Two People, Three Years, and a $9 Billion IPO — with Ed Hallen

[00:00:00] So it's not Hallen, it's Ed Hallen I just learned. So no Van Halen over here. No Van Halen roots. All right. So Ed, welcome. We met I think probably about a couple years ago now. I learned that you helped build a company and went public, but you did that not here in Colorado. You did that elsewhere and then you came to Colorado, but you're still actively involved in the company.

[00:00:24] So why don't we start there? Let's talk about, and one other tidbit as well that I think is interesting that we should bring out is, although you're involved in different branches, your wife also is a very accomplished entrepreneur, which I think is interesting as well. So let's start with Boulder. So bring us through the circumstances that brought you to Boulder and why Boulder and what you have been discovering as you've been in and around Boulder.

[00:00:50] Yeah, totally. So you actually kind of hit on some of the circumstances, which had been in Boston for a long time, had started Klaviyo in Boston and a year or probably six months into the Klaviyo journey. And Klaviyo was started what year? In 2012. 2012, so now it's about 14. Gosh, yeah. Feels much longer in some ways. So started Klaviyo in Boston, met my wife in Boston, who was in a joint graduate program in Boston and California.

[00:01:20] And so we, for a long time, were just commuting back and forth. And so kind of like every weekend for a couple of years, I was flying to California. And then at some point she finished school and took an early job at SunEdison, launching a renewable energy business in Africa. And that happened right before SunEdison went through a massive bankruptcy.

[00:01:43] And so she left that and promptly started a fintech business serving African mini-grid developers. What was, and we're going to get her on this podcast at some point too, but what was her affinity for Mexico and for clean energy? Yeah. So it's really, it's what she'd always worked on. So she, her father worked in sustainability for the local utility.

[00:02:06] She, coming out of undergrad, her first job was a Fulbright going village to village in India with solar lights. And so she had always been interested in climate and renewable energy and had then worked in a series of jobs and kind of had been very excited about this, had met the former assistant secretary of energy who was going to launch this business at SunEdison. And then SunEdison's, you know, the U.S. business drove the collapse of everything else.

[00:02:34] And they left to start this business with Emily kind of being the main founder and running it. And so. Emily being your not-wife. Emily being my wife. Yeah, exactly. So, so right afterwards, we got married in 20, midway through 2016 and we headed straight to Boulder. And the reason we had, she was graduating from. From Stanford. Stanford. And you said a dual program. A dual program at Harvard. So she was a policy master's at Harvard and then an MBA at Stanford.

[00:03:01] And your Boston connections, where did those. Yeah. So I, my grandfather had been from Boston, but I ended up in Boston because right out of college at University of Virginia, I wanted to start a company and wasn't sure what yet. And so went to, met a few guys who were starting a company to build large scale business intelligence for people like Walmart and Starbucks and Bank of America. They were former McKinsey consultants launching kind of a business to do this with software.

[00:03:29] And so kind of, at the time it was big data and AI. Way before AI. Way ahead of time. Way before AI. And I was just a junior, originally a junior person doing, I worked clients, I did sales, I coded, I did product, I kind of did a bit of everything. So I was just a junior, at some point ended up kind of starting the product management function and then also moving to London to really launch their European operations. Okay, we're going to come back to all that in a little bit. So let's finish off on how you end up in Boulder. So your wife graduates.

[00:03:59] She graduates. Her job now is Africa focused. Yeah, Africa focused. She's going to realize that she's going to start a company and took some very early venture money from a group called Factory Ventures. And they're in Fort Collins, spin out of Colorado State. And working with that team, we came to Boulder with the specific idea of, hey, one, we're newly married. Let's live in the same place for a little bit. And she was going to be an entrepreneur in residence for two months while she launched the company.

[00:04:27] And then we expected we would go settle somewhere else. And so had no intention of staying in Boulder, but also didn't really want to be in Boston. The winters were horrible. Didn't really want to raise kids in California. And so we were open to the idea that we would find somewhere to be long term. And moved into, thought about Fort Collins. I knew I was going to start more companies and wanted to explore the Boulder startup scene. Got an Airbnb for a month. Liked it. Stayed for another month.

[00:04:56] Got the Airbnb directly across the street. And then two months later bought the house, Coyote Corner, to us. And then actually now ended up, we live a block down the street. So we've lived on the same block in Boulder for 12 years. But really incidental. So probably after four or five months of being here, we realized, hey, this is where we're going to be long term. So easy to get to the coast. Not great for her on the Indian-African time zones, but she just gets up really early and does that. Easy for me to get to Boston. Great place to start companies in the future.

[00:05:25] I think just felt more, both how we think about starting companies, how we think about life, just everything felt more aligned with our values and our views on the world than Boston or San Francisco, though. Certainly have a very, very fond spot. Talk more about when you said how we think about life. Was it about Boulder and Colorado that fit well for you in that category? Yeah. So, I mean, we know, like, we both work hard. We both are entrepreneurs. We'll keep starting companies.

[00:05:54] You know, that's, we kind of like the idea of, like, you can go and create something and drive change in the world. And Boulder's a place where there's a lot of people doing that. But at the same time, you know, in the minutes you're not doing that, you can get outside and go for a hike or run within minutes. So, you know, Boston's great, but the problem is you have to drive an hour to get to any mountains or anything else. And Boulder felt like, one, that was very accessible. Two, a very easy place to live in kind of the best possible way. There's a strong community. You have everything you needed.

[00:06:24] It didn't feel like a big city. But yet you had amazing people from, honestly, from all over the world who you could still work with. And so on all those dimensions, it felt more manageable and doable than Boston. Than Boston. And, you know, I think we just liked the community much more. I think compared to San Francisco, I think something that I always liked about Boston and I love about Boulder is it's way less hype driven.

[00:06:47] So it's way more about doing real things, driving real impact and building something versus, hey, how do we tell an amazing story and raise a ton of VC money and then go figure out the second piece of it. Yep. And it was 2016 that you arrived? That's right. Yeah. So now with a decade of living here, what would you say? Yeah, I think it's remained true. I think, look, there's a lot of things Colorado can do better. There's a lot of, you know, all those dimensions I think have generally proven true.

[00:07:17] Now we have kids. They're in the Boulder Public Schools. Like all of that is great. So we've been very excited. I think we continue to meet new people in the community who are doing incredible things. Which in some ways speaks to, I think, the one maybe downside, which is that, you know, we're 10 years in and we're still meeting people who have done incredible things, who have been here the whole time and we've just never met. And so, you know, there's a lot of room to grow connection across the entire startup scene and across the entire community.

[00:07:48] But it seems like more and more we see that happening. I mean, you know, partially thanks to things like Caruso Ventures being the impetus for it. And you're involved now in Endeavor Colorado, which is part of Endeavor Global. And that's a great way to get exposed to more of the, not just the new entrepreneurs who are doing crazy stuff, but also the ones like us who want to hover around that and be part of getting to know the broader community.

[00:08:12] You know, I like to think of myself as, you know, perhaps, you know, with Brad Feld, not here as much, the most connected person when it comes to who in and around Boulder and even extending into the Denver area are part of the entrepreneur crowd, the venture crowd. And I still get amazed at discovering new people. I'm like, how could I have not known that person existed? And we've been, you know, living a half mile from each other for the last, you know, five years or seven years.

[00:08:41] A lot of really interesting people arrive in town here. And more and more of them are from the creative crowds. So people are coming from Southern California, perhaps, as opposed to the Bay Area or the equivalents of those two. And they're into film or they're into different types of arts. And, you know, that's an interesting new dimension here in the greater Boulder area as well. Yeah, it's great. It's really, it just makes, it's a richness.

[00:09:06] Yeah, that, you know, this men's book club over the past year that, you know, you have tech people, you have film people, you have just, just a complete array of very interesting people. But none of, you know, none of whom were aware of each other until, you know, beforehand. Good. So you knew you wanted to be a startup guy, it sounded like very early on in your journey. I, yes, I knew, I'd always been very interested.

[00:09:33] It felt like a way to go out and drive, you know, kind of like real impact in the world. And so had to explore some of things. But by the time I was graduating college, I knew that that is, you know, kind of the direction I wanted to at least start in. But why, why, for you, and this is a personal question, why is having a real impact in the world? Why was that important to you? And what does having a real impact, you know, mean from your, you know, your personal lens? Yeah.

[00:10:01] You know, I'd grown up in rural Tennessee for the first nine years of my life. I then moved to Switzerland to live there for four years, moved to Holland for three years, and ended up back in rural Tennessee. And my father was just an engineer at a chemical company that used to be part of Kodak, now a separate company called Eastman. So his job, their desire, but then his job is what took us abroad.

[00:10:23] And so I think through that journey, I'd seen just a lot of different communities and certainly different types of people and felt like, hey, you know, as I think about the types of things I could do that felt like, you know, if you have the opportunity to go drive change in whatever way you can, why not do it? It felt like a really interesting way to build a life and build a career. And so that had been, really had been my focus.

[00:10:50] And so I'd explored various paths. Very quickly, I wouldn't publish this. I wouldn't, if you don't mind, not post this part. Basically, I thought, I always thought I was going to go into politics. What happened was, much longer story, ran for a student body president of UVA and ended up in this like massive racial hate crime FBI investigation where my opponent was assaulted by someone. New York Times, Washington Post with knots calling me, trying to track me down.

[00:11:20] I wasn't suspect, but I ended up having to talk to the FBI a ton. And the whole thing, I was like, I'm never going into politics. This is miserable. And so then I was like, okay. I'd always debate between that and technology and leaned hard into the technology side. Yeah, it's amazing how people who are covering politics or in politics, let's focus on people covering politics. And I get it. They're focused on things that will cause them to be significant. So things people want to read about and hear.

[00:11:49] But when you come from the world we come in where you're building stuff and you're developing companies, you see the impact of what entrepreneurs and venture investors have and back those entrepreneurs have on real people's lives across the entire world. And how little attention that gets when people are covering the press. They don't even know what's going on. It could be going on all around them and it's the last thing they think to report on. But someone does something that maybe could generate a little bit of news and that gets all the focus. And it's frustrating. Yeah, it's depressing.

[00:12:19] Yeah, I worked for a technology lobbying firm, one of the primary technology lobbying firms as an intern in college. And they did the lobbying for Amazon and Microsoft and all the tech giants I'd always admired. And I was the only person in the office who understood any of the technology. And kind of like these were great – they were great at getting things done and like getting time with congressmen and senators and whoever. But it was all just kind of – it was depressingly performative over time.

[00:12:48] Okay, so Boston, that's where you went to? Yeah, so I was in London, knew I wanted to start something, felt like there was no time like the present. And I'd always heard that an MBA was a – you know, you didn't really need to go to class. It was a great place to actually go, you know, take time and start something. It would cover kind of a gap in my resume. And MIT in particular had always really admired the people I knew.

[00:13:15] Went there and the environment and just, you know, the fact that you kind of across the campus could be exposed to a ton of great people starting great things. And it completely worked out. And so really spent, you know, the entirety of those two years trying to figure out what company to start, experimenting with different companies, different co-founders. My Klaviyo co-founder had been one of the early engineers I'd worked with closely at the first company. He moved back to Boston because his family has been there for multiple generations. You know, he was one of the people we talked about starting something.

[00:13:43] And finally, after I'd been in Boston for a year and a half, at MIT for a year and a half, he quit his job. And I basically went full-time too. And we said, okay, we're going to start a company. We fundamentally believe that companies are entities that solve problems. And so we're going to find a problem. The tool we knew how to use was software. So he's kind of a hardcore engineer. I've been a generalist and a little bit of everything. We will start a company around various – trying to find a problem in the world, solve it with software, get people to pay us with a credit card. And then we know that that's a real problem.

[00:14:12] And we can – if there's enough of those people, we can build something massive. Well, that's pretty interesting. Let's go through that again. So you're trying to figure out what to do. And so your idea is we're going to find a problem. We're not sure what the problem is. We're going to find a problem. We want people to pay for it with a credit card. We want to solve it with the software. That sounds like a great idea. Let's go. I mean, and the reason for it – the reason for the obsession with the credit card is that we needed a tight feedback loop. So we needed to know this is real.

[00:14:40] And people like to tell you ideas are good. So if you ask somebody, hey, I'm going to start this company. It's going to do X. Even if they're in your target market and you say, hey, will you buy this? People invariably will say yes if it's at all a decent idea. When you actually ask them to pay for something, much more hesitation. You really have to have found a real problem. And at the time, we could have focused on selling to big companies. We knew we wanted to bootstrap. It was two of us who were going to be in a basement.

[00:15:07] We needed to kind of within a month or two figure out if an idea was good before we pivoted to the next one. We knew after a year or two we'd run out of money and have to go get real jobs. And so we said, okay, we'll give each idea about two months. If at the end of that we don't have paying customers, we're moving on to the next thing. And it took us four or five different iterations until we finally hit on something that seemed to be at this intersection of like, okay, they'll pay. Feels like a big market. And we can really solve this problem. And then we're off to the races.

[00:15:37] Yeah, I'm going to go off on a slight tangent. By the time we dropped this, the Tulagi Fest, which is the new name for Boulder Roots, would have already happened. And my team, including myself, were spending a whole bunch of time on it. You know, the kind of time where you can almost say, well, if you just cut the check and paid for it, we'd spend a lot less time.

[00:15:58] And, you know, isn't our time valuable, including my time, relative to some of the stuff like the general mission ticket sales, for example. You know, most of the way we fund it is through what we call the founder's experience, which is people who can contribute more and want to help support the community. But you've got to get them the right checks. And then you've got to get people to buy tickets. And certainly the founders, they're writing a lot of checks. And, you know, that's maybe a different conversation.

[00:16:27] But for the GA, you could argue, why not just cover that cost? And then you don't have to work so hard to sell tickets. But it's what you said. When people have to spend money, that's a way of them expressing that it's important to them. It's valuable to them. It's, you know, what you're doing is being done in a way that they place value in. And without that feedback loop, how would you know? You know, you'd be guessing and you'd be taking maybe the easy path.

[00:16:50] But you'd probably end up with something that really isn't all that interesting because it will probably die before it continues forward. Because you didn't get that true feedback loop, which is, you know, people are giving you something in value in exchange for it. That is proportional to what matters to them. And, you know, I guess I hadn't thought about it that way until this conversation.

[00:17:14] It's why we're working so hard to get people who participate in a way that they have to put some skin in the game. Right. And then they feel ownership. They tell their friends. You know, it helps everything spread if you're providing a great experience for people. And they have an expectation that you then have to exceed. Because if you're giving them something for free, you know, maybe they still have an expectation, but they didn't really buy into it to begin with. So they can move on and they won't even tell you if they didn't like it. Why would they?

[00:17:43] They didn't give you anything in exchange for it. But if you've asked them to give something in exchange, well, then, you know, they're going to expect something. And that's going to make you more able to give them something that exceeds their expectations. Yeah, that's right. I never thought about the parallel. Not too lucky. But what you guys are saying is that credit card is an immediate feedback loop. It's not a sales funnel that you have to work on for a year, identify the customer. You know, it could be a long time before in other business models where that feedback loop comes in.

[00:18:11] You have to build something that takes a long time to build. And you want something where the feedback loop in the form of people writing you checks was happening really early on and could help guide your efforts is what you're saying. And it made sense for, you know, we were first-time entrepreneurs. We didn't, you know, we had very, we had like a tiny bit of savings in the bank. So we really, we knew that we had kind of this tight window.

[00:18:34] And so for us, as long as we didn't raise money like we had, like that was the path we, it made a ton of sense for us because we could just resolve quickly. There's obviously a ton of businesses that need to get started that we therefore didn't think about starting because of this. You weren't going to go build a rocket ship as their first step.

[00:18:52] But you still had a problem because you knew, you knew you wanted to have like a big problem that needed to be solved that people would pay for via credit card and you're going to do it through software. But it's still an answer. Well, okay, now we got to narrow down and start to pick possible things to solve. What was the range? The range was wide. And so, yeah, so we'd, we'd been exposed to various things, just working in technology and the first business we worked in.

[00:19:22] And so, you know, the things we tried range from privacy focused, consumer privacy focused software. That would be kind of a layer that set atop the web to help people protect their privacy to customer support software. What kind of the ultimate journey where we settled was, you know, the, the, one of the, the first company we worked at was this company called Applied Predictive Technologies. They, um, worked with a huge number of kind of the largest, uh, retailers and banks and et cetera in the U.S.

[00:19:50] They ultimately sold to MasterCard, um, along the way. A piece of what they did, you know, they were basically empowering these organizations at the kind of like CEO level to run big real world tests. And the, to do that, what you needed was, uh, a clean data set sitting underneath that unified all the data in an organization. And so when we thought about, you know, what was novel about that is like one technology had just at the point where you could build that. And previously, you know, we could build it for these with a ton of people.

[00:20:19] We could build it for these large companies. We realized like, Hey, we think every business needs this. They need to understand, you know, if you think about like a large coffee shop chain, like the historically that the data system that knows what the sales are is different from the data system that knows the inventory is certainly different from the HR database that knows if the store was understaffed is definitely different from the real estate database that knows where the store is. So you can ping, um, federal APIs and look up like what the weather is outside and actually understand like on a given day, why coffee sales might be down.

[00:20:49] You need all of that information in place. And so we said, okay. And, and, and traditionally those would be very stove type. Yes. You'd have, uh, you know, and then you'd have people who are protecting their system. Well, you know, I know you define a person by their first name, then their last name, but our system only looks at last name, then first name. And we think that's better because when we do alpha to the quarter, it's easier to do it that way than this way.

[00:21:16] And, you know, and that's like a very simple example, but everything is, you know, what a building is named, what a, you know, what's the location of a building name? What's, uh, you know, when you say customer, are you talking about the name of the company? You're talking about the name of everything is different in every system. Uh, and, you know, trying to even get them to, you know, know anything about what's going on in the other system is an integration effort. And that's just the way it was. Right.

[00:21:43] And, and I think, uh, even still, I mean, I just, you're jumping ahead as we think about AI and what's going on. Most organizations are still the same way. Like it's, it's, it's very difficult to have a unified single source of truth. You can kind of like really force your way into it with systems like Salesforce and others or custom homegrown systems with Snowflake.

[00:22:02] Like, but for the most part, like this is, I think what we're going to see is like the, what is going to be needed to really drive a ton of power out of AI is that until you have this common understanding that all that lives in humans. Like humans are the ones who have to like initially tie everything together and have technology. And we don't have it yet. And we've got two of our newer people at Crucible Ventures are here with us in the room and they hear me harp and harp and harp about, you know, our CV central kind of database that, that everything resides in.

[00:22:30] You know, it's the source of record for everything and everything's connected to that. You know, the entities, the people and all the attributes and that, you know, I preach that as a religion that, that, that, that is our source of where value lives. It makes everything else easier to do and, you know, amplifies all of our collective efforts, but everyone has to be committed to that. You have to be committed to that, be comfortable with the fact that all that information lives in one place.

[00:22:56] It's accessible by everyone and you're responsible for helping to make it as comprehensive as possible. So all of our intelligence when it comes to those types of attributes has to go into that system and it has to be maintained. It has to be, you know, as clean as possible. And then we all benefit, but it's, I think I drive most of our people crazy. But it pays off. Years of that is so powerful.

[00:23:21] Oh, it's, I mean, you know, people will, you know, both when I was at Zayo and certainly now with what we do, like, how do you guys do all that stuff? They don't realize that half of the answer to that question is command and control over that central data and getting the whole organization to buy into that and to, you know, religiously use it. That is such a superpower.

[00:23:45] And most organizations just don't, one, they don't believe it so they don't do it because unless you experience it, you don't realize how important that is. But even if you're the experience of it, you don't understand kind of why it is. You become the beneficiary and you just, you know, if you're brand new to the world, you say, well, everyone must do it that way. And then you go somewhere else and you're like, oh, my God, this place, you know, has no clue.

[00:24:10] Or you just, you know, underestimate or underappreciate what it took to get that to be put in place. But the power of it is undeniable. Yeah, it's really incredible. Yeah, and exactly. It's the annoyingly simple stuff like you pointed out. It's like we have different names for each customer. Customers have multiple email addresses. Sometimes they have multiple phone numbers. Is a, until you, like, get into the weeds that, you know, all this stuff matters. Like, the details matter a ton and then that's the platform for kind of all the decisions you make.

[00:24:37] Yeah, we've been helping an organization that is, I'm being a little hesitant because I don't want to necessarily call out who the organization is. But, you know, they have this very issue and they've seen how we operate. So they've like, you know, can you show, like, my people what you guys do and help them do it? But so we've been kind of showing them and kind of leading them.

[00:25:04] But they're in that classic, you know, big time, you know, well, we've got, you know, eight different organizations. And, you know, and they all, it's the same variations of people that cut across. You know, you could take a person, any given person, and they are relevant across four or five of these different initiatives. It might not be the same four or five for each person. And maybe one person's already involved in two but is a potential for three others. There's, you know, some are employees, some are former employees, some are customers, some are sponsors, some are this, some are that.

[00:25:34] But then it's the same thing with, like, organizations they work with and people within organizations they work with. And all that is one interconnected set of intelligence, which right now for them lives in at least 10 different places that, you know, don't even have visibility into one another. And it's, you look at that and you're like, oh my gosh, you guys really dialed this in, which would not be that hard to do in today's AI world. If the right people were doing it, the lift you guys would get would be monumental.

[00:26:02] But you can't even, like, convince them of that because the first thing they do is give you all the reasons why it's really hard to do. And it's like, yeah, sort of. But, you know, the other way to look at it is it's not really that hard. You just need to find the people who would look at this and say, oh, that's pretty easy. And in today's AI world especially, it is pretty easy. Yes, yes. I mean, automating any process, it's like there's just some amount of time you spend and it can feel like a waste of time. But invariably, like, you do that, it's a big enough problem. It's going to pay off in the years.

[00:26:30] Every process works better if you have command and control over kind of centralized and connected data. So anyways, that was what you guys were seeing. Yeah, so a few ideas in. More than a decade ago. So at the time it was, you know, a few things that happened that made that possible just technology wise. But we said, hey, we think this is relevant to all businesses. We started looking at the customer service domain because we'd been there and especially of software companies.

[00:26:58] We, at some point, had, you know, started to focus on some kind of like SMBs because we knew we could sell to them very fast. And SMBs, small and medium business. Small and medium businesses. Small and medium businesses. You know, they could, a sales cycle could be one phone call or a week. And so we knew they can pull out credit cards. And then early in that process, or after a couple iterations, we started meeting a few of these who we realized that a marketing problem.

[00:27:23] And so basically it was like, okay, if we have this database, if you're selling to smaller businesses, it's very tough just to sell people this data problem. To your point, like it doesn't feel valuable until you put in the work to actually do something. So how can we actually drive revenue with this central command and call set of data? And what we did was said, okay, if we stick marketing atop this and then kind of we're at just at a point in time where that was possible. Take this deep customer understanding. Take the customers. We could directly go out and drive attributable, measurable revenue.

[00:27:53] And use that to add a ton of value to this business. So when we went and started talking to kind of businesses about this, like very quickly that, you know, we talked about problems first. But kind of like the problem we would hear about is like, hey, they're treating everyone the same, the exact same. And if they weren't, it took a ton of time and effort.

[00:28:12] So treating everyone means, you know, customer that might have a relatively small amount of opportunity or might be a small customer and permanently, you know, would likely remain a small customer. And then you compare that to someone else who's a big prospect or already a big customer with a lot more opportunity. It was hard to differentiate how you would treat those two. They all kind of look the same. That's kind of what you described. We definitely see that. And then we'd see other flavors based on the type of company.

[00:28:39] Like a really common one is, you know, in the e-commerce space, which is where we kind of ultimately did. You know, you'd be a retailer and you'd say, hey, we have male customers and female customers. We're just going to send, you know, every email will just blast all the content to everybody. You'd have a first time customer who bought one thing ever and you'd have somebody who'd spent $10,000 and you just treat them the exact same. And so it was all kind of different shades for each business. And people usually had some idea.

[00:29:09] Like early on, when I went to a startup dinner, I was sitting next to a guy who ran a custom-made suit company. And he would spend, you know, six hours a week. There was one night a week he would set aside and he would manually go back and email past customers. And he kind of looked through the purchase logs randomly to say, okay, hey, have you ordered recently? Do I think you ordered for work? Do I think you ordered for a wedding? And then he'd write a personal email. And the conversion rates were incredibly high. And completely not, like, unsustainable. But he actually kind of, like, we could automate that whole process.

[00:29:38] And so kind of the presence of customer understanding and a way to reach out solved the problem that customers had and removed a ton of manual work and drove a ton of revenue. And so that was the point where we realized, okay, we're really on to something that people are willing to pay for. They're very willing to pull out their credit cards. It's really just a process of, like, okay, lean into the idea, build better technology, and how do we find more of these people in a very affordable way?

[00:30:31] Okay. So you're going to segment your customer base through a few, many different dimensions and have it much more of a personalized, customized experience based on the attributes that a cluster of customers would have? That's right. I mean, the thing we were saying to customers, you know, because of kind of this approach, like, find a problem, get credit card, get them to pay us, was, you know, we focused specifically, you know, there were two of us. And so we knew we had to focus.

[00:31:00] And so we focused very much on, to start, given this was 2012, on email marketing. We said, okay, for your email marketing, are you sending everyone the same message? And they would say yes. And then you're kind of like, do you want to be doing that? Invariably say no. And then it kind of the pitch was like, for every, if you give us a chunk of money, we'll make 50x whatever money you give us by treating, you know, building those digital relationships, treating people much better. So kind of like the way they want to be treated.

[00:31:31] And by kind of like personalizing, kind of segmenting exactly what those customers are doing. And so we realized, you know, ultimately going into business and saying, okay, you're spending money over here on these email marketing systems. You should spend that same amount of money with us. We didn't care that it was email. We didn't particularly care that it was e-commerce. We didn't particularly care that it was marketing. Like our thesis was every business needs this vertically integrated customer knowledge stack.

[00:31:57] But that was our way to go to market really easily because we could get on the phone and close really fast because it was just like a, do you pay for, at the time, MailChimp? Do you send everybody the same message? Hey, just like give us that money plus 25% and call it a day. And that made the pitch like very seamless and very easy. And do you have some early specific examples where, you know, customers heard your message and said, yeah, let's do it and turned out to be successful? Yeah.

[00:32:24] I mean, it really, the nice thing is because we had the customer knowledge stack and then the like market on top, like everyone, every customer, we could immediately just point to the revenue. Like there was no work required to do it. And so initially these were pretty small. You know, the first 10 customers were all over the map. Three were e-commerce. Some were software companies. Some were, you know, one was a parking garage. There were just all sorts of different companies. We couldn't, to simplify it for ourselves, we said, okay, the e-commerce seems like the easiest here. Let's lean in there.

[00:32:54] And, you know, the first 10 customers, they were this custom-made soup company that was out of Australia of all places. There was like a dog food company. There's just a complete range. Can we pick one or two of them and kind of go through the case study? Blank label is a good example. So the blank label was the guy we originally met. And he, his backstory was his family was in Hong Kong.

[00:33:22] He'd grown up partially in Australia and then had landed in Boston and launched this custom-made suit venture. And the basic idea was you'd go online, order a suit. He would send your measurements to his family in Hong Kong and they would make the suits and then ship them. And arrive in the States. And, you know, historically, the way that business had worked is you'd pay, you know, pay money to run ads on Facebook or Google or other places. Get customers to the website. Most people aren't ready to buy a suit right away. They might be, but oftentimes they'd do some research and look at other places.

[00:33:52] So they'd sign up for an email list. He would then, you know, before us, he would blast them all with a kind of like the same message to everyone. Once a week, he would just send a message to be like, hey, is it time to buy a suit? And kind of what he had realized over time is like, okay, once people have bought one, if you came for a wedding, you're not likely to come back. Like when you buy a suit for a wedding, typically you're buying a suit. Oftentimes like groomsmen are buying a suit.

[00:34:21] There's like a whole bunch of people actually buying at the same time. But those customers are a very distinct type who you might be able to convert to people who buy suits for work, but you might not. There's then other customers who are buying, you know, a couple suits a year for work. And figuring out the two customer segments and where they are on their customer journey, there's a tremendous ability to treat them differently.

[00:34:42] So what he did is he, when he started working with us, is he basically went from, I'm just going to blast everybody with the same thing to based on what those customer behaviors are, I will automate messages to you. And so, hey, you bought this type of suit. You asked for these things. I realized you're a wedding customer. You're going to go into the flow of like, one, I'm not going to email you for three months after your wedding. But there's like some point where I'm going to kind of start trying to figure out if like you're somebody who's interested in buying something long term. If you're not, I'm going to stop emailing you.

[00:35:10] I'm not going to like blast you with like all these random emails. But if you are and you're interested, then I will lean into that and build a deeper relationship with you over time. You used the word customer journey. Let's maybe use an example of a simple journey. Got married, need suits. Maybe won't need any anymore. But customer journey starts with somehow you discover that there is a potential customer.

[00:35:38] And maybe discover because they found you through some kind of ad. Or maybe, you know, you're more outreaching them. You're targeting them. So it starts with like, can we have some understanding of someone who we would like to buy? And the other extreme is this is our biggest repeat customer. Yes. And there's a lot in between there. And even big repeat customers, there's a lot of variety.

[00:36:02] So talk more about how you're messaging to the, you know, the customers based on their customer journeys. How would that evolve? Yeah. I mean, I think so. Even today we find it's quite rare. It's quite rare to segment. And if you are, it's typically very simplistic. It's to your point. It's like the people who have just like discovered me on day one, people who have been long-term customers. I probably split those in two.

[00:36:31] And then there are humans in the back end who are just saying like, okay, I'm going to email or SMS or WhatsApp or change my website content. And I'll do one thing for these people and I'll do a different thing for these people. And the reality is actually we're each pretty unique in different ways. And so, and the ways we're unique vary a lot. So this is a Viori shirt, which I buy a ton of.

[00:36:57] The things that have changed for me over time is like, okay, in for me specifically, it's like not only do I spend a decent amount of money at Viori, but also I am somebody who. And Viori is a very successful brand. So it's a good example because of, you know, one of the things they've done really well is establish a really strong brand identity with certain types of customers. So I'm sure they're pretty sophisticated in what you're about to take us through. Yeah. And I think, you know, there's, yeah, exactly. But they have customers who are new to the brand just discovering it.

[00:37:27] And so there's a certain amount of treatment. And if I think about one of those customers, like I, for Viori, like I might be very willing to have, to get more messages. And I, you know, I want to get, if I just ordered something, I want to get those order confirmations via text message. So I don't like a lot of people texting me, but there's somebody where it's like, if that has shown up, like I want them to let me know. And if they have new products that they're pretty confident that I will like based on the other things I bought, I'm also fine with them texting me.

[00:37:57] Most of the brands, I don't. I want fewer messages. I don't want to get texts at all. And there's, like, my relation, my digital relationship with those brands is very differently. But also, Viori, there's a store down in the 29th Street Mall. I, like, walk into that sometimes and buy things. I buy things for my wife or have occasionally bought her gifts. There's, like, all these different things that have changed over time that are very bespoke to me. And somebody else might be not married. They won't be in Boulder.

[00:38:26] They buy primarily long-sleeve things. They may be at a different point of the customer journey. They might be really into yoga. Like, there's all these different things that were each different on all these dimensions. And the way we all want to be treated, the way we, you know, if you think about 50 years ago when you walked into a store is, if, like, the clerk actually knew you, they could treat you based on who you were and the things they knew you were interested in. And they wouldn't just bombard you with just everything all the time.

[00:38:55] And that's the way it should work in a digital landscape as well. And so it's kind of just, like, one, you know, what the software does is either set those up, you know, let humans go in and curate those journeys. You do this. We do this. Based on these customer behaviors, set up this. Or more and more, it's, like, more of that is via AI. Hey, we think you should treat these people this way.

[00:39:16] And we'll build out the journeys and, like, guide people through them and dynamically change the website, text, email, everything based on who the person is. Yeah, and one thing hidden in the background of what you're saying is how much technical and analytical skill set capability is built into kind of the, hey, we need to communicate with our customers. So in some respects, you think of marketing communications as a less technical area.

[00:39:45] And maybe years ago it was if you could write well and, you know, you had a certain type of personality. That was probably the kind of people who were doing marketing communications. Now to do it effectively, you need some of that. But what you really need is technical chops and analytical chops. Because it's one thing to say we need to, you know, communicate with our customers in a very, you know, customized way, really understanding them.

[00:40:14] It's like, yeah, well, how are you going to do that? Well, the how is mastering kind of data, mastering AI, you know, getting a lot of analytical feedback to help guide how you should even categorize customers in small type groupings. But that the groupings that matter when you're trying to, you know, trying to build relationships with them. And, you know, I think it's that's also an example where AI is going to be doing more and more of that work.

[00:40:43] But the human in the loop for that is going to be extraordinarily important. They're the orchestrator of, you know, how are we really going to do this? And, you know, it's going to change and evolve pretty dramatically. Yeah. I mean, everything you guys envision and have been doing. Could you imagine if you were doing that and you were the only ones who had access to today's AI? You set off to do that 10 years ago. I mean, crazy. Yeah, it's wild. Yeah.

[00:41:07] I mean, I think we've started to see a breakdown of in some ways there's like it's harkens back to old times where if you're an amazing writer, like amazing marketing communicator writer. You know, what's different from the last three years is all of a sudden tools like ours, but also AI, like a lot of the technical work can be can go can be, you know, can go away and let people really shine. Because like a lot of that can be automated. So there's there's like one class of people there. On the flip side, we see this.

[00:41:33] It's what we see with our engineering team, too, which is there's a set of people who, you know, are both have those skill sets and then are very technical. And like the unification of that person in AI and all this data and tooling is that they just have superpowers. Like they can do the job of 10 people and it's unreal. Yesterday. Yesterday, again, I'll use Talagi since it's so top of mind. I sent out 240 individual emails from my email box. And these were to people who are associated with the founder's experience.

[00:42:03] But based on kind of who each person was and where they were in their process. So these are all people who are already parts of this. This isn't a campaign to get new people. It's a campaign to take converted people who already said yes and get them completely ready for festival. Well, it turned out there were maybe 18 to 20 different permutations of what email was appropriate for a particular individual.

[00:42:28] So I had to go through a process using AI, thank God, to one, figure out, you know, what were all those different permutations that mattered. I get all that organized and, you know, Claude's help, we categorize each one of those. And then we wrote a custom, very tight email to each person. Some as simple as, you know, you've done everything you need to do. Here's the next step in the process that we're going to do for you. So that was the simplest.

[00:42:57] And maybe of the 200 and call it, I think it was 40 people, maybe eight were in that category. So eight were like 100% done with everything we needed to be done with. And so it was just thanking them for that and telling them, okay, the next step is going to be this. So you don't have to do anything, falls on our court. And then every permutation from there, some just need to do something little. Some need to do something else that was a little bigger but different. But then there was one else who had a different bigger all the way to the bottom list where they had to do everything.

[00:43:25] Like, we kind of need you to log on and we kind of need you to download the app because don't do those two things. Right. You're not going to get the full experience. But I was able to do all that on my own on a Sunday before I had to get out for a round of golf. It was a little stressful. But they all got one-on-one emails from me that were very custom and spurred everyone advancing their way through their customer journey. I mean, that would be beyond impossible even for a team of people to do right, you know, two years ago, let alone when you guys were starting your company. Right.

[00:43:55] And I think we envision more and more it's like you just shouldn't. So you could be way less. You know, once you there's probably a prompt or there's like some understanding of what your goal is. But you shouldn't even have to get involved. Like, you should be able to kind of put in minimal time doing any of that. Like, that should just be something that I like software and data. I think we're a ways away from that because, you know, unless you set it up. I mean, I think the key is if you set it up right from the very beginning, you know, I can do more and more of the work.

[00:44:22] But, you know, and I'm not kind of – I'm probably in the top 10% of AI, but I'm certainly not in the top 1%. So I probably haven't learned all of the next, you know, level skills. But, you know, getting it to understand what is going on, what all the context is, what you're really trying to accomplish, all the nuances involved. You know, AI needs a lot of help figuring that out and how to draft – you know, even the drafting of notes, which it did a lot of work.

[00:44:52] If I would have let it do all of the work, it would have been ugly. Yeah, totally. Yeah, I think – yeah, there's a lot – I think that there's that gap and then there's the gap of just the – we just generally lack contextual data these days. And I think – and this was true of our hypothesis from the beginning, which is that you need these highly opinionated data stores to know exactly what this data is. It cannot be a – So talk to us about what you mean by highly opinionated data stores because I like that.

[00:45:19] When we started, like the way marketing systems worked at scale is you could upload all this data. You could clean the data and you could say these people have installed the app, these people haven't installed the app. And you could get engineers to build piping between them and it was a giant pain. But like in theory, you could kind of get your head around like, okay, get all my data in one place somehow. And what we have realized over time is basically like humans – there's specific human data.

[00:45:48] So like people take actions at certain points of time and there's a lot of data about those actions. So just looking at an e-commerce brand, it's like I discover the brand on a certain date. There's a day I show up. There's things I do on the website and how many minutes I spend on different products. And then there's – you know, like there are purchases. So at this point in time, I bought this product and then maybe at a different point in time, I returned that product or I didn't return that product. But it's – Some things are facts. They're facts. And – It's an age.

[00:46:14] It's a gender, which I guess it's usually a fact now. Maybe there's a little gray area there. But there's a set of things that are – did this happen? Did that happen? Is it this? Is it that? So that's kind of like one type of data. And especially facts about actions where it's like this – this happened at this point in time. I walked into – I biked to Whole Foods. I walked into Whole Foods. I bought these products. You know, meanwhile, the store has facts. These products were on sale.

[00:46:43] This is – you know. So those facts, like especially where they relate to time, matter a lot. That is not how we approach – like the world approaches data. If you think of like a – you know, it's kind of more our world. But like there's these tools like Snowflake where it's like basically – we think of databases like, hey, let's get all the data in one place. But like dump it somewhere. But that lacks the context of like what is actually – what is this data? What's going on? And the reality, it's like you want to know if you're in a commerce business. Like a purchase is a purchase is a purchase, right?

[00:47:12] Like that – you know exactly what that is. That is the same across every brand. Returns the same across every brand. Like there's no ambiguity about that. And so once we know that, the types of intelligence or things AI can do with it is way cleaner and you need way fewer humans because you can say like I can figure out like if I know all these facts and I know exactly what they are, I don't need a human to weigh in on kind of all the gray areas. I can go out and like run a bunch of algorithms for like your lifetime value or how to treat you or kind of all that intelligence top.

[00:47:42] But even now, whatever, 15 years later, we're not opinionated about data. We're like – we treat it as like just kind of dumb data to dump in one place and we build our data stores. You know, like every sales organization rebuilds their sales force every single time. It's not a lead is a lead is a lead. And you've got to have that – by being opinionated, you can do way more with the data. And opinionated, talk us through that.

[00:48:07] Yeah, so opinionated specifically kind of being super clear on exactly what this piece of data is and have that be a concept that extends across everyone in that organization and ideally across other similar organizations. And so, you know, I've now set up Salesforce a huge number of times.

[00:48:31] It's – if you think about a lead, it's like, okay, there's a point in time when we say lead, we're talking about somebody who's interested in buying a product usually, right? That product could be an event. That product could be a software application. That product could be knives, sold to order. It doesn't really matter. But like underneath that concept are facts, which is like the person hears about the brand for the first time. The person at some point, you know, maybe talks to somebody, maybe visits the website. But those are like very discreet facts.

[00:49:00] The person maybe at some point writes a check, gives a credit card, fills out an invoice or something. Like actually like money is transferred. A lead is like an ambiguous concept atop. All those other things are – there's no – we don't need to debate what they are. It's like they're just like clear things. And so that's where kind of moving from the superficial like we have simplified data to be able to deal with it to let's just understand everything that's actually happened in the world. It's now possible because we can like manage this huge amounts of data.

[00:49:28] And we actually can interact with it because we have AI and other tools to go out and like – it can actually, you know, you take way more data in and do way more things with that data. So let's explore a little bit more of the concept of a lead is opinionated data. That's what you're saying, right? So it's opinionated because – not because all this other kind of black and white data doesn't help you come up with who should be leads.

[00:49:58] But the subjective nature of like, okay, let's be really thoughtful about how to think about how to interpret this data, how to have, you know, AI help us interpret the data so that we have a really tight and well-functioning approach to how we define a lead and act upon the leads. Is that the right way to think about it? I would say that's kind of how it used to work.

[00:50:24] I think the challenge is if you get a bunch of smart people in a room for a business and say like let's lay out exactly what a lead is and be very thoughtful about like, okay, this is what it is. Then we're going to go out and take all our IT systems and we're going to like work them to work around that concept. We're going to train the organization. We're going to do all these things, right? So you're going to put these like very bespoke definitions.

[00:50:46] The challenge is then you lay AI atop that and you both have a lot of employees, new employees, you also have AI trying to understand all that context. And kind of like which is distinct and discreet from all the objective facts that sit underneath that like subjective here is the lead. If you just focus on that and you store them all in a way that you know what that data is, like here are the moments where this person first showed up on our website.

[00:51:16] Here is the first time they clicked an ad. Here is the first time they did this. That is a lot more. That one, there's no debate. Like it's very clear that like I know what happens first time somebody needs a website. Like you know what happens. We don't need to train new people on that. We don't need to train AI on that. We like know that it's just a fact. So be opinionated in the data store there and that actually extends across all types of businesses. So the opinionated part is? Is the data systems, not the people. Okay.

[00:51:46] And so it's more. Opinionated about what data are we going to tune ourselves into and then the data, we don't have to get a bunch of people together and think about who the lead should be. That's right. It's like that will just come out of the data. Right. And they should, the people should spend time on like, okay, what are we going to do? Like these, okay, the data and AI and all these things can identify like, okay, we have these people who are just about to buy. People are really, really good at saying, okay, how are we going to get those people to buy sooner? How do we build stronger relationships?

[00:52:16] So the people should spend time on that strategy. But there's no debate about the underlying data because that's a fact. But the people who spend time on that strategy today aren't going to spend it like they would have spent it five years ago. They're going to spend it with AI. Exactly. What do you think we should do? You know, why do you think, you know, what do you think we should do to convert that from this stage to the next stage? And what do we think is most effective? Why show me the data? So even the human in the loop, if they're trying to do it by talking to each other, they're not leveraging AI at that point. Right, right.

[00:52:46] But to your point on the like, hey, like you, you're, the fact that you wrote these emails on Sunday before, like your, your input there was incredibly valuable. Like if you had just said, hey, I take all this communication off my plate entirely. Like the little bit of involvement by you is where a huge amount of value comes. And I think more and more believe we'll be there for AI.

[00:53:09] We're very far from AI replacing all human input on kind of like how do you treat humans and work with humans. But the same me doing the same thing a year from now is going to be 10 times more powerful a year from now than it was now. Because I'm, you know, I like most people who are even leveraging AI in a big way are still in the, in the steep learning curve. And frankly, the AI is still, I mean, some of the stuff AI does say, it's like, really?

[00:53:37] I mean, there was one day, I probably should admit to this, it was like, I don't know, maybe six days ago or something. I was, I swore to AI four times in like 10 minutes. Like, how the hell are you? I didn't use that word. How the hell do you not know that? We've worked on this four times. You got to know this. And finally, like, okay, yes, I could keep screaming at it. And it does respond because it does protect that. Yeah, you can't scream at it. But at some point, I think it said, well, I guess I'm just not satisfying you.

[00:54:06] So you have to do it some other way. Like, okay, that's, now you're really starting to sound like a human and poor. It's true. It's very true. Smart ass. So, you know, I called our two AI guys. And they're like, okay, well, we're going to actually come to your house, clean up some stuff. It sounds like you got, you know, some shit that's going on in your AI instance. So they came over and cleaned out stuff to create some other problems by cleaning out too much stuff. But, you know, that's not, I'm not going to repeat that a year from now.

[00:54:36] They're not going to let us get into those traps. I think part of it was AI was trying to save itself, you know, processing power or whatever and losing data that should have at its fingertips or know-how that it should have at its fingertips from past sessions that it just erased. So now I was asking to do stuff that it didn't know how to do and all of a sudden it forgot how to do it. It had amnesia.

[00:54:57] So those are, so half of that at least was because of lack of cementing of AI tools. And maybe half of it was I didn't, I was probably working in a part where I should have put somewhere else so that it had more of a permanent memory of the stuff. So, you know, I need to learn some and the tool needs to develop more. Yeah. But it's still crazy. Yeah. So let's get back to, you know, I got you way off, way too many tangents. We still don't even know what the journey of.

[00:55:28] Klaviyo. Klaviyo. Totally. Yeah. So it was January 2012 was when we, you know, kicked off exploring ideas. It was August 2012 when we kind of lasered in on this initial idea.

[00:55:55] And then by December of 2012, we had our first, you know, 15 paying customers. And so at that point, it was clear to my co-founder and I that like, hey, there's something here and this is what we're going to do. And so we were co-CEOs, he was kind of entirely focused on building the product. I was entirely focused on, hey, how do we get more customers? How do we close those customers? And then how do we, you know, provide a great experience and then rinse and repeat over time.

[00:56:25] And so. How special was the relationship between the two of you? I mean, it was, I mean, we were very complimentary in skill sets, which was very helpful. And it was very helpful. You know, he was, is and was like the type of person who can build, you know, do the work of 20 engineers. Like he could lean in and kind of like build an incredible product. And I think that the biggest thing that Andrew and I shared was just deep values alignment, kind of like deep trust.

[00:56:53] And here's why we're doing this. Here's the focus. We're going to focus on customers. We're going to solve a problem for them. We're here to kind of build something great. And that is, that's what matters. And so, you know, I never had to doubt, we would disagree on what to do sometimes, but never on the kind of like the underlying motivation or the why. And that made it tremendously effective to work together. And so it made it easy to resolve conflict.

[00:57:20] It made it easy to, you know, not know, a lot of times I meant disagreeing and committing, but, but it let us kind of do great things together. And we were different enough that, you know, I think there were various things we disagreed on where we'd go with what one of us believed and that ended up being the right call because, but the other thought, hey, ahead of time, that data, that idea was crazy or not going to work or something. So, and then now we, you know, we've been run the whole journey together. So, so the first year.

[00:57:46] It's pretty rare because to be 12 years into it, 13 years into it and two founders still being together is. Yeah. It doesn't always happen that way. It's a, no, it's a. Usually doesn't happen. It's quite a, quite a journey. Um, you know, certainly, uh, you know, he is, um, you know, I still spend a very decent amount of my time at Klaviyo. I'm the chief strategy officer, but, um, a year or two ago got rid of a moderate, got rid of all my direct direct reports.

[00:58:15] I'd been the chief product officer and the chief marketing officer kind of like is on an interim basis, but then said, Hey, I want to step back, not have direct reports. Uh, so he's in my, he's, um, we recently bought on a co-CEO, but he's kind of very much deep, deep, deep in it. And I certainly am spending a good amount of time on Klaviyo, but he's definitely driving, you know, the core force driving it forward. But, but still, you know, um, very honored to have gotten to build a business with him. So, um, so yeah, it was a year in, we were still two of us.

[00:58:44] We said, okay, let's try to get this profitable enough that we can, uh, you know, pay our rent, pay, you know, for anything we might possibly need, but like this business will basically be unkillable. And so by the end of the second year, got to about 250 K in annual annualized SAS revenue. And so still just the two of us at some point in the next year. So we ended the next year around a million in revenue, but, uh, still mostly the two of us though.

[00:59:10] So, uh, we had a theory that if we did every job for as long as possible, we would know exactly which jobs we couldn't automate because we would try to automate everything we did. And we would know exactly what each job took. So we could hire people way better than us to do them. And at some point in that third year, it was very clear that the constraint on growth, um, was, was ourselves. And so we started saying, okay, the first hire we made was a customer support person. So someone to work with our customers. So you guys got to year three with, yeah, just the two of you, just the two of us.

[00:59:40] Um, and so, and at that point, I mean, it's like today I started off. It was great. Yeah. But, but not the growth curve of today. Say hi. But, um, but as you know, it's crazy. It's insanely profitable, but, um, we didn't feel like money wasn't the constraint to growth. Um, and so, but eventually it was, and we, but we had the money. So we just used our money in the bank to hire support person. Soon after, like I couldn't do all the sales demos. I, we hired a couple of salespeople.

[01:00:08] Uh, we needed more people writing code. We hired a few engineers. And so by the end of that third year, we had a newly built team. We were still very profitable and, you know, we had probably hundreds of customers, but it was very clear at that point. You know, I think we thought, Hey, we'd have to quickly jump to other industries. And like, we didn't really care that e-commerce was like the beachhead. We thought that like all businesses should work this way. And we, we still feel that way, but it was clear that e-commerce was bigger than we thought.

[01:00:35] And so we could build a very large business doing exactly what we'd been doing. Um, and so then it really became about, Hey, how do we, how do we scale this? And so how'd you scale it? Yeah. And so we kept, I mean, really it was doing more of, and at this point you have no third party money, no third party money though. At that three year point built the initial team. We started to worry and say, Hey, if, if, uh, if something happened, like something in the macro happens, um, we don't want to have to lay people off or end up in a problem.

[01:01:05] So let's find a very high trust investor who will have the, like provide the exact terms we want, who will let us just keep building this business, um, who we can, you know, really just use to take that money, stick it in the bank and never touch it. And so we asked a couple entrepreneurs in the Boston community, they came back with a name of a specific person at a local firm. We talked to them and I think we closed that money like 24 hours later. So we basically said, here's the money we want. That's all we're looking for. And it was kind of exactly what we wanted.

[01:01:35] So that, um, that investor, um, who we've now known for many years has been great. He, a year later had left that firm, uh, and, uh, he raised a side fund to basically do the same thing for the same reason for us. So we said, Hey, okay, last year we put a million dollars in the bank this year. We want to put more like five. We're not going to spend any of this money. It's just kind of for a rainy day, you know, in case something happens. Um, and we'd raised $5 million again from him.

[01:02:03] And so at that point we were still kind of, um, hiring, doing the same thing and just rinse and repeating. Everyone we were hiring was basically either a salesperson, a customer support person or an engineer. And so kind of like everyone was effectively working on something related to the customer problem. We were way late to building the HR finance, all the other parts of the business. Um, and you know, then in come 2017, maybe, maybe later, um, at some point it kind of in

[01:02:33] like between the 2017, 2019 timeframe, um, actually 2019, we, um, went out and said, okay, now is time. We have a much more established business. We went out and raised a, a much more real round of funding, um, from summit partners in Boston. We were very much more VC oriented than their typical investment, but, um, kind of worked with them on the terms we wanted and exactly wanted to do and raise money from them. A year later, we raised from Excel.

[01:03:01] And at that point it was like very clear we were on a journey to eventually going public. Mm-hmm. And so, um, and really kind of doing, you know, the customers, we went, got more and more smaller customers, more and more bigger customers. At that point, we very much started to build out kind of a real team of, you know, CFO, chief people officer, chief legal, like all the, all the things. Um, we had, you know, hundreds of millions of revenue at this point, still very profitable,

[01:03:28] had kind of really built the business around, um, that kind of ethos of like profitability, but really like, Hey, solve the customer's problem if they pay us and then be very disciplined about building a cash efficient business. Simultaneously Shopify was the platform that ran a lot of these e-commerce businesses. They had really kind of hit this market dominant position. We'd been able to really piggyback and grow with them and, um, raised, uh, again, a kind

[01:03:55] of like pre-public round and then went public in, uh, 2023. And so, um, since 20, like back half of 2023, um, have been kind of living the life of a public, public SaaS company. Yep. And, and how did you go public? Um, just kind of the classic going public process. So we didn't, we didn't need to raise any money, but you didn't, you know, we knew we were of a scale, we were of a scale and a profitability metrics where we knew that we could go public.

[01:04:24] No one was effectively going public. No software companies were going public in that timeframe, but we knew that we were unique in kind of growth scale and profitability. So we, we knew we could do it. We also deeply believed that, um, we were going to go public at some point. And the reason we thought that was one, um, while there are lots of other liquidity options for employees, kind of, there's something very important about being liquid in the way that you are when you're a public company.

[01:04:51] So we wanted to do it for our employees to, uh, we believed we're building a, you know, really aimed to build a generational company, a company that would be really be built to last. And we believe that public companies are, are better, better run. Like just the one, the things you have to do to go public to, um, the, uh, numbers, what you have to report once you're public, the types of people you can attract as a public company. We believed at some point it'd be very important to be a public company.

[01:05:20] And that if you're, if, if you have this mindset, you're doing four employees and have a better run company. There's no time like the present to go. It like removes distraction from your, your employees. You get it out of the way. And even though it was like a bad time to go public, we knew that we could actually go public in that market. Um, and so we did. And what was, when you went public, what was the, what was the valuation at the time? Did you end up raising primary or did you? We, no, we didn't. We knew we didn't need, uh, we may have raised a little primary, but we didn't need, we

[01:05:49] didn't need new primary. Primary is when cash comes into the company. Secondary is when you're selling shares in the company. We, we were at Zaya when we went public, we were in somewhat of a similar situation that we weren't doing it to go raise money per se. We ended up raising some primary, but it was primarily, you know, to get liquidity and primarily to do some secondary to just to create the float. But the goal was, you know, how to raise as little primary as you could, uh, and, you know,

[01:06:18] have the process, public process play its way out. Exactly. We might've been a little bit too much, too focused on minimizing the amount of primary in hindsight, but for some reason at the time, it was like, why bother, you know, raising any more primary than you need to recognizing there'd be a, you know, the go public discount. And we believed, you know, whatever we went public at, we were still on the value creation curve. So it would be a expensive primary money to bring in at the time.

[01:06:46] And so we wanted to, oh, that's interesting. Yeah. Be as tight as possible. Yeah. Yeah. I think very similar. Yeah. Like you, you've got to get the, the size of the IPO big enough to make sense. And so I, the, that's where the kind of primary comes in, but then, um, that's just part of it. So, so at the time our last round had been at nine or 10 billion. And then, um, you know, kind of like in the, your, your pre IPO round or pre IPO round

[01:07:15] the same, uh, initially. And then, you know, we've seen a bunch of spikes. I think the, and then the big spike is kind of, we've been very caught up in the, um, we've SAS, SAS apocalypse, if you will. And so, um, you know, we've, uh, but we've continued to kind of, um, be very happy with, uh, growth. So still growing very fast. So kind of driving, going, you know, growing very profitably, um, or, you know, many times larger, uh, than we were when we went in public.

[01:07:44] Um, and I think your enterprise value when you went public was somewhere around 10, I think. And did you, did you have much debt at the time? Probably not. No, no, no, no, no. Um, you know, of the, in the life of the company of all the money we've raised, it's all gone either to the balance sheet or to employ secondary to secondary investors or other people. The, um, you know, there's about $15 million that we spent of that money of the seven or $800 million that we spent on a marketing, like kind of like a larger scale marketing branding test.

[01:08:13] But, um, basically everything was bootstrapped from the, the cash generated from the company itself. Wow. Congratulations. And how's it, I know there's been the, the apocalypse. Uh, how's that affected the value? So, um, you know, I, the, uh, I'd have to check, but the enterprise value certainly now is less like the stock prices, you know, you know, uh, some percent probably less than when we were public.

[01:08:42] Our belief, our fundamental belief, you know, because we've always had this view of like, we're building this generational company for the longterm. It's the macro is going to change the kind of like how, you know, um, how the market thinks about like the dollars, like, you know, given a given growth rate, given a given, uh, cash efficiency, um, given a given size, the market's going to think about those things in different terms. And that's going to vary day to day, week to week, month to month.

[01:09:08] Um, well, I think the, the SAS apocalypse is related to the uncertainty about which of the SAS business models is going to perform well in this emerging era of AI versus which ones are going to be vulnerable to that. Because certainly if you were starting from scratch, you know, in today's world to do what you did, you would do it completely differently. You'd have a, you know, the power of AI behind it.

[01:09:36] Uh, yet if you're starting from scratch, you would start with no customers, no, you know, no brand, no track record. So to what, you know, so some of the companies that are, I think, you know, in this holding pattern because of the SAS apocalypse are going to come out the other side of it worth a whole bunch more money because they're going to have, uh, proven to themselves and proven to others that their business model is not going to be vulnerable to AI.

[01:10:04] And instead they're going to be able to leverage AI in a way that's going to make their business model far more valuable than it would have been. And others are going to, you know, be the opposite. They're going to, you know, say they're not vulnerable to AI and they are going to be vulnerable to AI. And I don't, you know, I think the, the betting on this, on the public companies is, and in the private markets too, is, you know, who thinks, uh, that, you know, they're going to be worth a whole bunch more. They're the buyers of the stock and who's so concerned.

[01:10:33] They're the sellers and the, you know, the prices kind of mediating, you know, what people, you know, think on both sides of those bets on a company by company basis. And, you know, I think, you know, I think it's going to take a while to sort through though too, because I think, you know, who is really vulnerable to AI is not a hundred percent, you know, obvious at this point and who is going to be propelled forward because AI isn't. But you're going to see separation. You probably already are. I don't follow it that closely, but you're probably already seeing that separation begin to play out.

[01:11:03] Yeah. No, I think that's generally right. You know, I spend a decent amount of my time with public market investors. And I think to your point, we've seen a lot of kind of like broad, broad brush treatment of, Hey, who are going to be the AI winners and losers? And like, what is, what is defensible and what is reproducible with AI? And I think, you know, I think we very much believe, you know, in the same way, like we built the company that to say, Hey, the valuable thing about a company is that you go out and you produce value for your customers.

[01:11:33] And our belief in certainly in marketing, but in kind of like SAS writ large is the, to drive, to really be able to drive a ton of value. You have to provide a lot of intelligence and you have to underneath that, you have to have a lot of data. And what we see with AI is it lets us build software faster, but not a lot of the actual, like, if you think about like those understand the data, getting the data, storing the data,

[01:12:02] using the data, a lot of those things are actually, there's kind of been minimal progress. And I think it's why we see, you know, that we largely haven't seen kind of like, um, fast moving upstarts who have made much progress on that problem, because it actually is a, it's a problem that AI so far to date helps with a lot less than it does with say productivity apps, things like monday.com or like things where, um, there are much thinner layers of what those apps are doing.

[01:12:30] Um, but yeah, it's all going to get sorted out in the next few years. But I think it's also why we, you know, if we build a business that's growing fast, bigger, generating a ton of cash and like also leveraging all the new technologies, don't spend a lot of time worrying about the market cap. Yeah. Yeah. The, um, the companies I think that are most likely to do well in this period of massive

[01:12:57] transformation are ones that, um, one are humble. You know, they really feel that they might be vulnerable, uh, because I think you have to approach it that way. But two are technically very advanced. They're very advanced in terms of understanding how to use data at the core, understanding how to apply AI to that data at the core. I think the ones who are most vulnerable are, and there's a lot of them, are ones who are,

[01:13:26] you know, who don't have a lot of technical DNA in their culture, including at the CEO level. That if the CEO level is, you know, is technology uncomfortable, we'll say, uh, there's a good chance the team under underneath and around that individual, you know, is that way. And in the past that was probably, okay. You could be a sales led CEO, you could be a marketing led CEO, you can be a finance led CEO.

[01:13:56] But I think particularly over the next five years, you know, if you're not really comfortable with technical transformation and betting AI into the culture, to your people, to your mindset, to how you think about, okay, what do we need to be now versus what we were, you are going to be caught way off guard. Yeah. Yeah. Certainly in software. It's definitely true. Oh, I think in the heart, I think in everything, I, I look at the business I'm in, uh, which

[01:14:24] is, you know, on the surface, a hardware business, it's physical networks, it's fiber in the ground, it's, it's optronics, it's running something that's in the real world, but you know, the command and control over data and the interconnectedness of the data is everything there. Interesting. And, you know, and there, the impacts will, will be slower over time because you don't turn a ship like that real quickly. But, but, you know, the winners and losers are going to be based on who's leveraging AI

[01:14:55] now. And then, you know, their ability to demonstrate that they're winning from that might be a three, four, five year time lag. Right. But that's where it's going to be. And, you know, I think there's lots and lots and lots of businesses in that category. And, you know, most of them are going to talk about AI all the time, but some are going to be doing stuff about the AI. Others are going to be talking about AI, you know, whatever. And, uh, they're going to get caught with their pants way down and they're going to be just, they're just going to be tumbled by other companies.

[01:15:25] Yeah. But it's also, it's a good time to both be building the winners. I mean, if the people are going to win, it may not be clear they're winning now, but like they're going to win in the longterm, but also it's a fun time to be building. Like it's like, if you like to build, it's like just, there's a lot of neat stuff to do. That's where you get to like, you guys were doing with, you know, two people for a couple of years and, you know, then 10 people or 15 people. Now, I mean, there's 10 person companies, uh, that will be able to build billion dollar

[01:15:52] businesses more effectively, even in today's world than if they were a 50 person company trying to do it. Cause as long as, you know, it's a cohesive, a cohesive team that works really well together is deeply embedded in the tech and has the mindset you described earlier about like, you know, we've got to solve a real customer's problem that they're willing to pay for. But the people are able to do that with the full power of AI, what they're going to build and build quickly and then be able to maneuver more quickly as well.

[01:16:22] Is, you know, already seeing many examples of that really phenomenal. Yeah. It's exciting. Yeah. So I'm excited. I'm excited about all that. I'm, uh, I'm certainly in the camp that says this is not like every other big technical change. Uh, you know, I think it's demissive to look at AI and say, yeah, it's kind of like the internet and kind of like, you know, the steam engine and kind of like, you know, the wheel like, yes, but no at the same time.

[01:16:51] Cause there's, you know, there's an aspect of AI that's more like, you know, like, uh, splitting the atom, you know, splitting the atom could do a lot of great things, but the atom could also end humankind. Uh, I think AI is a legitimate, you know, where's this really headed and, you know, and, you know, what kind of, uh, challenges is it going to bring to the world? Some are going to be in the form of disrupting people's lives, disrupting their careers.

[01:17:19] And a lot of those people won't be able to react and respond to that. And they are going to be left behind while at the same time it enables others to accelerate forward. But there's also the, you know, AI in the hands of bad actors, like one bad actor of the type we described, who's got that 10 person team can do destructive things that, you know, that 10 people couldn't do in, you know, in the absence of AI and, you know, how's that going to play itself out unclear.

[01:17:47] And I think, you know, there's the ultimate, you know, threat of, you know, AI is undermining, you know, humans. I think that's a real thing that is probably a multi-decade thing. But in the meantime, it's, it's how does AI in the hands of, of, uh, people are doing mischievous stuff with it. You know, where's that going to lead in? Everything from how it affects politics, how it affects elections to how it affects, you know, even, you know, uh, conflict in the world.

[01:18:16] I mean, there's some scary things to be legitimately, uh, you know, talking about and navigating and solving for. Or while at the same time, there's no question in my mind, AI is going to be a source of, of, uh, creating value for people in every corner of the globe. You know, more access to food, more access to medicine, more access to shelter, um, living longer lives, you know, taking on some big challenges like what your, uh, wife has taken

[01:18:45] on, you know, environmental challenges. AI is going to contribute to all that at the same time. But, you know, this is definitely a, you know, a big category of things get very excited about and wishing I was a lot younger and would live a lot longer as well as things to, uh, concern about and say, glad I'm not the generation that's going to have to deal with that. Yeah, that's true. But you know, the time, yeah. To the point, like if you think about entrepreneurship as solving problems, it's just right.

[01:19:13] There's the good stuff that could be done with, in this technological wave. There's the problems it's going to introduce and like maybe at a scale we've never seen. And, um, we just need a lot of great young people building, ideally a big chunk of those in Colorado, but like solving these problems by building new companies and doing new things to, to figure it out. Like this, this is, it's going to be a really exciting 20, 30, 40 years, most likely. All right. So now I want to get into something a little bit more provocative.

[01:19:39] Uh, this would be a way we can kind of, uh, work toward the wrap up. And, uh, so here's what I want to set up, this is a set up. You set out to do some, something good in the world. That was your ambition. And I'm going to be provocative. Yet the only thing you did is you built some software tools to help small businesses make more money. Um, why do you think, and I'm going to believe that that's enormous good.

[01:20:07] So let me tell you right now, you know, I know where my head is on this, but let me put myself in the shoes of people are like, you're just not sure. You know, instead of doing some good for world, you just, you know, built a business that made a lot of money and rich yourself and rich, your partner and rich, your investors and, you know, helped other people make more money and whatever business they were in, you know, selling suits

[01:20:34] to wealthy people, you know, why did you choose not to do good in the world instead? You know, or have you done good in the world? Yeah. And have you set yourself up to do even more good in the world? Yeah. I think it's the right push. And I think I struggled with this a lot going out of, coming out of college where, you know, basically I went to work for a startup that was building kind of like, you know, large scale nostalgia software for very large companies.

[01:20:58] And my belief at the time was, if you think about a lifetime as long and a belief that, hey, by building new things in the world and solving problems, like one, you personally learn a ton. And so being in this rapid, fast growing environment, you know, I was also thinking about jobs in the government. Like this was a way I personally would learn a ton very quickly. And then I would have a full lifetime to go apply those things.

[01:21:24] And I also believed that, hey, on world scale, if everyone is working on different problems, like sure, some of those problems are driving the capitalist machine forward in a more efficient way. Like that is also, that has in a lot of ways provided a lot of the world change that we've seen in the past hundred years. So that's how I thought about the first job. The world change in a good way? Yeah, in a good way. Just like the... Like how does that help the world change in a good way? I mean, I think just as we look at economic development, like the mortality rates being down,

[01:21:52] you know, how much healthier people are like across the world, like the number of people who have access to power, if we think specifically about my wife, it's just all of these things, like economic development has happened in a large measure by... People are living better now, and the dimensions that really matter, you know, across the entirety of the globe, not because we're smarter politicians, but because... That's right. There's a book by Hans Rosen. I forget that it's like hopefulness or something, but basically it's like, if you just look at

[01:22:22] the data of how the world has changed, like we're in a massively better spot over the past few years, and it's largely not because of policy. It's largely because of, you know... Maybe despite policy. Yeah, exactly. And it's like, and some of that is, it's not necessarily for-profit things, but it's because of invention and technology and change. Which is driven by for-profit... That's right. ...at least rewards. It's not clear how much of that is driven by for-profit motivations because...

[01:22:48] And this is, I think, you know, this is maybe complex because people are doing this stuff. They definitely want to see and be appreciative for what they did, including, you know, financially. You'll feel like, you know, the appreciation is there, but most of the people I know who set out to do big things and have done them, it comes from somewhere way deeper than that. I mean, they're really trying to do something that, you know, is leaving the world a different and a better place than had they not have been there.

[01:23:18] And the for-profit motive is part of the equation. You know, not unlike what you were saying earlier. If people are going to take credit card and pay for it, it means you're doing something valuable. It means, you know, you've done something really valuable if you've generated a whole bunch of wealth in the process. But the inner drive for almost everyone I met with, who's certainly the ones who've had success, is coming from a way deeper place than I'm just trying to become rich. Yeah. I could not agree more. You know, I do think...

[01:23:45] So especially early in people's journey, like, I think very few entrepreneurs actually care that much about being rich. I mean, a lot of entrepreneurs care a lot about, like, I want to get to work on the things I want to work on, but I don't, you know, so I do want to solve kind of like a lot of the money problems. The being rich side, now, I think there's a lot of room for entrepreneurs, especially like once they're post-journey as they further in to think about, okay, how do I give back? What are the things I can do with money I've accumulated?

[01:24:13] How do I solve other sort of like more society, like really needed problems in society? So I do think there's like more room for a lot of successful entrepreneurs to spend more time on that later in their careers or even throughout their journey. Like you can't ignore that entirely because I think there is an element of people make all this money and then it's like you've spent all this time building company and like you don't, the money just sits there. But, you know, as we thought about Klaviyo, like our customers are entrepreneurs for the most part.

[01:24:40] And that's like, it's very satisfying to watch other people go out and build things in the world. And so I think it's been very easy to say like, hey, the types of people we work with are builders and it's very satisfying to do great things for them. Um, now I do think there's a lot of other big, like when you talk about all the AI problems, there's those, there's poverty, there's energy. There's a lot of problems that need great people solving them via entrepreneurship.

[01:25:05] Um, and I think both myself and other people who've started companies just thinking about how we and they, and everyone spends their time and their money. Um, those are the types of problems that we just need more people working on, but also like local problems. Like how do we build great entrepreneurial communities like what you see in Colorado? Yeah. The, I'm going to use one other analogy, just to maybe illustrate a point. So it might be a little more kludgy.

[01:25:31] Um, but a naysayer could say that in what you were doing and because of the success you had, you cost a lot of people jobs because those companies that you were serving, had they had to send all those custom emails out with people. They would have needed a lot more people and therefore they would employ a lot more people and the people would have been a lot more valuable. And you, by automating a lot of that, you know, took a lot of jobs away from who they otherwise would hire.

[01:25:59] And the reason I bring that up is that's kind of like an AI argument today, like AI is taking jobs away. Like how would you respond to, did you, by helping those small businesses get much better what they're doing and, and survive and make money and do better? Were you taking away people's jobs or would you have a different response to that? Yeah. I think it's, it's so easy to, to take these very simplistic views. And I, and I think it's like, you know, if we look at, if we look at like a lot of what's going on the Klaviyo customers is you used to have these massive consumer products companies that just had all the power.

[01:26:29] Like every, every product on the shelf in a target would be built by Procter & Gamble or made by Procter & Gamble. All of a sudden you have people in all over the country, like people in, you know, we've got customers in say like Kansas city who had a vision, built a company, have now hired a bunch of people in those communities. And rather than these like huge organizations having monopolies, you have kind of entrepreneurs who's who have built these incredible businesses all over and net.

[01:26:54] I think, you know, it's not like we're not, um, we're not seeing that there are fewer jobs invariably like the more economic development is there are more jobs. And it's, it's what we saw with, you know, the overused example of accountants in the eighties when you introduced Microsoft Excel, but like we thought accounts were going to go away and you know what, there's way more accounts than there ever were. And so, you know, with the world writ large, it's kind of like the more people out able to build productive things and, you know, the more jobs we can provide, um, it's hugely powerful.

[01:27:23] So at some point along the Klaviyo journey, I started another company called Team Engine, which we, um, we had this realization that there's... Building a $10 billion public company with two people. I mean, it didn't keep you busy enough. I just liked to build, but the insight we had was that there's a bunch of underemployed people who, you know, will go work for like a Walmart or whoever, and they'll make 15 bucks an hour, but they could actually be, you know, there's, and meanwhile, there's a bunch of small factories paying 30 bucks an hour.

[01:27:52] And, uh, those two people never find each other. So the factories don't have enough people. The, you know, blue collar workers make $15 an hour. And we basically built a kind of like AI forward algorithmic platform to kind of like find the $15, you know, the, the people who are underemployed match them with the local businesses and get them jobs. And the whole thing's totally worked. Um, it's turned into this like profitable, small SaaS business. Um, but there's just like something tremendously valuable in driving the economic, uh, you know,

[01:28:22] uh, engine forward and like getting people great jobs. And, you know, there's a lot of bad that can come that we need to work on too, but it's just in itself, like providing opportunities to people is incredibly valuable. Yeah. Cause what I would say in my somewhat kludgy, uh, semi analogy to what discussions are taking place today is by you helping those smaller companies be more effective. You weren't, it wasn't like they were going to go and hire 10 people to do this. They would have never done that.

[01:28:48] What they would have done is not been as effective, which means maybe they would have stayed in business. Maybe they wouldn't have, maybe they would have, uh, grown, but at a slower rate, you were helping the ones who were creative, innovative, ambitious. You were giving them tools so that they could, uh, they could create, you know, whatever they were creating at a bigger scale and create more competitive pressure on kind of the ones that established more of a monopoly position. You were really feeding kind of that next generation of businesses.

[01:29:18] And, and although it's harder to kind of put your finger and said, you know, does that mean you created more jobs? Aggregate answers. Hell yes. I mean, that's, that's the progression we talked about earlier that, that the, you know, the application of technology, which is what entrepreneurship has unleashed has largely led to greater good across the world. People are living longer. They're healthier. They're eating better. They've got more shelter. They've got more clothing.

[01:29:47] They've got more opportunities in aggregate, you know, in every corner of the world than they would have had, you know, 10, 20, 50, a hundred thousand years ago. And, and that, you know, I think to say that's the result of a whole bunch of things, I think, um, is the wrong answer. I think it's because of basically one thing, and that is innovation and the ability to drive innovation. And most of that innovation is highly correlated to where economic rewards come with that.

[01:30:16] And I'm going to do one more, uh, one more analogy to leave this off just because this just came to me. You know, you'll hear like when a sports star, uh, his team wins the World Series or her team, uh, wins the Olympic competition. And when you talk to them, they talk about, that's why I worked so hard so I could, you know, be part of a team that won this.

[01:30:44] You get an Olympic gold and you believe them, right? You believe them that that's what drove them from the very beginning all the way to the very end. But you also know that, you know, the star of the team that won the World Series is going to make a whole bunch of money, you know, and is positioned him or herself to make a whole bunch of money in the future. And those don't seem like two conflicting outcomes. They seem like, you know, appropriate combinations.

[01:31:10] But you don't question that, oh yeah, sure you really want to win a World Series. All you cared about is making money. That's why you work so hard to be, you know, football players. Like, eh, I'm not so sure that's, in fact, I'm quite convinced that's not the case. And I think that's kind of what we were talking about. Like, a lot of what drove you in your very beginning of your story, like I wanted to do some good in the world. That's why I wanted to be an entrepreneur. I think, you know, you did, you have done good and you're going to do more good in the

[01:31:36] future by creating a very successful business that helped make the economy function better and helped a lot of other businesses kind of find their way. And I think that alone should be something we recognize as being, you know, what is driving humans, humankind forward in a positive way. Yeah. Yeah. I mean, I think you're a good example of this. There's just a time though where, whether it's sports star or it's entrepreneurs, like

[01:32:05] also anything you can do to get back. Like you have that success or you have these things happen and then you take that and figure out how you can keep pushing the community, the, you know, other, the next generation forward to do the new things. So it's exciting to watch. Absolutely. No, I think there's a sense of responsibility that comes with that. And, you know, sense of purpose. And frankly, you know, you feel good doing it. So there's a sense of selfishness that comes with it too. And I think that's okay. Yeah. It's a mutually beneficial virtual cycle, which is good. For certain. Well, thank you so much.

[01:32:35] It's so cool having you as yet another example of, and your wife as an example of people who've somehow found their way to Boulder and to Colorado and are, you know, have done crazy, interesting stuff in the past and continue to do and aspire to do crazy, interesting stuff in the future. And I think that's part of what makes this whole community so special. Yeah. Thanks for having me. All right. It's been good to know you. Totally. Thank you for listening to this episode of The Bear Roars. Check out Stretch, the new song from Dan Caruso with music by Jason Mendelsohn, available

[01:33:04] now on all streaming services. If you enjoyed the episode, please like and subscribe on your listening platform. This podcast was produced by Loud Bear Productions and edited by Hannah Schmetzer with support from Kendall Weinberg, Alex Kim and Gibson Siegert. Thank you.