Decision Intelligence and AI with Priceline Co Founder Paul Breitenbach

Lauren Bedula 00:00 Welcome back to Building the Base, Lauren Bedula and Hondo Geurts here with today's guest, Paul Breitenbach. Paul has an incredible story that we're interested in digging into today, including as one of the founders of Priceline. I know I'm a big user of Priceline, and now founder of a company called R4 which is focused on AI, doing some work primarily on the commercial side, but also for the Department of Defense. So, we'll get into that story. So, Paul, thanks so much for joining us today. Paul Breitenbach It's great to be here with you. Hondo Geurts 00:36 So, Paul, once again, we really enjoyed this podcast because we get such a diverse set of guests here with all sorts of interesting stories. What's your story? How does a guy growing up in New Jersey wind up having the career you have? And now I'm getting in the national security community? Paul Breitenbach 00:56 Yeah, it’s great. The Yeah, I grew up in New Jersey, I was a musician as a kid. And, you know, I kind of thought I was got to school music originally, like a you realize after a couple weeks, okay, I really don't want to do this. I'm really good at it. I was the youngest union member local 399, in basically the middle of New Jersey. And so, I thought I was going to be my career. And then so I get to show up to my advisor when I show up at school, so I don't want to be in music. You know, how I got into Cornell was really because I was a phenomenal musician, right? So, they when they're trying to rebuild a wind ensemble, they're like, this is pretty cool. Is it mean is the director of the wind sample says, How do I tell you, I want somebody because I was not the guy to get into, you know, a top school like that. But so, I was very lucky. But so, my advisor was the chairman of the sociology department. This is I had no clue what I wanted to do. He's up this is perfect social on one. So, she wanted to just sign up, because he wanted to fill up his incredibly, you know, empty department with new kids. And so, I said, I'm really interested in this computer thing, like, you know, whenever it gets the big buck out, puts his glasses on, and he's flipping through. And he says, computer science 100 Don't take that. So, I knew nothing about it. And of course, I said, I want to go do this. So yeah, my story, the steps, I took the computer science 100 failed the first prelim, and the professor said, Go for oh my god, I've never gotten an F. And then it's a stick with the class, are we okay? So, to the second prelim felt like worse than the first prelim. And I gotta guys, I, you know, I got to withdraw from this class, I know, it's gonna be w, but I need your permission. Now, it's so bad. And he said, Okay, fine. I'll let you withdraw. Again, to me such a huge favor. He says, I'll let you withdraw. On one condition, fine. What's the condition you have to come to class every single day because you're my best student, all the other monkeys just repeat what I tell them. I actually go home and think about what you say. Of course, like, as a young kid, I'm like, 10, why are you failing? Right? How can I fail something that you think I'm so good at. So, it's just kind of a precursor to the, you know, my career as a tech guy. So, I'm a tech entrepreneur. I was very lucky, you know, to find my way to a great team with Jay Walker and the other early pricing crew. And you guys know this, it takes a team to win right culture. We had a great strategy, but culture eats strategy for lunch, we had an amazing group. And it's not much to debate about, it's just been a runaway home run 25 years later, you know, when the UN, I just feel like we're lucky. We're living in this world of the internet era. That was awesome. And, you know, this world with AI, you know, a long time ago, we said to ourselves, geez, we're making all this money and not really doing anything. Remember back then with the internet, computers are running, and this thing is making all this money, there's revenue billions of dollars. And what about all the other organizations that were left behind in this data? Math thing? Right? Because you know, the, one of the big secrets by President Shatner was cool and whatnot, but the in Dave's data and math, and, you know, being able to automate that, so we're just really lucky. So, about 10 to12 years ago, we second round, tried to get this AI thing, Dortch. And it's, it's really going incredibly well. Lauren Bedula 04:15 So, but that's a common story, tech entrepreneur for a long time and this led us into defense as we get to a little bit and 10 to 12 years ago to have that vision around artificial intelligence. You can't go 30 minutes in this town without hearing AI. What was it about AI that caught your attention as a founder, a tech entrepreneur, how did you know this was going to be the next big thing? Paul Breitenbach 04:33 So we were doing it before the call today, I felt being straight, like this whole concept of using data and math in real time in the you know, we had a very heretical view and we built into R4 something that people still have a hard time getting their heads around is, you know, people think of AI and it's just incredible amounts of bodies, and peep data scientists doing things manually, right. We built it with a goal in mind of the customer needs zero data scientist and that's a mic drop moment, right? Like, it's like, it's so like, What are you talking about? How can that be possibly true. But we knew this was gonna be such a complicated thing, the concept that if companies don't know how to build software, they know how to build technology that in that space, the idea that be able to get their arms around this super complex technology capability. It's like a nil thing. They there's no way they can do it. So, we wanted to make it so they can buy and not have to build. And so yeah, so after 1012 years of getting into work in that promise is now reality. And people say, Oh, my God, it's so visionary and like, well, so was it when you told people, I'm going to let people buy things with computers? Because in the beginning, before the internet, you had to explain to them in really simple terms, why this was going to be doable. Now. What is this internet? Like? How can they even see, but the world of AI, I can see why people talk about it, because a lot of people missed the internet era. Like, I think about everybody lost this banana thing that's for a bunch of kids, it doesn't matter to us, doesn't it? Not gonna affect our business? Well, I mean, come on, we can look back and say every industry was touched by the Internet and transformed, and now there's entire branches of it. And I think people missed that. I think nowadays, the reason why their fever is so high, because Oh, my God, I missed the last one. And, you know, there's a big opportunity now and the new one right to not miss the boat. Lauren Bedula 06:19 Now, correct me if I'm wrong, but I don't think of Priceline as a government contractor. Although you think I've heard a lot about the defense travel system. And I'm sure there is a lot of efficiency to be had there. But it sounds like now at our four you are doing work with the US government, particularly DOD, the national security community, how did that happen? Paul Breitenbach 06:39 So that's a cool sort of additional in your chapter, right? I mean, we're doing great and commercial member of the commercial guy. So, you know, retail, insurance, CPG, manufacturing, all that kind of stuff. We do great supply chain logistics. You know, and then, you know, folks from Senate Armed Services said, you know, they get to know what they're like, and we don't really understand exactly what you do. But if you doing really well, in the commercial, have you ever thought about defense? Of course, you didn't stop. And like, no, till you mentioned, it wasn't it was the last thing on my mind. And I look back that a couple of years to an animal's three years ago, now, I'm really grateful that they suggested that and it’s you guys in defense? No, it's not for the faint of heart. It takes a lot of grinds, and a lot of patience, and a lot of, you know, whatever, perseverance, but, but we're doing some really, really amazing things to, I like to think to some of that, because we're gonna help out tech, the adversary. And, you know, the, like, at the end of the day, he was you learn quickly at the end, to provide a life we get, we got to do it the American way, we got to do it the innovative way. And a lot of times, I think this concept that defense can learn from commercial of things that we do so naturally, they're like, Oh, my God, can you do that? Yes. 100%. And so that's, it's been a really, really cool additional chapter to our story. Hondo Geurts 08:02 So, I think, you know, the challenge when you get the new buzzword in the Pentagon, or in, in DC, is everybody uses a buzzword. And I sense, you know, AI and machine learning automation all get kind of glommed together. What's your sense on the market? How's that market changed, even in 10 years since you started this new company? And how do you see it impacting, I would say, you know, from frontline to back office, you know, because it all kind of gets glommed together and overgeneralize. And then it's really hard to understand truth from myth. Paul Breitenbach 08:39 And I tend to like to simplify everything because the world I come from, it's so complicated, it's easy to get wrapped around, I like to think of this AI thing is, like, if you look at all technology, evolutions, like this thing before they were dated, you know, before they were an Oracle, I had to build and do this. Before there was CRM, like, before there were Salesforce, I had to build my own CRM. And same thing for ERP, you know, with the government standardize on SAP, right? So, there was there's always this idea that I got to build something before there's a solution I can buy, right? And so, you know, the sort of phases of this it follows now no difference as a human as a human society is the evolution of this technology. The first phase is built. So, what does that mean? We aren't around to doing that they're you know; they're supposed to try and throw to create software. They've had to go through the build process for usually hundreds 1000s of people. It's really complicated, hard to get it to scale hard to get it to connect and but it's no different than any of these other phases that we've all watched and said, Oh my God, that makes it good. It can sense that I got to build impressive but whether it was a cookie, we had to build a set we built this thing called P session. So, like, you know the website we'd stick this little thing because it was the system couldn't figure out without that he's the same person. So, you put in New York to LA and then all sudden were we treating you like somebody else? So, you know, the technology is it evolves, they know, technology can provide solutions. So, first phase of AI is built, right and body shops toolkits, you know, ungodly mass of people. And, you know, phase two, in simplifying this right, the chat GPT. Like the thing I love, everybody confused as chat GPT, enter a large language model is AI raise kids, it's a really important, but really, you know, it's a small portion of what the world of AI is. But what's cool about it, is the first time that the rest of the planet, that's not a data scientist around the space can say, Oh, my God, this is doing something sort of like AI and I didn't need a computer data science person to do it. And what's nice is the user interface is you can relate, you know, it kind of feels like I can understand that, right? And then, you know, I believe in this third phase, right, which is where we started, right is decision intelligence, is how do I predict what's going to happen? How do I understand by using, you know, just like we human beings use an ungodly amount of data automatically to try to then shorten the decision cycle, right? Whether it's, you know, what supply chain items need to be put in a spot so that you're ready for the bullets, the fuel that whatever parts and pieces you need to keep a plane flying? Or whether it's, you know, the person matching to what role, like, what do I need in order to be ahead like from a talent management point of view. So, I believe in this world of AI, that's it's not really the large language version of it. I love that, I believe in this idea of decision intelligence, all about predictions is about figuring out what's going to happen. Because if you can figure out what's going to happen, you could do something today, to take advantage of the fact that you can see that it's sort of like in the land of the blind, the one-eyed man is king. And so that's the version that's the phase three, I think that's where the trillions of dollars unlock will happen in commercial end and in public sector. I think if we are bold enough for a moment to think about how the government can do more with less How do I just optimize how do i in all aspects just deliver a better improved service experience at a lower cost? And by the way, or for that was named by our head of data science. He was with me at the game press on its terrible, right product, right consumer right time, right price, but it's us to the fourth, meaning that if I can be right on these dimensions, revenues can go up while Costco now. And when you think about that, as an analogy, it's very, I think it's very American, right? It's the idea, I'm going to, I'm going to, you know, innovate, I'm going to do something different rather than just try to do the war like, like our adversaries, why this body shops because I got more body, so I'm going to win the bodies. That's not a that's not the American way. Lauren Bedula 12:44 So, one of the challenges I see from AI companies trying to operate in the defense and national security space is figuring out a way to differentiate, which often looks a lot like what you just did is going through specific use cases, understanding mission or connecting it to the user community. But adoption of the technology is well, and part of that is teaming with other companies so that you're coming up with a solution or a use case that really resonates How do you think about teaming on the commercial side? The government sides. Maybe you don't have to, but is that part of your strategy? Paul Breitenbach 13:18 So, you know, in the world, that you need less bodies, it means to traditional system integration process less relevant. So, you know, like, that's not usually a popular thought, because so much of the business models are people based, right? You know, we believe in this idea of like, of making one person the power of 1000, right. And so, you know, in the world, in the software and the technology, we've created an AI, it's much more important to understand the problem. And the outcome that we're trying to drive than it is to have all the bodies kind of, you know, touching data and doing all these kinds of, you know, sort of basic thing to the software does all that and in our world, so therefore, you're the main person is or people is always the one who says, Okay, let me describe the problem really clearly, like hyper clearly. Because let's think of the retail example, when you talk about one sale to go up, oh, that sounds really specific long sales to go, Okay, well, I can put it at 90% off, so they destroy your profits. So, you have a lot of sales, but then, you know, make it up on volume, right as the old joke, okay. So clearly sales going up in like promotional, whatever our counter indicated, if your goal is really profit, so clearly being more specific about sales going up, clearly is really important. So, but the same thing happens in the defense world, right? Like, okay, what do you actually really mean by this, like, what is readiness mean? Everybody talks about readiness, as like on these little one-dimensional aspects. Ready means key in turn on fly, it goes. And there's all kinds of other things that have to happen to make that happen. You have to have the part the piece the person all the all Is everything behind it has to be ready to go. How do you coordinate the left and the right hands in order to make that thing, whatever the system is ready? It's the same thing on the commercial side. So, to me, our focus is always on the bit, you know, it's funny back to the business user, and understanding the problem with great precision, because the problem and you take a supply chain and inside defense, it's complicated. I mean, you got 10s of 1000s of parts that make one particular system work. I mean, how do you coordinate that? And, you know, you think so in the New World, I think the new ideological like people have talked about, okay, I'll really, what matters now is problem definition. And I think that's the way our differentiation is we can free people up because we could talk about the problem talking about the outcome, and then how do we what is being happy? I had a business school professor, that every test you got, whereas I you know, are you happy? Or you said, and help Biermann, right and, and then defend when you get the suit that but it's a really way good way to think about things, right? So, what does it mean to be happy, right? I mean, for this outcome that you're trying to drive, because in the world of AI, it's a lot like a genie in a bottle, you rub on the lamp. And then lots of people say, I want to turn a life, okay, well, they put you under the, you know, in the prison chained up in the dark, because then, you know, for eternity, okay, clearly, I should be more specific. So, this is where I think, you know, the power of who we are. And teaming on the problems in coordination is our advantage. And I think that's what's so cool about the world of AI that we're living in this phase three, this next gen world, at least we're in right, of trying to make things better. Hondo Geurts 16:42 So Paul, you engage now with a lot of different DOD communities, servicemen and women in all branches, and, and all walks of life, all such different levels of technical skills, is there are a couple of things that you wish, like everybody in the DOD understood about AI as a going in premise, you know, that would that would help break through some of this, we'd say, challenges we've had with adoption, is there a couple of things like they have to be in the back of their mind when they think about this? Paul Breitenbach 17:19 So yeah, I used to coach literally forever, right? So, I used to tell kids, you got to use your superpowers for good and not evil. Right. And so clearly, you know, there's a lot of anxiety when you talk about AI, I'm gonna I'm gonna have the jobs, I'm going to use it to do these horrible things. And, and, of course, you know, anything we can be used for, right? So, the first rule of thumb is, people with good intentions, right? If we, if we can focus on what the positive that the outcome is, and to be smart about it, that that goes without saying that's table stakes. But the other thing I'd say, for the average person, rather than get hung up in learning, the deep learning all this stuff is I would rather get hung up and all that. You know, it's thought of it in terms of like the iPhone, like all of us have different phones, right? And I can have my settings, I can change it up. Yes, no, yes, no, yes, no green off, whatever, blah, blah, blah, my apps, your phone that can be very different than my phone use, it may use it for very different things. But it's gone with the mindset that like it's okay, that in the world, I can sort of figure it out for how to make this engine work for what I need to do to get my job done. And the reason I say it like that is I'm trying to get people to not be scared of it, and just make it relatable. And I think the new technologies that are here now clearly in front of us, they're like when you see them, you're like holy, guacamole, Batman, this is an unbelievable situation just tells me these answers. And but the thing with AI, they it really is not very good at making a prediction if something is never seen before. So therefore, the human part, you know, we're really good at like, immediately figuring out Oh, my God, this is what I'm doing is this really bizarre situation, but in the world, we come from that the bizarre situations happen more frequently than that. So, my first rule of thumb is, try to think of it in business outcomes. Think of it the world should be like an iPhone, like a simple user interface that I can then instruct and understand. And stop thinking about this AI thing is like some kind of report. Like that's the one thing that happens to us all the time. Like, I can't tell you how many defense books are like, oh, like show me the reports. I'm like, Sir, Ma'am, this is about the future. These are predictions hasn't happened yet. But this is what we think is going to happen right? And so, the idea of you knows, try to break the frame of reference I guess is my point don't look at using a new school capability and trying to force it into a way that it uses old school stuff. You don't even have an eye I think our expectation has to change and that's why forward leaning people who are willing to like almost to be a little bit kid like and reimagine. Those are the ones who are usually get this really quick and I think that would be my advice. Don't worry about the technology. Apart, let others do that they pick the right partner the right technology, that part is going to go find. Hondo Geurts 20:05 Where its corollary to that is, don't worry about creating a 600 billion instance data lake. Because I think also there's a set, you see these large language models, and it's got, you know, a bazillion different parameters and all that. And I sent a lot of folks think that they can't use AI machine learning automation until they've accumulated all the knowledge about something for the last 50 years? Paul Breitenbach 20:35 That’s a really good question. So, data, Lake things, it's like, it's almost become these excuses to why this should take $2 billion, and why it should take 10 years. And, you know, the reality is, is that we're hitting, we've entered this era where the data problem basically gets solved. Now, yeah, there are going to be things, laser precision munitions, it has to be correct, right, there's, I'm not talking about that I'm talking about the 98% of the other use cases where, you know, human beings will make decisions, we're not landing planes, we're not launching missiles off of the vast majority of this. So, the idea that I have to have pristine data is, first of all, there's just too many new data sources every single day. And like we humans, we use a variety of data altogether, with only partial data, and we can figure it out. That's the new objective, this concept that you got to rip everything out. Is a is an anachronism of old technology that is in no longer need to do that. For the vast majority of use cases, and by the way, the vast majority of use cases a world of money is that's where the budget releases. That's where the benefit, the ROI comes from, right? It's no joke with us all the time. Because the Aberdeen immune department always says, Oh, we don't have any data, we don't have the data on like, you have way more data than we have in the commercial world. We were using signals to try to come up with answers to make decisions. And we have a percentage basis a fraction of what the department has, but because they've been taught, it's all going to be trapped. And it's all going to be data lakes and all this stuff. They don't have access to it that makes it feel like they don't have it. And I think that's another really important question he's brought up is Stop, stop worrying about the daily part. Lauren Bedula 22:16 I often hear two complaints. One, we don't have the data to we don't have the talent. When you talked about the early days of Priceline, you talked about the team, how culture eats strategy for lunch, I love that thing. But that the team is so strong, and you attributed that to Priceline success. You've also talked about how AI is changing team dynamics, whether it's company to company or internally to or some fears around how AI can replace people. Can you talk about how you see people as integral or not to where we stand in terms of connectivity between the national security community and the defense community? Like, are we okay with talent? Are you seeing on the commercial side interest in supporting the national security community? What's your take on where talent stands? Paul Breitenbach 23:03 So first of all, I think we produce some of the most amazing talent, we tend to look in unusual places for talent. So not your typical, you know, AI programs, right. And oftentimes, when kids coming out of school or even grad school, and they show up, I have a degree from a sir, I met my first time ever the other day, I met a kid that comes in, he's like, Oh, I was in E commerce major. And like, I just did, I missed the fact that was a major ecommerce deal. I was doing ecommerce before there was a major. So, the, you know, I feel like the, you know, looking for talent and unusual places, the right mindset of analytics, I feel like there are so many overlooked programs that we pull from that are not your typical things. But you have to remember that comes at this from we built software. And so, as a result, you don't need to deploy, you don't need hundreds and dozens and hundreds of people. And the first time you deployed, we show up with one person, right? And the government has an ungodly number of people in the meeting Ungava. And where's your team? I'm like, Well, we have, we have something that looks like it feels like 10,000 people in the background, when we tell them, we're gonna configure it to do what we needed to do. So, I think that that comes out, then it gives you this permission to look in places that are unusual, because remember, going back to our problem, you get folks that come out of school that are really maybe geeky with a particular math or they took a class and they do one particular branch, but then they don't understand the problems. And in our world, you know, our issues are technologists need to be really good at being able to absorb whatever that problem is. We do stuff and food to all these areas where like I have to have some understanding. I can't just be good at the computer part. I have to be good at the life part and understand it when we're trying to make it do something new. That becomes the hard part. So, I feel like I'm and by the way in defense To be new and notice how they're more than anything. We have ungodly, capable people. I mean, it gives you chills when you leave these meetings of oh my god, you're doing this with, with paper and pencil and clipboards and spreadsheets. And I'm like, so if we just create this new capability, they can do something in seconds that would take them weeks, if they were lucky. That's the part which makes me really, really feel good about our defense capability. If we give capability to these people that are incredible in subject that they are in their unstoppable force, I feel like that's the big idea that people think I gotta go hire more data scientists. First of all, they're hard to find they're hard to get. And they just tend to make it harder for the subject matter people to make progress. The defense community produces amazing people the way it is now, without anymore, you know, new AI stuff. Right. Hondo Geurts 25:54 So again, you come from this, you know, very commercial, very tech entrepreneurial. What would you tell your peers or folks coming up with that background about, you know, what's, what are some of your lessons learned? Engaging in national security goods, Bad's. What do you take away from it? And are there things you're learning? Through the engagements with national security that actually bleed over to your commercial side? Because I do think there's this sometimes mindset that commercials got it. All right, and national events got it all wrong. And it's a one-way transfer of ideas. Paul Breitenbach 26:30 It’s definitely not that way at all. Well, this, the thing about being an entrepreneur for so long my whole career, right is I mean, this is a great country, the fact that we create so many new businesses and the spirit of innovation. It is it's incredible, right? It is also like this enormous curse. Forget about the defense, per se, right? Just being an entrepreneur is really, really hard. It's, you know, look at me, if you look at society, right, you know, the Pioneers are the ones that took the hours into negative selection in evolution, right being the being the innovation innovators, not normally. So that's what makes America so interesting USB, so interesting, because we create all these new ideas and this mishmash of things they spit out, I think I got this other new problem, I think it could solve the problem this way. So just forget about the defense side. Like it's hard to begin with. And you know, the only thing I can say is teams, culture eats strategy for lunch teams, teams, teams, you got to have a recipe, it's not just about one person, it is you got to have the If so, like the joke I learned from my Submariner friends, right, like, how many parts does it take to make it so you know, leave all of them, you know, takes the whole team, right. Like, and so this is where I my advice on that. Now, when you get to the government, it's like a, you have to kind of make it through the first, you know, phase of entrepreneurship, because the government part of it, it's, it's complicated. And it's good that it's complicated, in many ways, right? But it is, you know, and the government has its stuff to do to lean forward on the opportunity entrepreneurs, because otherwise you just keep doing things same away, right? Because the limit they can get done. My sense of it is that you'd have to, you have to be pretty Battle Ready to try to go make it through the government, valley of death. I mean, I don't know how else to describe this. But it's if you think being an entrepreneur by itself is really tough. Trying to do it within the government is it's a special breed. Right. And but your point, I think, is even more important is that when you do get there, or if you do get there, you know, it goes in both directions. Right. And I think, for so long, we made it seem like what you said, is commercial government, they don't really talk, you know, like they were even a horrible like, things they say, Oh, it's good enough for government work, right? It's a horrible thing. Right? And its idea that somehow the government is just incapable, I don't see that at all, I actually see the opposite. I see in seeing capability, that the Java technologies to unlock it and deliver that in a really effective way. And I think, you know, maybe this is a maybe it's kind of playing with things. But it's got to be it's got to be done the American way. Right, it's got to be who you are as a, as a from the beginning of time, it's got to be this, this notion that I take ideas, I give some flexibility to the people in the spot. And unfortunately, the old way where it's all dozens and hundreds and 1000s of people, it hardens the silos. So, in my mind, the way to unlock this is to try to, you know, put technology in the hands of the actual business people and allow them to lean forward into it at the point of need. But yeah, and I think once that happens, I think the government's role in the future of leveraging AI should all about you know, just like our adversaries like you know, that a lot of times in other countries the business is owned by the government, right? It's like it's unbelievable how their national security and commercial is Actually, you're literally embedded out there making a ton of money while actually promoting national security. I feel like we have not begun to scratch the surface on that topic, which is a whole other podcast in the subject. What can we do that actually helps encourage all the economic productivity on the commercial side, but yet advances our ability to deliver for our citizens and improve our security? That is a whole other you know, but it does not just go in one direction you are 100% Correct. And I think we'd be wise to listen to the plethora of unbelievable capability inside our departments. Hondo Geurts 30:34 All right, I have to ask it, only because I've lived it. Give me the Shatner story. How did Shatner get in Priceline. How did that all get conceived? If you're willing to share? Paul Breitenbach 30:46 Yeah, I just share parts of it. Right. It's actually I think it’s, so you know, we ended up with Shatner, because Bill Cosby turned us down. A deposit that I like, and that I chalked that phrase, I trust that I will come up to the harder I work the luckier I get like, you know, the like, not the new piece merging Bill Cosby, but I mean, it turned out to be an unbelievable thing. So, but ya know, through jays, friends, we networked our way to get to Shatner's late wife, Noreen Jenner. And, you know, at the time, like, you know, it was one of these things that he was retired, he retired, you know, on his horses and shop. And he's like, we got to go to let people buy things with a computer. Like what you'd like to talk about mind blowing, I'm trying so how do you explain this? No, to the guy like Shatner, and of course, it was we’re just very lucky. Meanwhile, he sent his consigliere to the office, right and consistently hair that the limousine pulls up and the dog gets handed out. So, like, what do you when somebody with dog comes out? Let you do so I pick up the dog like, I'm petting the dog. So, like, so then, like, remember the old flip phones we had like the old Star Trek things, right? It's on hold the dog trying to flip the thing and have to die out. There's no memory. So, like, we had this whole, like, internet thing. We had this whole presentation really, really early. This is why the consigliere is going to hear and it turned out to be one of Noreen Shatner's, you know, friends, you know, very mafia supermodel friends and the boyfriend and knew about computers. And so, we have like, yeah, so let's just say our original presentation, we didn't do any of that. And with the dog, we kind of walk around and talk about what we're trying to do and very and that's how they reported back and so but the funny thing of it is that you know, he made I won't say he made a fortune in stock options are an absolute fortune and but we had no money to remember back then we were literally entrepreneurs starving for cash, I think we paid him I want to say is like 300,000 bucks, which in the world of like that is like basically not paying someone like that. And his options, which turned out to be worth like, you know, ungodly 10s of millions of dollars, right? And of course, trying to convince them we're going to give you the stock options. You're trying to build off and blah blah blah they're like no, no, no, but you know what was cool about that story though, is you know, in the in the heyday of the internet but also when the exact irrational exuberance speech where the world exploded, right and then you know, that man was the most committed was do more commercials, what do we have to do to make this work culture eats strategy for lunch. And Shatner was in his he's an amazing guy stellar, like, but he was for pricing, and then quickly rebirth, the whole concept of having a celebrity spokesperson and, and, I think, contributed to relaunching his career, Boston, legal, all the rest of it. So, but he's an amazing guy. And he, he's a great storyteller. And he was incredible. He's just an incredible guy, but that's how he is how. Lauren Bedula 33:53 Cool, I have one more, which is your energy is pretty contagious. And you've been doing this for a while. How do you stay fresh? How do you keep the good ideas come in any tips for folks that are getting burned out? Paul Breitenbach 34:07 Yeah, it’s funny because, you know, you're not the kids, right? My kids are older now. Right? And, you know, make it to high school to college is nothing, nothing's nothing is easy, as you guys all know, right? And you got to find your own inner way to refresh and trying to explain especially on the entrepreneurial side, you know, like set the scene and Deadpool always comes to me right, but he's got the Mantis is listening to air supplied, he's blowing himself up 100 times. I can't seem to kill myself. Like and so but that's, it's an interesting, like analogy. You got to find the place that allows you to mentally like in that scene in the movie, to refresh mentally because it's a marathon. It's not a sprint. And so, the I think that you know, that's the advice you got to find something like that and you got to find the people that can help you get point of view in perspective and you know, I think I do a pretty good job of listening and taking, you know, advice of what can I do to make this actually come forward? But the only way to do it good otherwise, you know, yeah, it's great to I could have retired a long time ago. But, you know, it's a lot like it, there's just there's a big opportunity here. And it's our obligation and our privilege to try to be able to make this better. This is not like an opportunity. I always look at this as with gratitude that we're allowed to do this. But the only way to do that is you got to be able to survive the marathon. You know, these guys that run these 100 miles? I mean, how do you do that after the first 26 miles? Lauren Bedula 35:35 Well, that is such helpful advice, Paul, we know how busy you are. Thank you so much for coming on the show sharing your story. I know I found it very encouraging. So, we appreciate it. Paul Breitenbach 35:43 Appreciate you guys. Nice to see you. Thank you.