaii. And when I got out of the military, I went to work for Lockheed Martin, designing firewalls and redesigning the, YouTube mission, the YouTube spy planes mission planning system and stuff. So I'm just setting the premise there that I have background in technology. Even if you look up my most recent college degrees, I got burned out in it.

[00:09:47] So I went and got a master's degree in MBA in marketing and did everything from running real estate investment firms to buying and selling businesses now. But, it was mostly because computers, or like I told you before the show, I think computers are so logical that, usually it's the person behind the keyboard or the guy that wrote the software that messed something up. And on the marketing side and the human nature of business, it's so fascinating cuz it's never the same problem twice. And you can give a human being the step-by-step procedure to make a billion dollars and showing 50 people have done it and they'll wanna do it their own way anyway. So human psychology has always been more interesting to me than, computer logic.

[00:10:26] And that's how I ended up where I'm at. Now we're kind, we're hitting it in the world where, there's actually almost a thing that's gonna be called like computer psychology, right? This AI thing will actually have its own, especially when it gets general intelligence level. It's gonna have its own, way of being, I guess is the word I'm looking for. And majority of that, the parameters for that are set by the programmers. But is it really, like in the long run can it develop its own, Intelligence on top of what we create for it. So let's jump into how, what technologies are out there right now? How does that impact mergers and acquisitions?

[00:11:07] And then we'll talk about how AI is gonna change all that. Explain your role in, right now in technology. Currently in technology and mergers and acquisitions, private equity and stuff. And we'll go from there. 

[00:11:18] Kurt Stein: Sure, sure. Probably a good way to do it too is to baseline or to level set, is to talk a little bit about what everybody knows about from an AI perspective.

[00:11:27] Artificial intelligence. and I like to explain it this way. I think it's a, it's better when it's a storytelling and people can understand the journey and then people have the aha moment. So let's take AI for instance. AI has been around for a long time. You know that, I know that. There's been a lot of companies utilizing it. The government's media utilizing it for a long period of time. But the general public itself, while they may have heard of it, when you're not exposed to it all the time, it's that saying of out of sight, out of mind. ChatGPT put it into everybody's minds and hands. And how'd they do it? It's no different than the iPhone right?

[00:12:03] Take an iPhone, put it in their hands of a two year old or an iPad, and they figure out how to use it quickly because it is so easy to use. So ChatGPT started in around 2019 or something like that. You have open AI as the company, which everybody's aware of, and ChatGPT being the AI model. So open AI scrapes the web. It gathers all the information out there and wherever it can find it, it scrapes it, it pulls it in, and then ChatGPT is the model itself. So the first release was in 2022, ChatGPT three. And that's when everybody saw it. So what were they doing before that? They were testing it. They were tweaking it. They were enhancing it. They were making sure the results were right. They probably had beta testing. They were looking at how is this working?

[00:12:47] How is this performing? Is the information a good, is the information good? Probably no different than when a child learns to walk. You first get up, you fall down, it doesn't look good. You fall, you roll over. It's like, it doesn't look good what you're doing. But eventually when everybody sees the baby the next time it's running around. Nobody saw her fall down. Almost hitting its head, given black and blues, whatever the case may be. That was ChatGPT. So it gets released in 2022, November. The interface is simple. It's just a line. You just type in there something, and it's instantaneous in terms of the response. Man, it got a hundred million users in two months.

[00:13:22] Outpaced TikTok, was nine months. Outpaced Instagram years, Twitter, et cetera. Now you look at threads. I don't know how quickly it's gonna get to a hundred million, but it looks like it's on pace and moving quickly. 

[00:13:31] Ronald Skelton: Passed it already. Yeah, they did it in I think, less than a week. 

[00:13:35] Kurt Stein: So that, that goes to show how all of us now our craving technology, we're craving it. People may say they fear it, but they crave it as well. So ChatGPT burst on the scene. Now it's ChatGPT four. You could type something in there, you get an instantaneous response. And the responses are pretty good. Could there be bias in there? Yes.

[00:13:55] Could there be malicious things in there? Yes. We've talked about that. A couple of hacks, et cetera. But that's where you baseline it. That's where now everybody sees that. Everybody could see what it could provide, and now the light bulbs go off. What can we do? What can we do with AI? And now you're seeing so many companies and industries making such vast, vast,investments into AI. You got Bloomberg, G P T, 50 billion, I think they put into it. You have Microsoft at Open ai. I think you have a $25 billion investment. You have PWC making billion dollar investments. K P M G, Ernst and Young, Accenture.

[00:14:29] The list goes on and on. MasterCard,insurance companies. Everybody's investing in it because they're saying, how do we take advantage of this? And it goes by the saying, which I like to bring up, it's the one from Mark Randolph, one of the founders of Netflix. He posted this on, on Instagram probably a couple months ago. And he said the following, very simple to understand. Either you are willing to disrupt your business or somebody else will. So it comes down to innovation, right? We constantly have to innovate. We constantly have to come up with new ideas. We constantly have to come out with something different. And then you see your competitors out there taking advantage of AI, this GPT that everybody's aware of, and they're innovating it into their business.

[00:15:07] And then what are their end customers believe in? What are their end customers thinking? Have they increased productivity? Have they created revenue? New revenue generation? Have they provided cost savings to their business? What have they done now that could possibly disrupt your business because you haven't done it yourself? So that's when a baseline, that's where it begins. 

[00:15:26] Ronald Skelton: It's interesting that a lot of people don't realize that. Like you said, it's been around a lot longer. I remember I graduated in 2007 or six, I can't remember. I know shortly after that we were hearing about Watson, which was an IBM AI that could, basically they would do things like legal medical research.

[00:15:47] You had to be a big corporation. You would pay to get access and compute cycles. You could feed it data, it would help you answer things. It's been around for a long time. And then you talk about your client being BlackRock. BlackRock's has one of the oldest and most powerful AI's in the world. Everybody's worried about AI will take over the world. And I jokingly say it already has, you just don't know it. BlackRock currently holds what, $3 trillion worth of physical assets or hard assets. 

[00:16:13] Kurt Stein: I think they have investments, it was nine or 10 trillion. It just. 

[00:16:15] Ronald Skelton: Nine or 10 trillion. Yeah, nine or 10 trillion. Yeah. They have their hands on anything. They just bought, ancestry.com, which actually, owns 23andme and, lemon something medical. So they have all that data. So they're buying these companies up that have huge amounts of data and it's like, well, why do I have the company, why do they want companies that hold all this data?

[00:16:33] It's because of their AI, Aladdin. They've owned that for a long time. And this backstory to that is the original founder actually lost one of his first jobs and actually really had a bad experience with technology. Was using technology to make financial decisions and it went wrong. And, I think it was like 25, 30 years ago, and he said never again. He just focused on creating a system that could give him logical, leads into making correct decisions. And they've been working on it for, what, 30 years now, they said. And a lot of people don't realize how powerful that tool is. You can't be a top end stock brokerage firm without subscribing to Aladdin.

[00:17:08] You can't be a top, insurance provider and and not have your actuaries subscribed to the Aladdin system. It's just outperforms them and they can't compete without it. Anybody out there's AI's gonna choke over the world. It kinda already has and it hasn't heard us yet. Aladdin's probably the single handedly commercial side. They're probably something, NSA probably has something better. A government agency out there probably has something better. But hands down, as far as the commercial one, it's probably the biggest, baddest one I've ever heard of. 

[00:17:37] Kurt Stein: Yeah. Now IBM named it Watson X. That's her AI side. And yes look, I come from the school of thought of AI and people working together. It was where you'll get the best. Best of both worlds. Is there that intelligent AI? Yes. It's absolutely possible? Yes. Everybody's saying that could possibly happen. Absolutely. 

[00:17:53] I think Terminator, I think Matrix. I think of all these things, right? Which is not beyond the realm of possibilities. You just hope that, governments, they'll do something right, which is take a look at that and regulate and make sure things don't get outta control. 

[00:18:06] Ronald Skelton: I think everybody that sit on those congressional hearings where the guys from Open AI and everything were telling them about, the threats of, of artificial intelligence and why they ought to regulate it. Should be forced to set through the Terminator series again and Matrix and some other stuff. Cuz it has another meaning once you've watched it after you're into this world and you know what AI's capable of. To watch those shows again, you're like, wait a second. This is not too far in the future of possibilities. 

[00:18:32] Kurt Stein: Or it's already happening. Just don't tell us. The kind of idea I think of as well is, there was a post that was done, or a poll that was done. The magazine, somebody posted it on LinkedIn where it was, do you wantAI to become intelligent? And I think it was like 64% of the people said, sure, yeah, we want to go sentient. And my response was, wait, what? Who would want that? Who would want it to go sentient? Sentient in a sense that AI would think and doesn't need people anymore.

[00:18:59] It does it itself. It's like us, but it just quicker and faster. And if you take some of those, what could be, like take a, take an example of humans wanting to put their lives at risk to save somebody else. That's the courage or, the drive that we have. It's a situation where somebody could die. There's a possibility that we could die, but we're still gonna go for it. Whether it be our dog, our child, our loved one, or somebody that you just feel that. AI would look at that, or a human eye robot would look at it and say, there's a 40% chance that you won't survive, but there's a hundred percent chance that person won't survive.

[00:19:36] Therefore, instead of losing two, it's good to just lose one. We're not gonna allow you to do anything. 

[00:19:42] Ronald Skelton: Yeah. It's even,there's a also the potential that's, being sentient. The biggest, one of the biggest threats I see is how fast would it basically develop its own language and not need us for anything, right?

[00:19:54] If it's truly more intelligent than us and it can grow and learn on its own, even if it never harmed us, there's still a high probability it would develop, they've already seen it develop languages of it's own and, and different tools out there. Before, it's talking to each other, doing stuff on its own and just basically quits responding to our prompts. We provide no value anymore. That's the best case scenario cuz it doesn't harm us and it doesn't help us. Worst case scenario is that, it really looks at our track, human track record on the earth. We don't have a great track record, right? We've destroyed things, we've wasted resources, we've hurt people, we've had wars, all this stuff.

[00:20:28] If you really looked at this from a third party point of view and are humans good for the existence of earth? I don't know anybody that logically could say, without coming in flowery and go love and, and relationships and stuff like that matter, logically we just haven't had a great track record. So I don't think it would play in our favor to create something that was intelligent enough to see that and go. These things that program here just, they're just not very good. 

[00:20:55] Kurt Stein: Yeah. Look, it's good to talk about it. There's always a possibility, just like nuclear technology.

[00:20:59] Nuclear technology has some benefits, but it has some extreme uses. Weapons have some good uses and they have extreme uses. Vehicles, right? Cars. And you look at technology, what has ATMs done? ATMs reduce the need for a lot of tellers, right? Everything adapts. Everything,manifests and is developed. And there are points where it can be impacting to people into jobs. I know a lot of people think about that. And yes, that is the extreme cuz everybody's been talking about that and hopefully they put some restrictions in place and some maybe governors or fail safes, whatever you may speak of.

[00:21:34] Good thing is we're not working on that stuff. That's other people working on some of that intelligent. I guess they get a little ahead of themselves, right? They wanna do something else. But I believe there are still some great things to use AI for that can, provide a great benefit to businesses. Especially if they take advantage of it right now.

[00:21:51] Ronald Skelton: So let's talk about mergers and acquisitions. Let's tie this together. Where do you see the low hanging fruit for AI to come in and make a significant difference? In the way that mergers and acquisitions occur and, are successful. 

[00:22:05] Kurt Stein: Yep. So, where AI right now is, at least right now, is where it plays the biggest role, is taking vast amount of information, correlating in that and providing a response instantaneously.

[00:22:17] So in mergers and acquisitions, if you take a look at it from a financial perspective, you have to gather a lot of financial reports, financial statements, information, bank accounts, et cetera. And then armies of people have to sit there and they have to go through it, right? And they're brilliant, but they have to go through it. And there's only so much time that you have to go through things and you keep throwing more bodies at it. But from an AI perspective, again, if you think about the example I gave before OpenAI scrapes the web for information. AI is the output. So AI could be used from that perspective. You take vast amount of that information.

[00:22:51] You create a model that says, here's the output or what we're looking for. It scrapes it, it provides the output, and that's when we get involved. The CPAs, the accountants, the teams in M and A, the lawyers, and they look at things and that's when they can interpret the information that's provided to them and they can go through it much quicker. So from an information standpoint, that's gonna make a lot of sense. I was on a, a call early today from Financial Times, was speaking in the legal space about where AI, generative AI can make a, an impact there. And they actually asked in the m and a space, it was like 8%, the people in the poll said. That's where it really provide a lot of response. 

[00:23:30] But overwhelming 55, 56% said on the contract reviews and contract generation on the contractual side, generative AI from a legal perspective, they felt would have the greatest impact. Which again, that's part of m and a, right? Looking at contracts and correlating and spitting out information that would take lawyers to look through over a long period of time.

[00:23:51] Ronald Skelton: I think it's more than that. If you look at what true due diligence would be inside of the, like the legal side. Looking through all 50 states and internationally, anywhere that business has done business to see if there's any pending court cases, anything like that against the person.

[00:24:05] Very time consuming,very resource consuming. Looking through, current and past regulations, right? Regulatory compliance, all the different stuff. I think the AI could do that. And maybe, I use AI regularly, probably on almost daily in my work now, I use, ChatGPT and a few other tools, about four tools I pull together. What I've learned is I like to tell that the AI, here's a set of data, here's a set of data. What questions do you have of me to do X, Y, and Z? Cuz I don't assume that it has everything. I give it all the data I need to give it. And then I say, ask any questions you need to produce.

[00:24:39] And then I give it, a result I'm looking to produce. And it's always, even though after I've given it two decent sets of data, it's already got, always got something, right? In order to make the best, and it's good at that. I think the same thing goes with financial due diligence and with like legal due diligence, you could say, here's everything I got, my favorite thing to do in every, at the end of every meeting, I was like, what did we do well, right? What could we do better and what are we missing? You ask the AI, is this good data? What questions do you still have based off the data I've given you?

[00:25:07] And what are we missing altogether? And if you fill all that in, then I think it can absolutely speed the process up. A lot of due diligence can take, thirty, sixty, ninety, a hundred eighty days or more, and you could basically get that down to days if, and I think the third part of that, we talk about financial and legal, is if somebody, this might be a little risky, but I automate a lot of my job on a regular basis. Mainly cuz I'm inherently lazy. I hate doing the same thing over and over again. So if I had to do the same test procedure inside and out, I usually wrote like code or software to automate my job and then I could just push a button.

[00:25:44] If I had to run the same test over and over again, I would just push a button and, they have the keyboard trackers. Basically I use a keyboard tracker to track all the input, and I would turn around and play it back through a, basically a simulation to simulate me doing my job. And then I could show somebody I can do this a thousand times. It still don't break. I think the same thing goes inside of AI and technology. There's a set of tools already out there for checking the security of sites and stuff. You could do a full tech walkthrough, current users, like there's things you just gotta do in a tech due diligence.

[00:26:16] What are the current users on all the systems? Are they still employed? What's the tech stack? How much of it's outdated? What bugs are in the tech stack? A lot of the stuff you would do in a technology due diligence. That too could be done by AI, I think. With some trust level that you just let the,a computer system smarter than you call through the security of your entire business.

[00:26:34] Kurt Stein: What's important too is that, again, take that example of OpenAI, where it's, it scrapes the web. In businesses themselves you don't have to allow AI to see certain parts of your business. Like anybody wants to use ChatGPT today, they're pretty clear. Don't put personal information in there.

[00:26:50] Don't put bank accounts in there. Don't say, Hey, take a look at my financial accounts, and tell me what you know, is there a better strategy? There's certain things that have never changed. You don't provide personal information out. Social security numbers, things of that nature, because that becomes public in the public domain. The same thing here is if you don't want access to certain systems, then you should block that out where it doesn't have access to that information. Maybe you don't want it looking at employee information. Maybe you do, maybe you want to look at employees and find, hey, where are their skillsets?

[00:27:18] Where are we blind? And what our employees can provide to us from a skillset perspective. Or, don't look at salaries. Where are we from a salary perspective? Or maybe you don't want to know that. But certainly anything from a technology due diligence you could do on deal sourcing. There's a lot of, I see a lot of people out there saying they spend a lot of time looking for deals, speaking to companies, trying to find the diamonds in the rough or the needle in the haystack. What about from a deal sourcing perspective? Where AI is out there scrolling public and looking at what's happening out there. 

[00:27:47] Certain criteria on types of customers or companies you're looking for. Where are they in a domain? Are they saying a lot of things on social media? Well you can start narrowing down a group of what you're looking for. Again, I look at it as a productivity enhancement. How do you cut down and collapse the amount of time that you're working on something to your benefit? Go back to that, that due diligence. So now take, you've been working due diligence six to eight months. What about the information you pulled eight months ago? Or six months ago? Or three months ago?

[00:28:14] Is that still relevant information? Or have you already built in automation to keep pulling the next information? You're constantly updating that report and you constantly have to remake a decision based on information that's constantly changing. And I think that's one of the areas that are challenging as well. You have to either move quickly or you have to get that information quicker. And that's where AI can help. 

[00:28:34] Ronald Skelton: I think there's also a potential out there for, like you were talking about, not wanting AI to see certain data and stuff. I know for a fact there are facilities where, when I would go to test our, we built a firewall system for government agencies.

[00:28:48] When I would go test it, when it was put at a particular facility, I couldn't take computers or anything with me. I had to take, my tools and list tools and stuff and build something there, because once a computer was connected to the inside of that particular skiff, it never got to leave. Not without drilling holes to their hard drive and, decaling everything. It just, it was permanently part of that facility. So I think you could do the same thing with standalone AI. A lot of people don't understand that the data set that these things need while being huge for broad topics like ChatGPT, that has to know everything. If you built one that only did financial due diligence, my phone holds a terabyte.

[00:29:25] My computer setting here has two, two terabyte of it. I don't know what the, I think the entire Library of Congress fits on that or a quarter of that, right? You could store a vast amount of knowledge in a particular, defined subject on your average device and have enough computer,computing power to crawl through it and make it worthwhile. I think that somebody could bring a standalone device into a system, tell you whether or not his financial due diligence is fine and stuff, and then pretty much, at the end of the report, if you're really worried about stuff getting out there or being, open access, it just becomes part of that system.

[00:29:58] Kurt Stein: That's right. So take the definition of large language model which is, which NRA AI is For a company go build that, it takes a lot. Like you said a little bit earlier, you have to get that database of information. They're using the worldwide web. That's why Bloomberg uses ChatGPT because they have an enormous, vast amount of information.

[00:30:13] Why go build it yourself? It takes a lot of time, et cetera. So that's why they use that. But you're right, you can build an AI model for anything you want. So what we work with clients on is the following. We first look at your data lake. First, actually we first looked at your IT infrastructure you said before. Can you implement AI into your business? Are your systems correct? Do the firewalls there? What part do you wanna partition out? Are you ready for AI? That's the first part you look at. The second part you look at is that data lake. So that data lake is your information.

[00:30:40] Where does it reside? Is it on hard drives, right? Is it mobility? Is in ERP? Is it in CRM systems? It is in financial systems. Where is the repository of the data that you want to utilize? So if you are a company that in financial, where's all the financial information for all your clients? Are you housing it? Do you have it in a data center? Do you have it in the cloud? What's your security posture for end client information? How are you protecting it? Do you want it accessed by AI? And then is it structured? Is it tagged? Is it labeled, right? So if you take it from that perspective, anybody here's not familiar with it.

[00:31:14] Take the example analogy of an Excel spreadsheet. If you took your company and threw it onto an Excel spreadsheet and said, here's my company, here's all my numbers. People would say, I don't know what that means. But once you tag and label or structure it, that's called putting columns, putting rows, labeling it, tagging it, saying, this is what the performance is. Here's, last year, this year. Now that data makes sense. That's when AI is able to take that data and provide you with a response. So companies like that would say, you look at your infrastructure, is it ready? Look at your data lake. What do you want to use? Where is it? Where does it reside? Can we get to it?

[00:31:50] Is it structured or not? And then what are you looking to do? Do you want cost savings? You wanna go into the defensive position? I wanna cut costs. I think I'm doing something that I shouldn't be doing. I could do it much better and much quicker. Do you want revenue generation? I want a new product and service. So take a private equity firm that says, Hey, by the way, we just built an AI tool with D C T strategy. And now anytime we do due diligence, we just gather all the information and we input it into our tool. It spits out a response for us. And here we go. Now our, our analysts look at everything and say, great, this is a good deal, or not a good deal.

[00:32:23] And we could do that in maybe seven days instead of doing it in a couple of months. So we give different options in terms of AI models. Do you want revenue generation? Do you want, productivity enhancements? Do you want your team to be able to get that information quicker? And then the last thing is, here's the roadmap to do it. Here's how you go from point A, to point B or point A to point Z. And this is how you get there. So that's how I look at things. And you're right, you can do it from a very specific laser focused area, deal optimization, deal sourcing, m and a, IT due diligence, due diligence, right?

[00:32:56] There's so many different areas that you can apply it to. And you can keep the scope narrow in terms of what you do. And then, If you get better, the model keeps getting better. Maybe you decide that you wanna get a little bit bigger. 

[00:33:08] Ronald Skelton: Well, you do the hybrid model, right? Because you can actually train things like ChatGPT and stuff. The trick is, I'm not sure on this one, can you train it? Use the general model and use the general language model, but keep your data separate, right? Like I say, I'm gonna train you on, here's some data I want you to use for this decision, right? And you have access to all this, but you can't incorporate it into anything else.

[00:33:30] I don't know that it's true. And that's why they're telling you not to put your private information in there. But, at some point I think that there'll be models out there. We can do that with here's my data, I want you to use it, but use your, everything you know too as far as the worldly data. And never tell, show the two intermix. 

[00:37:06] Kurt Stein: We could sit there and say their APIs are fine. You could tell them not to look at, that private, information and everybody says it's great, we won't do it. But understanding any CIO that listens to us says, great, you know what?

[00:33:57] I'm gonna trust but verify. Or I'm not gonna do it because there's an option, it could happen. So in that sense, even if people said it wasn't possible, you're gonna carve it out and say, I'm not gonna allow this to be touched because that is my IP. That's the heart of my business. And if that information is accessed or becomes public, that's the end of our company. And nobody, I'm sure nobody wants to be on that side of the table having that conversation.

[00:34:19] Yeah, I haven't heard of AI wrecking a company yet, but it's bound to happen. There'll be one out there that somebody implemented it, it did something, and now it's destroyed. And there's plenty of tools too that, that would take information like that and make it, remember in the software side, you do a pre-production environment where you test everything out.

[00:34:36] You do the same thing in AI. There's a lot of different companies out there to gather the information, pull the information in. Make it much quicker for our data scientists to now work on the models and train the models and improve the models. There's a lot of things to be done out there. And you could do it in a pre-production environment without anything happening. And then when you release it, it doesn't, it's not like you just let it go and, okay, we're never gonna touch it before. You can't do that. Our team still stays involved and is looking at it. Again, remember the example in the beginning ChatGPT three was released.

[00:35:06] Before they improved the model, multiple iterations. Three came out and then they released four. And each iteration has only improved because they tested it, they improved it, they made tweaks to it, and they made sure that the information was getting better and better. And the same thing would happen in those businesses as well.

[00:35:23] Ronald Skelton: Yeah. So how does that come into play? We're looking at the world of private equity buying companies. Looking at tech stacks and figuring out, like if I bought a company right now that it was, say, SaaS enabled or a SaaS based company. And I'm purchasing something I want to be around for the next five to 10 years.

[00:35:43] I honestly think that this, AI has to be a consideration into this. How does AI play a role in the future of this company? How does it make it better? What are the,the SWOT analysis, right? Strength, witness, opportunities and threat. You almost need a full SWOT on how AI impacts any type of technology, based company. What's your thought process on that as far as really taking a look at these companies that you're looking to acquire or you're looking to divest from? And know what the future impact could be. There's definitely. 

[00:36:13] Kurt Stein: Sure. Sure. I think like anything else, even from an analyst perspective, and I wasn't in an analyst role ever in a PE firm. 

[00:36:20] But, working with AT&T and the business I'm in, I understood SWOT, right? Understood total account management, when you look at all this information. And that's what an analyst would do from a number perspective. They're gonna look at, here's the state today. And here's what's gonna be in the future. And again, it's gonna be like anything else. It's gonna be modeling. So allow AI to do the modeling post, well, it's pre, pre- due diligence or pre-purchase. Post platform, when it's on the platform. You're now gonna have to look at that modeling and it's gonna take it, okay, based on what we see here or whatever we set up of what the criteria is of what we want to either perform, or the amount of clients we want to come on board.

[00:36:55] Or the customer experience or, whatever the factors may be for why they bought that company, the measurements, they can even use AI modeling to now continue to test that going forward. And I think what's important, again, I go back to humans and AI working together. We've said that from the beginning. I look at it as it's modeling to give you information so you could set it up where SaaS companies most likely today are using AI within their business, right? They're using it for customer experience and chat and things you're looking at there, modeling as well. But the PE firms can also use that for post acquisition modeling.

[00:37:31] Okay? We have our baseline of what it was, what we're expecting it to be, and what is the modeling telling us about this. But then human interactions involved saying, do we believe as an analyst that this is trending the way the model's saying it is? Or do we believe somebody information may not be as accurate? We have to tweak the model. Or you're right, the way things are going, this is what's going to happen now. Now you've been in technology, so I wanted to say this as well. You have Moore's Law, right? I think it's changed completely. Where it's not happening every four years. Now it's almost every 18 months. Maybe now it's getting down to a year. 

[00:38:09] Ronald Skelton: Maybe with AI it's scary because the difference between current learning models. Think about this, in human terms if I go learn something and then you wanna learn it, next, either you have to go back to the My Source or I have to sit here with you and we spend a few hours, and if it took me an hour to learn it, probably take me an hour to teach it, right?

[00:38:28] In the AI world, if you got a network of 15 computers, when one computer learns something, or they're all stumbling, like, they're learning this learning process, they're stumbling. The second one of it does it, they all have the answer. And they instantly know it. That would be like saying, if you connected to all of the human beings on the planet, 8 billion of us, seven point something, 9 billion of us, and we all have the knowledge of each other instantly, Imagine what could be done. Especially when you can start, not only do we have the knowledge of each other, we can start correlating and analyzing the different facts and the different points of view from everywhere, all at once. It's incredible to speed, I think Moore's law is out. Especially when we start talking about large scale models, where the different AI models that,right now we have the,large language models.

[00:39:11] We have Wolfram, which is a logic model. You start connecting those two and letting them work off each other and learn from each other. Both scary and exciting. I see a world where, business deals are, we have an AI crawling around there and says, Hey, and an email is company A and email is company B and say, you two guys should really merge. You got these synergies, here's how we do it. Your public records say that your financials are fine. Their public records say that their financials are fine. I've looked at your audited, cuz if they're publicly traded, they have audited, returns, occasionally.

[00:39:41] I've seen your audited books. I've seen their audited books. I know you may not see yourself with all these synergies, but I could show you a model where you can increase your revenue by 1.5% over the top that the two you added together and it just it's, the technology's already there. It's just we haven't given it the, the wherewithal or I guess at the, the authority to say, Hey, crawl the world and see, and make suggestions, right? 

[00:40:03] Kurt Stein: Yeah, I have, so I have two comments on that. On the second one, Citadel is hiring interns right now from an AI perspective and paying them $19,500 per month. That's enormous for an intern. Which everybody knew the interns in the past were probably not paid as that much money. 

[00:40:20] And the idea was to get into there and possibly be on a role. So Citadel has released that and they're trying to do, because they obviously see a benefit there. And they most likely will be putting models together. So, going back to your point now about how would it be used AI for when a company gets purchased, maybe SaaS company, et cetera, looking in the future. If Moore's Law is out and look, people can argue that all over, but if AI is innovating so quickly, how can Moore's Law possibly apply anymore? So now look at the future. If you need, you're going to need AI now to constantly look at your model and providing constant up-to-date information on how is that business trending? How is it tracking? Where's the inputs coming from social media? Where's the inputs coming from customers? What's the customer experience?

[00:41:06] Have the customer sentiments changed? Are we seeing like, how are we predicting what's happening in the future from a sentiment perspective? Cuz that might mean that customers are falling out of favor, right? Which is, think of Twitter and threads. Is anything tracking or trending on customer sentiments and where people might wanna go? So I see PE firms having to have models where that pro, that post acquisition model is gonna have to constantly be modeling out, what they believe is gonna happen next. And then the analysts are going to have to sit there and look at it and say, based on everything I'm seeing as well, Is this accurate?

[00:41:43] Are we gonna need to change this technology? Is this gonna become obsolete? Do we need to pivot? Because what we thought was gonna happen is not gonna happen because threads came along. Or the iPhone's been innovated away and now something else is out there. So those are the two points I wanna make. It's kind of interesting. 

[00:42:02] Ronald Skelton: I smiled when you said that because there are CEOs out there making decisions that clearly they had analysts that told them it was a bad decision. The one that comes to my mind, and I don't pick on anybody, I love everybody, but Bud Lights the decision and what they did that just knocked them out of, they're not even listed in the top 10 of beer cells anymore, for their Bud Light can.

[00:42:21] I don't care how many AIs would've told them that was a bad decision. Somebody made a, a bad judgment call. I still think so long as companies have leaders in place that wanna make emotional based decisions instead of logic based decisions. we're still gonna have problems where bud Lights customer base was probably 75% dumb redneck guys like me that, I don't even drink beer, but, grew up in the country and did that, and that was just the cheap beer that we happened to like. And now you put something offensive to that particular customer base, whether it's right, wrong, or indifferent, that it's defensive. And, I don't think any logic in the world would've convinced that company not to do it. They were making a statement and now they're paying for it.

[00:43:04] Maybe AI can give, companies more guidance and decision making and customer sentiment and stuff like that. But, there's always gonna be the case where, human psychology overrules all logic. 

[00:43:16] Kurt Stein: Sure, sure. So you use it for the technology it is, right? It's a tool. So it's no different than a shovel. Do you need the shovel or do you need a hammer or do you need an axe or a sledgehammer? You have all these tools. So AI is the tool, if in my mind, as you use that to gather vast amount of information and to cut down the amount of time that you do something.

[00:43:33] Not to eliminate analysts, not to eliminate all these people that have gut feelings as well. I believe in gut feelings. Gut feelings is your worldly experience speaking to you pretty quickly, which is similar to sentient AI, right? It's your own. You are the sentient AI. Where you able to make decisions off that. And there are some CEOs or maybe a lot of CEOs, that still can make that decision like Steve Jobs. Would AI have told him to take that risk and to develop, these iPhones? I don't know. But you still have to have that vision. And then does AI allow you to provide you the inputs back, the information?

[00:44:10] Does it clear out the fog of war? Does it clear out all the emotional issues? Does it give you facts in order to make more logical based decisions that'll be better for your business? I believe the answer is yes. But you also have to make sure that your AI model is trained and tested and the information you're feeding it is good. If the information's bad, you're gonna get a bad result. 

[00:44:35] Ronald Skelton: Have you seen the company called Dragon Net Websoft? It's a Chinese, major Chinese game company. They replaced their CEO with AI years ago. It's like, I sort of see what it says, how long ago it was. But they basically took the C-suite and, replaced it with artificial intelligence. AI is the CEO of, of Net Dragon Websoft, a major Chinese game company. Appears to go going well so far. They're outperforming their competitors, right? But they allow AI to make all the, vision and,top level decisions. 

[00:45:06] Kurt Stein: I'm sure. Since you're come from an intelligence standpoint, I'm sure you still look at some of the periodicals, et cetera that's going on.

[00:45:12] And China right now is advancing much quicker from an AI perspective. They want to be the AI hub. And then you have also the EU rules that are coming around AI. Now, again, people could debate that. There's some things in there make a lot of sense, but now you have a lot of countries saying, Hey, hold on a second. These EU rules are going to restrict our ability to become a leader from an AI perspective. So everybody wants to become that hub of technology, which drives advancement. Which drives investments. Which drives your economy. And you have China that is leading right now from an AI perspective, which has gotta give you pause in terms of what are they doing?

[00:45:48] And I know the US government is also, they've also said a little while ago, I think a year ago, they were pretty concerned with, where they are from a technology perspective. Cybersecurity, AI. They're racing to catch up. And I think they've even said somebody ex- CIOs in military said we're nowhere in the near there. It's gonna take us a very long time to even, to try to catch up. So it's interesting you brought that up.

[00:46:08] Ronald Skelton: I have no idea where, I told you earlier, I was military intelligence, but I got out of the military in 97. AI was, some professors dream, back then. Maybe NSA had some model they were playing with by then.

[00:46:21] I didn't get to play with those boys too often. That said, I have no idea where they're at now. And to be honest, the way that data is compartmentalized in the military, only the people that use it on a daily basis would ever have the need to know. So doesn't matter if you've got the top level clearance and you're a four star general. If you don't need it to do your daily job, they won't let you know. Like in my job in military intelligence as a satellite imagery guy, I only got to know the portions of the mission that applied to me getting my job done. A lot of times we were doing stuff or looking at images, picking targets on there, stuff like that. And all we knew was we were told to look at this area of the country and tell 'em how many planes were moved.

[00:47:00] Weren't allowed to know the rest of the data cuz it wasn't needed to do our job. There was an absolute need to know. So having people, make statements, they go that were way behind and stuff, it depends on if we're talking to the right people, right? There are probably people behind closed doors using systems that aren't open to everybody. I've got some relatives now in the space program, in intelligence and stuff. They can't tell me anything. I rarely even get to talk to 'em. That said, I'm sure there's some, I'm sure there's tools out there. I'm confident there's tools out there, right? Just knowing what I knew and what we were playing with when I was in, in the nineties and what was out in the public, and the satellite imagery world, the differences between what was known to exist and what really existed.

[00:47:41] I have to have some level of confidence to know, that people saying we're not there yet are probably just not in the right rooms and have a true need to get their job done on a daily basis with that info. 

[00:47:54] Kurt Stein: Yeah, it could have been politics too, right? So it was one of, one of the, the CIOs of the government that basically stepped down and said, we're just, we are so far behind.

[00:48:01] And I was speaking to some folks and who worked for the Department of Defense. I mentioned that, and they said, yeah, it's, yeah, we're racing to catch up in certain things. Who knows? I know from an AI perspective, China wants to lead the world in terms of what they're doing. So it makes sense. And I know some of EU countries like Italy is blocked from using ChatGPT. 

[00:48:17] Ronald Skelton: I think it was Russia. Putin actually said a few years ago, the first person that gets, to really win at AI wins the world, controls the world. Like he, so they've been looking at this for a long time too. That's something that came out way before ChatGPT came out. As like the first person who really dominates AI, dominates everything. 

[00:48:35] Kurt Stein: So now you have people talking about the FOMO, right? The fear of missing out. And I see a lot of that out there as well. I see people always worried about it.

[00:48:42] There's a saying, when it comes to vaccines or something like that. So you don't wanna be the first, you don't wanna be the last, right? But you wanna be somewhere in between. I guess it's the same thing with other things. Like you don't wanna be the first one to do something cuz that the trailblazing is never easy. You don't wanna be the last either, because everybody else has done it. Now, if you're a CEO or running a company, you now are playing catch up. You want to kind of be somewhere in the middle. And you're not the first right now, if you're implementing AI. You are moving toward the middle. So if anybody's looking at it, at least listen to this.

[00:49:11] Yes, we talked about the bad side of it, which it's possible. It's definitely possible no matter what we talk about. But that's not a reason to stay away from it because your competitors are innovating and they're putting it in there. 

[00:49:22] Ronald Skelton: Even if we stopped today, somebody else in another country would do it. And somebody's liable lead would be very, very far ahead. The underlying technology, this is open source, there's probably some really intelligent kid from, that got mad that didn't get into MIT working in this backyard that probably has a better intelligence model. 

[00:49:37] But,It's coming. I get that. I think you have something for us here.there's a strategy and readiness assessment or something that, if you're a company you wanna figure out if you're ready to implement some, AI or get your hands in there, you have something for them to start looking at and figuring out how that works for 'em.

[00:49:53] Kurt Stein: Yeah. So you got a couple of different folks, right? You have those that understand AI like us. And it's great, we understand it. I want you to build me this model. That's easy. I have a team of data scientists, that's what they do. They build unique models for people. ChatGPT is a standard. Here's what it is.

[00:50:07] Everybody can latch on with an API. You could do that in your business as well. But if you have a specific model you want to build, you're like, I understand it. I get it. I know I need it. I know what my competitors are doing. This is what I need to do. We're here for you. We can actually implement that for you. Sit down and discuss all the different options and how it, how you can do it and what's the timeframe. But then you have the other larger portion of CEOs that are aware of it. They know it's being done out there, but they're not sure where to start. They don't know what they should do. They don't know what AI models to use.

[00:50:40] They hear about this ChatGPT. Their kids might be telling 'em, their friends might be telling. They read it in the paper, but frankly, they're not sure what to do. But you know what? They know they have to do it. And that's where the strategy and readiness assessment comes into play. So we do it about one week. It's 30 hours. You could do it over one week or two week timeframe. What we do is, we come into the business and we do four things. One is, we look at their IT infrastructure, which I mentioned a little bit earlier. Are you ready for AI? Is there anything you need to do? You need to, again, secure certain systems that you don't want to have access to AI. 

[00:51:13] Is your infrastructure ready for it? Have you put so much on your infrastructure that you couldn't add anything else because there's nothing left? We have to look at what you have, and that would be part of it. We'll come back to you with what it looks like, what you need to do next. The second part of it is we're gonna look at your data lake. That's where all your data is residing. Call your data pool your, data repository. We wanna look at it and say, is it structured? Is it all in one place? Is it easily accessible? If it's not, we tell you what we have to do. We have to structure it. Think of that Excel spreadsheet. We have to label it. We have to tag it.

[00:51:46] We have to structure it. Because the model needs to pull information from there easily. Excellent information provides an excellent response. Poor information gives you a poor response. That's the second part. The third part is our team will talk about what AI models are possible. There are a million possibilities. When you're building something, you could do anything. You could do something from a cost savings perspective that 10 other companies do the same way, but they hit different areas, right? So it's a little bit slightly different of a model. So we'll talk to you about different models that are possible. Give you ideas and allow you to choose what makes most sense for you.

[00:52:23] And then we'll build a roadmap for you and say, in this timeframe, we could build the model based on everything we know, and this is the roadmap to get there. And we provide that assessment back to 'em in about two weeks and say, here's what it is. So what have we done for them? We've minimized risk. We've minimized them having to go spend a lot of money to go figure out something the hard way. We've told them what they need to do within their business in order to be ready for it. We've given them all the ideas and what they possibly can do, and they could tell us, Hey, we changed our mind. You allowed us to think of something else. We wanna do that. And we've given 'em the roadmap, no different than saying to a house, Hey, look, we'll do an entire landscaping layout for you.

[00:53:00] You can go buy this tree and that tree and place it there, and eventually you'll have exactly what you're looking for. And that's, it just simplifies things for them in terms of how they get it done. 

[00:53:10] Ronald Skelton: Okay, well, how do you want somebody to reach out to you? What would be the best way for people to contact you?

[00:53:14] Kurt Stein: On LinkedIn, it's Kurt R. Stein. You can reach out to me on DCT strategy, so www.dctstrategy.com. Or you can reach out to me at Kurt, k u r t@dctstrategy.com. I'm happy to have a conversation and talk to anybody about this. 

[00:53:28] Ronald Skelton: Man, this hour blew way fast. If somebody could remember two or three things for today's show, what would you want 'em to walk away remembering?

[00:53:35] Kurt Stein: That AI isn't scary. That your competitors are doing it. So either you disrupt your business or somebody else will. Number two is they have resources here. It doesn't have to be tough. We can help you with all the ideas and what you can or can't do within your business. And you can have a roadmap that gets you there.

[00:53:51] So you can plan it out. You can start small. You can just test things out and see how things work. But the bottom line is you are gonna get some benefits to your business, that are gonna create that blue ocean for you. Instead of being in that red sea where your competitors are destroying things, you can actually put yourself in a position that will provide a better customer experience and make it a better business for you.

[00:54:13] Ronald Skelton: Awesome. I appreciate you today. I hate to end the show right now. I'm thinking there's all kinds of things we could, I could talk to you for hours on this, this topic. But I think we, we got it pretty well tagged there. I think right now it's, Hey, this is an emerging field. It's mature enough. You really should look at this. And now's the time to make your play on this, cuz if you don't, you're probably gonna be left behind. 

[00:54:35] Kurt Stein: That's correct. I look happy to come back on again. We could always take the conversation a little further. 

[00:54:38] Ronald Skelton: Yeah. This might be something we need to talk about every few months, six months or so. Cuz I think in six months from now, the whole world's gonna look different in the world of AI. It's just changing so fast. We might actually have a whole different conversation by then. We'll call that a show. And I thank you today, Kurt, for being here and helping us out get, a primer,for what the AI world looks like for mergers and acquisitions.

[00:54:57] Kurt Stein: Thank you so much. Hope the, audience, learns a lot from there. 

[00:55:00] Ronald Skelton: Awesome. Cool. That's the show.