Semantic Leverage | Albert Mangahas (Roo)

Speaker A: Roll into it. Albert Mangajas. Sir, we're in Vancouver. How often do you come to Vancouver?

Speaker B: Uh, this is my first time. Oh, yeah, it's your first time. My wife is actually originally from Toronto, so, uh, I always love an excuse to go to Canada, so. Oh, yeah, it's a little bit further from Toronto, but Canada, uh, nonetheless, I love it.

Speaker A: How often do you guys make it?

Speaker B: Oh, it's been a while. Last, uh, time we were in Toronto was pre Covid.

Speaker A: Wow. A long time ago.

Speaker B: Yeah, it's been a while. So. So, um, good to. To be back in Canada, but can't wait to go back with the family.

Speaker A: So. She doesn't usually visit the Great White North.

Speaker B: Uh, not as often as she'd like. Uh, so. But I think we're. We're overdue for a trip.

Speaker A: Okay. There you go. Well, it's great to have you here. So I'm excited for today's conversation. I mean, we had, I think a podcast like a year ago.

Speaker B: Yeah.

Speaker A: Uh, yeah, right.

Speaker B: Something like that. Yeah. Time has flown by, but.

Speaker A: And we've only. We only did one.

Speaker B: We only did one.

Speaker A: Yeah, yeah, but it was. It was great and it really warranted a second one. Yeah, right. But now on every level, we're kind of upgrading it, so.

Speaker B: Can't wait.

Speaker A: Um, we're also catching you at an interesting time in your career, which I'm sure we'll talk about. Right. But just for the scope of this conversation, as I mentioned to you before, so a lot happened in the past 25 years in business, digital, uh, data, AI. It's kind of been crazy. There are fascinating stories in there. And, uh, I just wanted to take this opportunity to review that whole story. And, uh, I get to do it from your perspective, which is going to be a lot of fun. So that's what I would like to do today. And maybe let's just turn back the clock to the Matrix comes out in 1999. I don't know why, but this is my new anchor for some sort of turning point. Uh, 2000. Right. Where are you in 2000?

Speaker B: Uh, 2000. I am still in the middle of university. Uh, uh, at the time, um, and I was studying at usc. Ah. Or University of Southern California down in Los Angeles. Um, and at that time, um, actually interesting. I started off as like a computer science major. Loved, uh, it. But my roommate at the time convinced me to pursue industrial engineering. Uh, I had no idea what it was. Uh, but this idea, the concept of trying, uh, to make things better really, uh, resonated um, and obviously there's a lot of different applications of industrial engineering. But, uh, yeah, that led me to industrial engineering and from there, uh, the kind of career kind of evolved in a lot of interesting ways.

Speaker A: Uh, why did you get into computer science to begin with? What was that?

Speaker B: Uh, I think really the impetus of this was, uh, my dad was a, uh, computer programmer at sunkist for like 25 years straight. Uh, and I actually had no idea what that meant growing up. Uh, but always got intrigued by it. Um, and it just felt like something, uh, where I do enjoy math, enjoy problem solving, and felt like the right thing to do. Um, I still enjoyed it. Um, I actually enjoyed all the classes. But again, I think I started to evolve. Like, oh, I, I don't know how I feel about just like being just pure coding all the time. How do I think about actually improving or actually optimizing things and actually solving problems was super, super interesting. Uh, and so that led to again this interesting role or interesting, uh, major, uh, of industrial engineering. Uh, and then the most random part, there was just one class. There was a SQL class. And so the one SQL class, uh, I kind of fell in love with it. It was this idea of data and then now data and how to improve things. Intersection of that kind uh, of was the start of my career.

Speaker A: I love that because everyone has their story of how did you come to data? Their own little love story. And you know, some of them are, well, I encounter a sequel.

Speaker B: It was it, yeah, it was just one sequel class. I, I, I didn't know what to expect. Um, but, um, some people are, are not as excited by it, but I, I was just.

Speaker A: What made you excited about it?

Speaker B: Uh, I think it just, it allowed me to really think through breaking down the problem more. Um, and it's like, um, not only understanding, oh, here's a business, here's a business problem. When you start to look at the building blocks of a business, all of it kind of is grounded in the actual data and how do you analyze it, how is it stored and all these things that maybe people don't care about, but it is foundational to, at least for me, the way that I like to operate is like really understanding kind of the building pieces, the building blocks, and that's data. Um, and so that's been exciting to have that introduction to it. But then that led to kind, uh, of a career, uh, in the consulting auditing world when my first job out of college was at price waterhouse or PwC.

Speaker A: Um, and I actually don't know what you did at PwC. I'm going to ask you, but before we go there. So that's interesting because hearing it from that angle of, at the end of the day, a business, I mean, what is a business? Right? Where is the business? Is it the brick and mortar of the headquarters? Is it the, you know, people working there? But they are there only for, you know, a time. So where is, where does this business live? What is the business? And then, you know, through a lens it is ones and zeros. Yeah, that's what it is. Right, exactly. Uh, and I love this angle because it's not just about, okay, you keep data on your, you know, cloud, so there's digital data, but a layer underneath that there's a numerical reality of the business. It's like not just computer science, digital tech, but on the level of mathematics. Right, that's right. So this is very interesting to me and also how industrial engineering was something that kind of spurred your imagination almost because their technology can actually shape the, like the real world. Yeah, right. So we're just working on um, you know, a project with Caterpillar. They're also joining the masterclass by the way, so.

Speaker B: Super excited.

Speaker A: Yeah, yeah, yeah. It's one of those cool companies, right? That's what they do. But then you got to something less brick and mortar which was consulting PwC. How did that happen?

Speaker B: Uh, so while at USC, ah, I think one of my, uh, one of my jobs was actually working while going through university is at working at the career center. Um, and so it was actually an interesting thing where I help organize all the companies come visiting, um, that help organize interviews that are on campus. Um, and it just so happened PDBC was coming to interview. Um, I kind of just did it to start practicing. I actually had no idea what they did either, uh, or what I was going to be doing. But I went through the process as kind of a practice interview. Um, um, but I kind of learned in the interview process as well as my own research, uh, prepping for the interview. But uh, it was a data role. And since at the time I took that SQL class, I was like, cool, this is something I actually would enjoy. Um, but that led to an internship, uh, at PwC, uh, over the summer, uh, and that was in San Francisco. I was in LA at the time. And so I was able to do an internship, uh, that summer of 2006, uh, that led to a full time offer to rejoin that team after I graduated. So it was uh, comforting at least having a job kind of secured for my senior year.

Speaker A: Yeah, that's nice.

Speaker B: And yeah, I started full time, ah, in the summer of 27 or 2007.

Speaker A: That was because it's kind of like the, the story of. Oh, well, how did you join PwC? I just stumbled into it. Yeah, it's like basically threw the job at me. Yeah, I mean, uh, definitely, because at that time. So what year is that when you start your.

Speaker B: I started with a full time, uh, gig was in 2007.

Speaker A: Okay, so we're going, we're going like from 2000. It's like seven years education and internship and all that. How long were you at university?

Speaker B: Like how long? Uh, that was. I was there for four years. So I started in, I guess that would be 2003.

Speaker A: Ah, okay, got it, got it. Okay. So yeah, because uh, before that it was like a 2000 where, uh, that

Speaker B: was then still in high school.

Speaker A: Okay. And I just want to put together the, the timeline here. Okay, so at PwC, um, you spent how many years?

Speaker B: Six and a half. Six.

Speaker A: And okay, so how did that role evolve? What did you do first? And then.

Speaker B: Yeah, so from, from the data side is something that I enjoyed, but it was a really interesting role because, uh, it was at the first portion of. It was really focused around PwC does a lot of financial audits. Uh, they're part of the big four. Uh, and so the team that I joined was really focused on the data component of that. And as companies uh, are going through their financial audits, there's uh, probably not exciting for most, but this notion of uh, kind of validating and going through all of the financials and the transactions, making sure it's complete and accurate. And that was effectively like a boot camp for me. It was like nonstop, over a hundred companies getting a bunch of transactional data. Completeness and accuracy, completeness and accuracy. Um, but it built this foundation of kind of one of my principles around kind of this, this trust in data is kind of always has to come first. Like you can't do anything else, you can't move on. Uh, you can't make decisions unless it's complete and accurate. Um, and so uh, whether that's brainwashing or I just was programmed.

Speaker A: This is so fun because I never discovered this thread by the way, but my best friend from high school, we uh, were like best friends but we took very different brass. You know, he was like, uh, shout out to Balaj, uh, here. So uh, I think he works at the UN now. And uh, very different. You know, he like studied all night and was like very diligent got into the best, like finance school in Hungary. Right. And then he just went on to obviously the PwC career and all that. And then I was like, very different. But we remain really good friends over the years. And uh, when I, he was like burned out at PwC. So that was always like one of those. When we came together with the high school friends.

Speaker B: Sure.

Speaker A: And we're catching up. Well, Balaj is depressed because he's at PwC. What's happening at PwC? So how he explained to me what PwC does. And I'm not an audit expert. Right. But it's like, okay, what does it do? And he's like, well, you know, the company needs to. Companies need to know their numbers and books are in line. Right. They are good and accurate and all that. So they need to prove that. So, you know, they hire a company and then. Okay, okay. And uh, basically, who pays that company to do that audit, that check on you? Well, you pay them. And he was like just that concept too. I remember, obviously the daily reality of working for PwC was grueling, but that component also, with all the issues contributed to that. Right. So at PwC though, there is this kind of very advanced focus on numerical accuracy. Right. And then they are unleashing technology at that problem. Right?

Speaker B: That's right.

Speaker A: So what did you learn there? Skills over six years?

Speaker B: Yeah, I think it was a variety of things. And again, there's, uh, PDBC has a lot of different practices, but I think the more simplistic view of it, there's kind of the um, financial audit or external audit component, which was exactly that. Where public companies are required to, to go through audits. Um, and as part of that you have to pay usually one of the big four, uh, to do. Go through that, that whole experience. And you do it every single year. Uh, and there's quarterly process and so there's always a ton of work, um, there. And then there's the other component, uh, which is also they do consulting and advisory work. So they're not doing anything from an audit standpoint, but then they're helping consult on all kinds of different things.

Speaker A: And is it a whole different business arm for all of them?

Speaker B: Yeah, yeah, there are. And then within that there's, there's all these types of specialties by industry to different, different technologies to, uh. And at the time, this is where, uh, you know, all the financial erps, there's experts. When SAP, Oracle and all these other financial, uh, systems were either either from an audit standpoint or from a consulting Standpoint of, uh. Oh, we just went through. Ah, ah, a merger with another company or require another company. How do we think about that transformation? Um, had an experience on kind of working on both sides of that.

Speaker A: Um, can we just spend a minute? Because this is really cool on the Big Four, just a little bit, because you work for one of them. Right. And again, the people who will listen to this, some of them are data professionals, some of them are just business leaders. So the big four. So we have, uh, ey, we have PwC, we have McKinsey. Right. And. And is it BCG?

Speaker B: Uh, Deloitte.

Speaker A: Deloitte. Deloitte. Deloitte, yes. BCG is just there kind of. They're also.

Speaker B: Yeah, there's. I mean, there's a management consulting firms as well, which. Then there's the, uh, McKinsey's of the world as well. Yeah.

Speaker A: So McKinsey and Deloitte, do they do audits?

Speaker B: McKinsey, um, McKinsey does more the management consulting and more the advisory work.

Speaker A: Uh, do they offer audit at all?

Speaker B: I don't, I don't believe so. It was, I think the Big Four are, uh. Now it's like going back memory lane here. It was Deloitte, uh, KPMG, KPMG, EY, and PwC.

Speaker A: Oh, okay, so McKinsey's not even there.

Speaker B: I mean, there was still a big player, but not.

Speaker A: Yeah, okay, got it, got it, got it. Okay, okay, interesting. And then, so what years are we talking about starting at, uh, PwC and then ending.

Speaker B: So 2007 to 2013.

Speaker A: Okay, okay, so 2013. By 2013, we had the whole mobile proliferation, digital revolution and all that. So how did that wave catch you at PwC? And then where did that drive your career forward?

Speaker B: Yeah, so I think this is where what made it really interesting is that, uh, PwC was a great foundation for everything I was doing. Uh, but again, it was just kind of parachuting into a lot of these different companies and you'd come in to solve a very specific problem, sometimes for a week, sometimes for four months. Um, and as I was seeing kind of this transformation happening, uh, with what's happening with technology and just, just the world and business, that's where I really kind of forced me to start thinking about, you know, what other opportunities can I. Should I start to lean into and actually join a company and actually build a team and be part of that. Um, and so that led me to joining Green Dot, which at the time, uh, is an interesting business because it's a prepaid debit card business. So they sell physical prepaid cards in a retail store, but it's a financial services product that also has a mobile application. And so it was an interesting hybrid of multiple businesses all in one of retail, uh, uh, finance and technology. So it's this retail fintech kind of hybrid public company at the time. Um, but yeah, that really spurred my, My desire uh, to really like, how do I, uh, immerse myself in this ah, new world that we're, we're moving in. Um, and yeah, it was, it was quite a transition, but it was quite a ride from starting with a team of one and over the years building that team out to almost 20 by the time I left.

Speaker A: Yeah, actually. And how long, how long did you spend there?

Speaker B: Uh, that was about two and a half years.

Speaker A: Two and a half years. Okay. And you said that it was already a public company.

Speaker B: Yeah, it was.

Speaker A: Okay. I just. Oh, okay. For some reason I thought that they were smaller.

Speaker B: Uh, but yeah, no, they've been a public company. Um, and again it's this a, ah, great product in terms of just uh, from a financial services standpoint, not everyone, uh, there's kind of a whole population that's underbanked. And uh, this allowed uh, this customer base to be able to have access to an actual debit card and something that you can easily get, um, uh, quite literally right at the grocery store.

Speaker A: And is this a continuous need still today? Like, yeah. And what are their. At that size of business? You know, public company, but it's not like, you know, it's not Caterpillar.

Speaker B: That's right.

Speaker A: Right. So at that size, what are, first of all, what are their goals? Like what, what do they have as a company? Vision and goal and, and what are their main challenges?

Speaker B: Yeah, I mean it was really, uh, trying to unlock uh, you know, the financial, like financial products for uh, a large portion of the US Population that again, it doesn't fit into the traditional banking system, um, whether uh, they didn't have enough credit or they didn't know how to build credit. And this kind of, this gap where, okay, well, I can get this financial product, um, kind of help improve that financial literacy, but also provide ways that um, they can then just get direct deposit right into their account to then having a mobile banking application. At the time though, which was um, kind of still evolving, uh, back in 2013. So um, that was uh, kind of the goal. And I think the other part that kind of uh, it expanded to is then it's just how do we um, help, uh, some of These customers, um, kind of continue to evolve and to start improving their credit. So there's the credit building tools and all these other new features that were starting to get explored over time.

Speaker A: Yeah. And actually fintech has been one of the big buzzwords of the past 25 years. And we have some of these. I mean, AI is the biggest already. Right?

Speaker B: That's right.

Speaker A: But then we have, you know, big data, we have fintech. You know, we have other stuff too, from, you know, Iot to 3D printing and all that. Fintech is a big one. Right. So, um, with fintech at the time, right, what was the, what was the most exciting, like basically innovation wave when you were there?

Speaker B: I think it was this, this transformation of, um, like the mobile banking apps. Uh, and this idea of like banking from your phone was still like in this evolutional, uh, state. I think, uh, all the banks were obviously everyone was starting to create all their different apps. Um, but I think this, this idea of making it really, really easy to do that, um, and not being tied to the traditional means. And part of this was like some of the technology would be, uh, thinking about, uh, well, I don't need to go to an atm, can I just go to any grocery store and be able to add money to my account or extract money from my account? And so, like, small little innovations like this of like, how do we get creative around, uh, um, expanding kind of the footprint of how people can access their money, um, to um, other interesting innovations where there was a great partnership actually with Green, uh, Dot and Uber to uh, do instant pay. This was that kind of the notion of like when someone completes a trip, well, how long does it take for that driver to get their money? Um, and that can be a challenge when.

Speaker A: Yeah, right. You need to shorten that time. It's not as simple. It's not as absolutely.

Speaker B: Yeah. Some of them are, they're reliving off of this as their income and waiting for some delayed, uh, amount of time. Um, it's, it's not optimal.

Speaker A: Yeah.

Speaker B: And so that, that was again, uh, the necessity and the evolution of what was happening. And Uber was starting to really, really grow, uh, over, over the years, uh, as everyone knows. But, uh, Instant Pay was one of the, those features that Green Dot, uh, partnered with, with Uber.

Speaker A: It's more complex than people think. Right. Like Kate always tells that story. She told it in Austin and I think also in Nashville that she was working before Expedia and before Miro, uh, working for a small, uh, fintech company doing analytics and their whole Business vision and model was to lower the cost of transactions drastically compared to Stripe. So small fintech company, they moved Target a few times, but I was like, this is the opportunity. We can do it. And then Kate did some investigations into the whole, uh, regulatory and many, many levers and many, many players which made it mathematically impossible to push it down below a certain standard. And she had to like tell the company, the company leadership and basically break all those dreams and aspirations. But they pivoted.

Speaker B: Right.

Speaker A: But it just shows like how important it can be that someone can actually read the numbers.

Speaker B: Absolutely right.

Speaker A: Um, so why did you. What came after for you and what year are we talking about here?

Speaker B: So now, yeah, from Green Dot again was from 13 to 13.

Speaker A: Okay.

Speaker B: About 2016. Um, and I think a uh, great run at Green Dot again scaling. When I joined, it was a team of one. Uh, and actually the scope was, was limited. It was just focused on their biggest account, which was Walmart.

Speaker A: Well, okay, Walmart wasn't the biggest.

Speaker B: And the, the product itself was the Walmart Money Card, which, which still exists today, where you can go get this prepaid debit card.

Speaker A: It was the biggest account.

Speaker B: Uh, the biggest account. Um, but doing the, the great work there where it was evolving really my role from a business intelligence like reporting perspective and evolving that to like actually driving insights. Um, and that allowed me to explore and expand my role, to actually centralize the team and have coverage across the. All the different products across all the different retailers. And there uh, are some tax products, the um, and different partnerships with Uber for example. But uh, that was at the end of 2016 when I was looking to um, kind of explore other opportunities. And I think at the tail end of my time at at Green Dot, I actually was working a lot more closely with our finance team and FP&A. And I was really intrigued of um, demystifying kind of the black box that at least where I was at the time. And they had good intentions but like they had their financial model, they're doing their thing, they refresh their forecasts and they, you know, they're going through the rhythms of what they need to do from a financial planning standpoint. But how do we connect the dots with that back to the rest of the business and where does data play a role? Um, and so I found, uh, I found out about Turo was hiring two different roles at the time. Uh, I was ahead of FP and a role and a head of analytics. Uh, role in the head of analytics. That's kind of my traditional path But I was like, I'm just gonna apply to both, and let's see what happens.

Speaker A: Um, well, someone can do both jobs, right? Yeah, we have Albert, but we can't decide soon to be the head of analytics ahead of fba.

Speaker B: And that eventually, as the conversation went, they were like, well, is there one that you want to do? Like, I'm interested in doing both. And I was like, you know, where do you think I can provide the most value? And I. That's. I just want to help. Uh, I love the idea of the business was super interesting. They're like, oh, why don't you do both? And I was like, sounds great. Uh, I did. And I actually didn't realize at the time that I actually signed up for three roles because, uh, that was fine. There were. It was a Series C company at the time, a little over. A little over 100 people. Um, I was also, since I was head of fpa, also, uh, playing a large part of the fundraising process for the next round of fundraising for the Series D. So it was fundraising, leading the FP&A team, as well as leading analytics. Um, and, uh, yeah, really great experience.

Speaker A: So it's funny because you're gaining experience in so many areas. I didn't even know that you practically worked in the Walmart ecosystem. Yeah, that's huge.

Speaker B: Yeah.

Speaker A: You know, uh, especially retail is like one of the front runners of analytics applications. Absolutely right. And you get. Be there with, you know, Walmart Best Buy. Right. They were huge. Um, when the whole big data thing emerged, like, retailers were like, flooding the news cycle with, you know, how. How well they are doing in that area now. So we have you joining Turo, right, in the. In this role. So before we dive into that, because a lot of the viewers will. Would have worked for an enterprise all their lives, work in different businesses, different roles. Can we just do a very quick, uh, view on this whole. What stage the company's in? Series A, Series B, you know, pre seed. Because we talk about business models and we'll look at also later on. Spoiler alert, Facebook. Right. Where you'll spend a little bit of time, which we'll talk about. But I want to look at these businesses from. Okay, what do they do? How do they operate? What are their goals? What problems are they trying to solve? And then how the digital transformation and data and AI play a role in that over the years. So can you just go through that because you had the view on this investment world as well? Yeah. Right. So how does it look like, just for those who don't yeah.

Speaker B: And specifically for Turo here first or.

Speaker A: Well also just in general. First of all companies like. Yeah, let's look at Turo as an example of you know, how did Turo evolve over the years?

Speaker B: Yeah, so Turo, uh, again I was fortunate to join in 20, uh, 2016, uh again that Series C. And for those of uh again as a startup uh, you start with your seed round and over time as you scale and uh, prove product market fit and all these things you start to gain uh, traction and you kind of progress through these later stages. Um and I had joined relatively about a year or so after the uh, Series C. Um and uh, I guess the way that things have evolved uh, there as a team of a little over a hundred, it was super fascinating to me in terms of how they were thinking through not only the business but how much um, they really relied on data for decision making. And it was kind of new to me thinking through it in a different lens as a startup where working at a public company um, a lot of the emphasis was really just like business intelligence. At the time it was, everything was just reporting. Understand what's happening, uh, what happened with the numbers yesterday. Turo when I joined there was actually uh, a huge contingency of amazing data scientists that were already thinking more predictive. How are they thinking about uh, what's going to happen in the future? How do we think about models that are going to help us uh, optimize the project experience. And uh, that really, really attracted me or got me excited in terms of how to think about uh, data being used in a lot more creative ways. But then to scale a business that at the time was uh, a Series C and just kind um of an unknown product at the time of Arturo, which was you know, peer to peer car sharing which uh, at the time was a really foreign idea. This idea.

Speaker A: That's what I wanted to also talk to, talk to you about like because it's nine years at Turo again like spoiler alert with a little bit of an intermezzo. But what does Turo do? Because actually most people I ask now do you know Turo? They're like oh yeah, yeah. So it's, it's kind of filling the public uh, consciousness now. Right, right. But what does Turo do? And then where were they as an organization from a business perspective? What was the, what was the value proposition? Uh, who were they like trying to get buy in from? From the end users to the investors. Right. So what was the m, the, the main idea? What was the big idea with Turo. And then what stage were they at of making it happen when you joined?

Speaker B: Yeah, so the idea, uh, or Turo is the world's largest peer to peer car sharing marketplace. But the, the idea kind of the impetus of this. And the founder, uh, Shelby Clark, who, uh, there's an infamous story of him, uh, when he was uh, in, in Cambridge in the Boston area, um, I think he was trying to get a car but he couldn't. Uh, and he was just biking and he saw all of these cars just parked on the street. He was like, wouldn't it be great if I could just like rent one of these cars? Like they're just literally sitting there doing nothing. And so this idea of Peter per car sharing kind of was uh, invented and created and it seemed crazy at uh, the time. I mean this idea of, of home sharing, uh, has, is not like a new idea. And obviously folks can uh, have been going through that. But this idea of sharing a very probably second most expensive asset, uh, other than owning a home is a car and sharing a car with a stranger, everyone, um, thought they were crazy. Uh, and I've heard it even, uh, when I was going through the fundraising discussion with investors like, who would do this. Um, but the idea is though, is that a lot of these cars are sitting underutilized. Um, 85% of the time they're just sitting in your driveway and the parking lot. Um, and it's a depreciating asset. Unlike real estate, which in most cases, hopefully, uh, it's appreciating. But here, once you buy it and drive a car off the lot, it's already depreciated and will continue to depreciate. Um, and so the value proposition from the host standpoint, these are uh, uh, consumers who are looking to share their vehicle with, with someone on Turo, is that you can actually transform the economics of car ownership where it's not, you're not losing money, but you can actually make it uh, a source of income and uh, a little bit different from ride sharing and all these things where you actually physically have to be in the car. Well here it's not 100% passive, but way more passive in a way where, oh, okay, I can share the car with someone. I'm going to make income. Otherwise it would just be sitting here in my driveway. Um, and that kind of really created this really unique economic opportunity with a low barrier of entry for host on the guest side. Um, really what we're competing with was big rental, uh, traditional car rental. Um, and I Can speak from my own experience, but probably for others. Many people who rent a car, there's a necessity for it. But no one goes out of that experience thinking, oh, I had the best experience. Let me tell you about my time with this car rental company.

Speaker A: Uh, I do have one. Might I just add that I do have one. Yeah, right. Which is, uh, maybe I'm just gonna plug it in here. So I was flying to Rhode island for a baptism, and I flew through New York. Right, right. And I, in advance, was, like, very prudent about the. The car. And I was like, okay, it's just a car, you know, just like, I didn't even know it was probably some, you know, suv. Uh, let's just stay prudent. I remember, like, being very mindful of that. But then as I was getting in the car, the guy was walking me. Enterprise, by the way. They're not sponsoring this show. Uh, the guy was walking me, uh, to my car. And he says, oh, yeah, so we can give you this, this, and that. And I'm like, I was joking. That, um. Oh, it's the challenger. You said. Yeah, right. And then he looks at me and he says, you want the challenger? I'm like, is it even possible? And then I can give you the challenger. I was like, okay, cool. So this drive to Rhode island just became way more fun. Right?

Speaker B: That's amazing.

Speaker A: So that was a good. That was a good experience.

Speaker B: That is, uh, but that's not every experience.

Speaker A: Exactly.

Speaker B: But I think the thing to highlight there is that you go and book with, uh, a big rental, traditional car rental company. You actually don't know what you're going to get.

Speaker A: Yeah, yeah.

Speaker B: You're booking a category, sometimes you're lucky and you get an awesome upgrade or something unique. Uh, most cases it's like, um, you really, like, sometimes you just need a carb and you get this massive moving truck. And it's like, that's not helpful at all. Um, and so from a guest standpoint, um, folks that are looking to run on Turo, this is where you actually are getting the exact car that you want to get. And it's that, that color, that trim, that style. Um, and it's extraordinary. Ah, selection. It was over 1500 mix and models. And so so many different use cases of, like, if I just need a simple car, great. If I want to have something a little bit more luxurious, that's also great. I've got four kids. If I want to jam everyone into a car, I always go with the minivan. Um, and so the options, I think, are the part and the selection. Um, part is the part that really, you can't. You can't compete with anything else.

Speaker A: And you and I, it's funny that, uh, you and I talked about this also when we recorded the previous podcast and probably just during casual conversation, um, that this is the flip side of my story with the. With the challenger. Right. At the same time, I'm planning my European trip. Last year, I told you. Because you were also going on your European trip, right? That's right. Um, so, okay, I have it all planned out and we're gonna land in Switzerland, we're gonna drive down to like, the. The Swiss Alps. You know, I'm gonna get this Mercedes and then we're gonna fly over to Austria and I'm gonna get the Cupra Fermentor, which is like a sport, you know, Spanish kind of. So I have all this planned out and it's all good. But then we get to Austria after I had the Mercedes, which was nice, by the way. I'm not really super into cars, but when we travel, I think it's part of the experience. It's kind of like what kind of. What kind of accommodation you book. It's not just roof over your head, but it's part of the whole.

Speaker B: That's right.

Speaker A: Whole journey. So I'm mindful about that. And I wanted the Cupra for mentor, but then they did not have it in Austria. Um, so there was a little bit of scrambling. And then they actually gave me an Alfa Romeo Tonale, which is a very nice cart, but I had to pay a little extra. Right. And that moment, it's like, this is like the. It sounds like I'm complaining about something that's like insanely luxurious. I did. Not at all. We loved it. Right. But I told you that it would be actually really cool to know that I'm getting exactly what I want. And that's when Paul also, remember he took that trip to Toronto from Vancouver. Like, literally drove over and he took a Mustang. Uh, yeah, that's exactly what he wanted.

Speaker B: That's amazing. Yeah. And so that's. That's where again, the selection and getting exactly what you want, um, to. Then also then the convenience, um, is that there's also, ah, the traditional experience. You, you wait in a extremely long queue or line at an airport. Hopefully, uh, you get there in time, you have to take another shuttle. But here with Turo, like, these are hosts that own the car. They can actually deliver the car to you. Um, and so whether they want to meet you at the hotel or in some airports, uh, where it's uh, um, with. Depending, uh, on the permit, uh, uh, regulations, uh, you know, they can actually deliver the car right at the curb. Um, so take your family, go right off the plane, step onto the curb, throw your bags in, uh, and off you go. Um, it is such a magical experience where you're not stressed, like waiting in line. Am I going to get the car? Is someone going to upsell me on insurance?

Speaker A: That I don't understand. You don't. You're not looking forward to the process for sure.

Speaker B: It's, it's not, uh, not an enjoyable experience. But here then it transforms it. It like completely flips it on its head. Yeah, it's uh, experience is kind of what we are obsessed about. Um, in terms of making sure everyone has that five star experience.

Speaker A: Uh, and this is awesome because you're also a. I don't know if you, you are still, but you're also like a. I know that you were at least like a Turo host.

Speaker B: Yeah, I still am.

Speaker A: Okay. Okay. There you go. And so it must be a fascinating playground for someone like you being at Turo at that time when they are trying to innovate user experience and user experience, but also just in general like the, the whole business model. I think it's very interesting that you have two customers. Yes, right. You have the, the people who hold the cars, they're one type of customer. And then you have the end users who will rent the cars.

Speaker B: That's right.

Speaker A: Right. So that must have been fascinating. How did that go? Like, what kind of innovations have you guys. Of course, whatever you can share, you know, of how much you can show under the hood to use a. Yeah,

Speaker B: I think this is what, um, my first introduction. And that's what a marketplace is, where you have two sides of their supply and demand. And there's so much that goes into it and I, I've only appreciated it over the years and now have fell in love with marketplaces as a result. Is that it's just so dynamic. Um, it's not. You're just selling a physical product. Uh, and here it's. You do in fact have two customers and how do you balance both? And how do you think about being thoughtful of growing one side of the marketplace but not too fast for the other side, but then kind of the network effects around both. So, uh, that's really evolved in a lot of different, uh, innovations over the years in terms of how we're thinking about, um, even just measurement, how do we think about these what seems like simple problems but even just understanding the business is extraordinarily complex and it's very uh, localized in terms of well what's happening in Vancouver is different from what's happening in Miami or Denver, uh, or in Melbourne. Uh, it's all uh, very uh, unique to the demand, the seasonality, the types of cars, the types of guests, where they're coming from. Um, and so it, it kind of really ah, forced from a data standpoint for us to really think through not only novel and creative ways of understanding the business, uh, but then focusing on all the different things we need to do to make the experience better for how do we make it uh, kind of the end goal, uh, or vision is like really for every guest we want it to be push button, get car, we want it to be that simple. And it's, it's, it's, there's no additional paperwork, there's no upselling afterwards. I got exactly the car that I want.

Speaker A: I don't want a lot of letters on the screen. That's right. Yeah.

Speaker B: And so uh, and, and that's, that's always been the vision of trying to make sure that that has been as easy as possible. But also then when they're on the trip that the experience is a five star experience every single time. And then on the host side we want them to uh, uh, effectively push button, make, make money. Uh and this is what it's really evolved. Uh the different capabilities of the idea at the time is that oh someone owns a car part time, they'll just start to share it. Um, but that's evolved from someone owning one car, uh, to then folks earning, owning you know, 5, 10, 15, 100 to upwards of a thousand cars on the platform. And it's like a full time entrepreneurial business that are doing it professionally. Um, but that requires us to think about the product and experience differently. Uh, that's a lot of cars to manage. They're managing their business. How do we make sure we help uh these hosts understand their business, how they have, do they have the right data? And so thinking about it from another lens of these are all thousands and thousands of entrepreneurs, uh and we want to empower them but they also need data, they also need information. Uh so a lot of interesting innovations there as well in terms of some of the things we had to build.

Speaker A: And so when people ask me, let's say two years ago or a year ago, um, what is Turo right If I said and they did not know. Yeah, right. The Quickest way that I used to describe it. Right. As an outsider, is that, well, the Airbnb of car rental. And everyone's like, ah, okay, I get it.

Speaker B: Yes.

Speaker A: They immediately get it. Right, Right. First of all, would you say that that's an accurate.

Speaker B: That would be.

Speaker A: Description, at least on some level?

Speaker B: Yeah, that would be the accurate description. At some point. I think we always strive that people can say, um, Airbnb is the, like the Turo.

Speaker A: Uh,

Speaker B: but obviously, you know, they're much larger and much known. But I mean, that. That is.

Speaker A: They pioneered.

Speaker B: They pioneered this idea of sharing. Like, it's. Someone physically owns this. It's not a hotel, it's not a corporation, per se. You're sharing something deeply personal with somebody. But, uh, it also brings a really unique experience. Um, and so I think that's where kind of the synergies between the two.

Speaker A: And it's interesting that I'm not saying that the Uber. The. The Uber of car rentals.

Speaker B: That's right.

Speaker A: Right. Because actually, Uber, it might have been predicated on the idea that, oh, you know, you go from A to B so someone can hop in.

Speaker B: That's right.

Speaker A: But that actually disappeared completely from their business model and became an alternative to a taxi. That's right. And just now they are trying to actually reintroduce the card sharing with, like, the set routes.

Speaker B: That's right.

Speaker A: Right. But there. It doesn't really work. But with homes. Right. People were also like, oh, really? Like, I'm just gonna let someone in my home. Yeah. So I remember when the selling point for Airbnb was that it's much cheaper than a hotel.

Speaker B: That's right.

Speaker A: And that actually also disappeared and it became experiential. No, sometimes when you go, you want the experience of an Airbnb. So let's say when we went to Switzerland and Austria, I was like, no, I want a house in the mountains. That's right. Right. I don't want a hotel. And, uh, later on I do in Austria, specifically because I want that specific type of hotel, but because that's the experience that I'm trying to facilitate there. So there was this conceptual shift. And then I remember that with Airbnb, their main bottleneck was trust. Um, right. And this is a great theme here with your career, too. Also, the projects that we've been working on together so far, very much focused on trust on so many levels. Right. And then basically with Airbnb, the whole user journey can analyze how each part of that experience reassures and. And the, uh, user and kind of re. Establishes just trust at every single point with the language. That's right, because they pioneered that and they proved that that model actually works. Uh, that empowered other industries as well. How much did you guys look at Airbnb as a concrete example to anticipate at different stages of your growth what kind of main challenges you will have and how you overcome them? Because for example, Airbnb had a very specific answer to this lack of trust.

Speaker B: Yeah, yeah, I, There was a lot of inspiration, but there's also some. The similarities and differences of it. And I think uh, this, this idea of scaling and trust, uh, in any marketplace, uh is something that um, as you scale it's something that you need to lean into more and make sure that everyone is feeling safe on both sides of the marketplace. And the, the special or unique and fun part of doing this from an. A car, an automotive standpoint is that there is this really complicated aspect of what happens if the car gets in an accident. Uh, and obviously there's the, the trust and the safety side from the, from the guest standpoint. But also. Well this is a financial asset that the host earns and they rely on this for, for income. And um, all the complexities that insurance uh, deals with that were really. I think most of our energy was really focus around how do we think about the cost of insurance side is probably the most complicated aspect of the Turo business is sharing uh, items with one each other. If you're just. Or selling a uh, marketplace like an ebay or all these things are straightforward in the concept of you're just buying a product. Sure there's some bad products, you may have to return it. But here there is uh, you know, people's safety, physical safety is, is uh, actually at risk or is also like the actual asset itself. And it's uh, something where we think deeply about is in terms of how do we really make that experience uh, as safe as possible on both sides. Uh, and uh, it required. I think this is the part where we had to then rely on a lot of the data to do this. Um, I mean there was no way around it of how to think about managing risk. How do we think about minimizing that cost and making the experience good. Um, and that kind of pushed us to evolve uh, a lot of different innovations of us doing some of our risk modeling. How do we think about pricing relative to risk? How do we uh, even just make sure we're uh, doing different things to prevent um, bad experience from happening to begin with. Uh, like predicting uh, we have different fraud models and risk. Different risk models. And, uh, incredible that what the team has been able to build and being a part of that has been, uh, really, really quite an adventure.

Speaker A: And uh, it's funny how you mentioned that story with the founder looking at the cars, um, like they should be just, you know, rented out. He probably did not anticipate immediately. Oh, but you know, overcoming insurance hurdles will be a big barrier. Uh, this is just something that you just find out about.

Speaker B: Absolutely.

Speaker A: Later on. So it's not dynamic nature of what are, what are the key problems that you need to solve and how do you identify the key problems, even how do you become aware of the right problems and then how do you solve those problems as fast as possible? So basically a company like Turo that race to uh, an IPO is about that. Right. And demonstrating the ability that they can actually navigate a dynamic space.

Speaker B: That that's correct. And I think the part of the challenge here, it was kind of a nuance where we knew insurance was always going to be something we had to solve. Uh, but also in your earlier stage when it comes to analyzing the stuff and risk and when someone gets an accident, there's actually not a lot of data because we were kind of subscale and there's kind of. The problem was like, well, we don't have enough claim volume to analyze and to be able to predict this. So we had to come up with other creative ways to be able to manage that. But as we've grown and scaled, we've been able to really increase the sophistication of how we're thinking, thinking about managing risk. Um, and really, I think what part of the interesting part of when I rejoined uh, Turo in 2019, um, there was obviously this global pandemic and it transformed how everyone was thinking about profitability. Um, and in that year, this is where I think in a matter of months, um, through a lot of the efforts of the data team, we transformed the economics and how we approached, uh, and leveraged a lot of this data to change the way, um, we think about risk and managing risk. And actually in the span of two quarters, we were able to actually move the company from, uh, being unprofitable to profitable, um, through a lot of, again, it was a required necessity in the world that we were in in 2020, uh, but also really, uh, leveraging the data that we had.

Speaker A: So I want to talk about before we get into the pandemic, because I'm sure there will be a lot to talk about around that time too. But before your equivalent of the Michael Jordan, uh, baseball break. And I'm not suggesting that you didn't do a great job at Facebook, because actually few know that Michael Jordan didn't do that bad at all in the, uh, in fact, it was amazingly impressive. But just to dial it back a little bit, so we're talking about 2016. If you, if we look at the, the world at large. Yeah. There are so many things happening and the state of data and analytics and its place in the public consciousness and, uh, how, uh, people think about AI and data science and all that. There were some of these big moments. And you probably remember, I mean, of course you do, because we just talked about that too. The Cambridge Analytica. Ah, I think it was what you call this scandal, uh, or it was basically, it was in the news cycle. People kind of remembered. And that's when actually, uh, data really entered into all layers of life, including, for example, politics. Right. So deployed in all areas because of its real efficacy, it's real power. So with Turo, how long was your first stint with Turo?

Speaker B: It was two years.

Speaker A: Okay, just two years?

Speaker B: Yeah, two years. And, ah, it was an intense two years as, uh, again doing the three different roles. Um, and I think, uh, the timing of. Within that two years, we were able to just wrap up and secure our Series D. We raised a little over 100 million, which is exciting. Uh, but that kind of coincided, timing wise, exactly when Cambridge Analytica happened. Um, and all this stuff about data privacy and how to think about this in the context of, uh, uh, all these different apps, uh, was the buzz and what everyone was talking about. And coincidentally, uh, that's when Facebook will now meta. Facebook tapped me on the shoulder like, hey, would you want to lead a team trying to measure how much bad stuff is on the platform? And I was like, that sounds crazy. Like, who want dude sign up for trying to measure the immeasurable? Uh, or how do you even think about this? But it was so unique.

Speaker A: Uh, it's a compelling problem too.

Speaker B: Yeah. And I think I'm attracted to these interesting, compelling problems that, uh, um, I ultimately decided that I couldn't pass up on that opportunity.

Speaker A: And they found you just because they were looking for someone who could do the job and they found you. They. There's this guy at this, uh, cost sharing startup that's doing good.

Speaker B: I think the unique part, and I think this is what's just so organic about, uh, my journey is not only being deep to the data side of it, um, but having the FPA and the Fundraising, uh, aspect. It required me to actually really develop my storytelling skills, uh, from how do I talk about the business, how do I talk about the data in a really nuanced way, but not only for an external audience, but internal. Um, and that experience I think really was super applicable to this data transparency role, um, uh, at Facebook because that was exactly what they're trying to do is how do we measure all of this stuff, all the bad stuff that's happening all across the family of apps and how do we talk about it internally but how do we also talk about it externally so that there's a heavy conversation? It is a heavy conversation. It's also extremely complex. And ah, again just this goes back to just another theme of trying to create clarity, um, out of complexity. That was something I've over time, in a lot of practice, something that um, I really got excited about this idea. How do I explain something that is deeply, deeply technical, deeply nuanced in a way that others can understand and have a productive conversation about it and whether that informs how the policy around all of the stuff at Facebook works to just understanding uh, opportunities of how do we improve the process of which we're, we're taking a lot of this content down as, as quickly as possible.

Speaker A: So, so, and how to quantify, to unquantifiable. Yeah, right. And okay, during your time there you

Speaker B: spent how, 18, uh, months.

Speaker A: Okay, okay, 18 months. So what were your key lessons during that 18 months?

Speaker B: Um, there were quite a few. I mean one of the things, uh, I, I, I will say that the, the talent and the, the intellectual ability of the folks at Facebook and Meta, it was just incredible. Um, and it was something that was super inspiring to kind of work amongst uh, such a talented team across the board, across all functions. Um, but I think uh, what I learned out of it really was this kind of, this in my role outside of just data, it was really around how do you think about at that scale? A lot of it is how do I think about influencing decisions? How do I um, take a leadership role and be able to transform the narrative around um, some of the opportunities and doing that in a succinct way and being able to lead a whole organization. Um, and that's where I was able to do kind uh, of an opportunity that with the data transparency efforts. But I also shifted over to the complete other side of focusing on all the good stuff, uh, on the social impact, uh, and social good products where it was not measuring all the bad stuff but how do we amplify good with Products like charitable giving to a, uh, blood donation product. Ah, that was more international to a mentorship, uh, product. And so um, this idea of sure, there's the data component, but then working and partnering with different teams, um, is something where I really appreciated that to make things work, it's not just a matter of just having a good data and being able to tell that story, but really influencing the decisions and working with others, uh, to then make that a reality. Um, and that's kind of where that kind of last mile problem is, where you can have all this great analysis but if you can't tell that story or if you can, you can't partner or influence the decisions, then nothing's going to happen.

Speaker A: Exactly.

Speaker B: And so that was really, really like probably one of the greatest takeaways from my time at Facebook was that ability to learn how to influence and be a leader, uh, and be able to partner and kind of move the organization, uh, as quickly and as impactful as possible.

Speaker A: And this is like basically Facebook at the height of its powers. It's not like it's weekday, right. But at that time it's like top three companies in the world, sexiest companies with probably Netflix. That's right, Facebook, Amazon.

Speaker B: This is the Fang. Facebook, Amazon, Netflix, Google.

Speaker A: The pre meta, uh, uh, phase. So at that time what was Facebook's business model? Because I know that it was spreading like crazy at that time. Right? Like geographically, globally.

Speaker B: Yeah.

Speaker A: Right. But what was the underlying business model of how they covered costs and how were they trying to generate profit? And I imagine that you were not like you just focused on that lane of. Okay, were you interested in going into other areas at Facebook?

Speaker B: Yeah, I mean Facebook, massive, ah, massive organization already at the time, uh, in 2016, uh, or sorry 2018. Um, I don't recall the exact size but it was, they, it was also

Speaker A: before downsizing later on.

Speaker B: That's correct, yeah. So they were continuing to grow extremely rapidly. Uh, I don't remember the exact stat but it's like the percentage of newer folks is just incredible. Just the pace that they continue to hire was uh, just like it never slowed down. Um, but again the core business model ends up being all the ads. Um, and there was a whole amazing, brilliant team thinking about the monetization aspect and how folks uh, were starting to then use Facebook and Instagram also at the time on um, trying to drive a lot of the advertising and using this as a new platform outside of uh, at the time just doing regular placements on a website to. Now all these people are spending so much time on these social media apps and being uh, able to monetize on that. I, I didn't personally get to work with this team. It was interesting to um, kind of be around uh, some of the folks that were um. But I think this is where it's so big that they're, they have to divide and conquer in terms of the uh, all the different teams focused on um, so many different products that they're developing from their newsfeed to uh, all the other innovations of what they're doing with Instagram at the time. There's also WhatsApp, um, had already had already been acquired and so there was just a lot going on. Uh, but I was just focused on a component around community integrity which was really, really great mission to then ah, social media good and social impact. But that, that's kind of what pushed me to really realize I missed kind of the broader scope. Yeah how do I influence all these different areas? And there was no way of doing that at a company at the scale of Facebook or now Meta. Um, and so that, that kind of ultimately uh, resulted in me joining Turo again and back in end of 2019.

Speaker A: Yeah. And I can also imagine that these companies are very competitive also internally and I just imagine maybe they are not. But I imagine that there's some natural like you know, territorial sense of you know, we know our stuff here in performance marketing. And as you talk about this now it's interesting to me that you know Turo is a marketplace basically it identifies uh, sellers and buyers and facilitates the transaction between them. And that's how Turo makes its money too. That's right.

Speaker B: Right.

Speaker A: Uh, they essentially, I know probably I'm butchering it but basically they take a cut from that transaction.

Speaker B: That's right.

Speaker A: Okay. Okay.

Speaker B: There's just kind of uh, it's, I think it unlike most marketplace, there's usually monetizing on one side here for, because of the, the way that we manage insurance. But uh, effectively on the guests, as you book your car there's a price and then there's also a trip fee on top of that. Um, that kind of got it. Uh, where we are able to collect 100% of that as is revenue.

Speaker A: Okay.

Speaker B: Uh, and then on the host side they have their price and then they can choose different protection plans. Interesting. Um, so that if in case an accident happens they can take on more risk themselves which means they take a bigger cut, um, 90% for example and Turo will only keep 10% or if they want to be risk averse they can take a smaller cut, uh, let's say 75%. Um, but Turo will be, uh, able to help financially depending on, uh, on what if an incident were to happen during the time.

Speaker A: So Turo also functions a little bit like an insurance company, but they also facilitate business between the seller and the insurance company.

Speaker B: Uh, that's right. Yeah. I wouldn't say we are insurance company, but we have an insurance partner that we partner.

Speaker A: Okay, so you facilitate the business. That's right. Okay. Because Tura is good at facilitating business between parties, so why wouldn't you just facilitate business between. Okay, so this is great. I'm also not thinking that, okay, this is a marketplace, even Facebook, on a business level of how they make money, they're also a marketplace in a, uh, in a sense that they identify sellers and buyers. That's what ads do. Yeah. Right. So as a business, they might actually offer an experience like social networking. Right, right. But they don't make money from that. I mean, no one is taking money from you to sign up to Facebook. You know, they're not. They don't have a monthly fee. And then would you say that the OG marketplace, because we're going to talk about also, you know, you are working for a marketplace today too. Would you say that the OG marketplace is eBay? Uh, is that the big. That is the big one, uh, case study.

Speaker B: And that's the one where there's a lot of inspiration. Actually. A lot of our leaders are X ebay as well.

Speaker A: Okay.

Speaker B: And so they drew a lot of that experience. And this passion for thinking, uh, about both sides of the marketplace, uh, really drew at it. I'd say that was kind of the point of reference of, uh, learning from what those experiences were at ebay, and they definitely were the og.

Speaker A: Uh, yeah. And it's interesting because in the beginning, people were skeptical about ebay on the level of, well, you're just gonna send the thing and you trust that they will send the money.

Speaker B: Absolutely.

Speaker A: It's not gonna happen. And it did.

Speaker B: Yeah.

Speaker A: So we found out, actually, something about ourselves that we had the wrong assumption.

Speaker B: Yeah.

Speaker A: You know, we kind of underestimated ourselves in that sense, which then opened the door to, oh, wait a second, this works. You can. You can just do that and. Okay. Will it happen? 100% of. No, of course. But that's fine. We can mitigate that. The core idea works.

Speaker B: Yeah. And that's this. This whole idea of sharing or this marketplace. I mean, outside of the technology world, I mean, this has existed for thousands of years.

Speaker A: Yeah.

Speaker B: Right. But creating the technology aspect of it and creating a way to be able to do that and interact with people around the world and do it easily, building that trust. Uh, and again having that selection, having all this experience to be able to do it um, at the time predominantly on desktop and eventually moving to mobile. Um, yeah, I mean it's quite uh, exciting kind of to have seen that and then kind of transplant this idea of a marketplace into a different use case of this car sharing. Um, uh, has been again and a wild ride of past nine years.

Speaker A: The Facebook saga. 18 months. Why did it end?

Speaker B: Yeah, it was, it ultimately was this recognition that I really enjoyed, uh, the mission and everything I was doing. But I missed the broader impact of being a part of a team that really is scaling the business. I missed working on the marketing side, the finance side, you know, the product side and all aspects of it. Um, and I naturally just ended up just re sparking some conversation with the folks at Turo, uh, and the timing just worked out, um, uh, in terms of just uh, them also just looking to uh, have someone fully focused on the data side in analytics. Uh, so I wasn't doing two jobs or three jobs. It was just fully focused on analytics, uh, which was exciting and what I

Speaker A: wanted to do and what you love the most. So they're like, yeah, we need Albert and now we're going to get even more of him even for better return because it's something he loves.

Speaker B: Absolutely right. And so that was November of 2019. So made that made the decision, um, uh, in hindsight again joining a travel centric, uh, peer to peer car sharing company right before a global pandemic. Probably not the best.

Speaker A: Yeah, yeah, I want to hear about it. So how was business for Turo for that 18 months that you were away?

Speaker B: Uh, business was good. I think one of the challenges, I think uh, as I got up to speed was um, and like any marketplace, it's a, the pendulum. It's like a pendulum swinging from demand to supply. Uh, and one of the challenges at the time then was uh, supply was a challenge, just uh, needing to get more and scaling more supply. Um, and so there's a lot of emphasis there. But the business was, was doing well. It was continuing to grow, uh, rapidly. Um, but all that changed in March of 2020, uh, where like the world stopped, uh, like flights stopped, no one traveled. International travel completely almost shut down. Uh, and there's a lot of uncertainty and it was, uh, it almost like it didn't feel, it felt surreal like

Speaker A: just looking at Even now looking back, it's.

Speaker B: Yeah, it just like um, like not only what everyone experienced, but on the data side you literally see everything fall off on the uh, off a cliff. Uh, and so it uh, was fascinating to be able to be a part of that. It was uh, I mean everyone was going through their journey during, during COVID and the Pandemic. But uh, from a data standpoint it was quite the journey.

Speaker A: Like a flat line on the ekg.

Speaker B: It really was. Uh, and it was like things were going up and up and up and it was just like quite literally just immediate crash. Uh, and so then it was just uh, the next couple of months of just trying to navigate, um, there were still interesting use cases where there was still uh, folks that in healthcare they still had to get to work. And some of them actually relied on TURO to do that.

Speaker A: Wow.

Speaker B: Saw these amazing stories of just how um, TURO was still something where folks were able to use, uh, in different types of use case, not necessarily leisure travel, which pretty much disappeared at the time. Um, but then it also uh, forced us like many companies that then focus on profitability. Uh, with Top line not growing where uh, we would want to and we couldn't control that. We had to then think about how do we cut costs and rethink that. And that was kind of the main focus of not only the data team at the time when I joined, but um, also kind of the whole company um, is really rethinking of how do we cut costs.

Speaker A: Like a wartime.

Speaker B: It was wartime. It was trying to really uh, navigate that. And so it was quite an experience where 2020 was like a focus on profitability. Um and we started to see some of the, the bounce back. It was interesting to kind of see uh, where one of the markets that opened up first uh, was uh, in Florida, uh, where people can go. And so everyone was um, excited to go there. This is in 2021 where spring break there was massive because it was one of the few markets that have just opened up. So everyone's like, oh, we're all going to Florida. Uh, but then we also noticed in, in March of 2021 this is when effectively it was like almost the anniversary of, of shelter in place protocols where everyone was like I need to get out of the house. Yeah, yeah, yeah, I remember, I need to go somewhere. Um, and that's when travel really started to pick up. But it picked up uh, in a way where people needed cars. But in 2020 traditional car rental, they uh, they require. Their business model requires that they're operating at a high utilization, meaning that their cars are booked 80, 90% of the time. But when demand completely, uh, was eviscerated, they had to actually sell off their fleet. So many of them actually downsized, uh, their fleets. This is Hertz Avis Enterprise, all the traditional car rental. Because they weren't getting booked financially. It doesn't make sense for them. But when the Demand came in 2021, they didn't have the cars.

Speaker A: Wow.

Speaker B: This is, uh. And it was an interesting time where even getting cars like they, they were trying to work with manufacturers, but then, well, manufacturers people also wanted to buy cars. Well, they can. Why sell it at a discount?

Speaker A: I mean wholesale. You have to, you had to, I remember, put, uh, down your name two years in advance for a Toyota.

Speaker B: Yeah. Oh, yeah. So it's like it was, it was insane.

Speaker A: Yeah. Talk about supply and demand, right?

Speaker B: Yeah. There's a supply chain issues with getting, producing the cars and all these things that was happening because of COVID And so then it was like people wanted to travel, but how do they get cars? And this is what really helped, like up level or step function change, kind of getting Turo closer, uh, to a household name.

Speaker A: Um, you were well positioned in that.

Speaker B: Yeah.

Speaker A: And it's, it's interesting because I also, just before we dive in. So with Turo, when you rejoined them, that was 2019, what month?

Speaker B: November.

Speaker A: Uh.

Speaker B: Whoa.

Speaker A: So how did you process personally that. Wow. I moved From Facebook to T2Row and now this is happening flatline, basically the whole data pipeline. Right.

Speaker B: Yeah, I was, uh. Panic was uh, probably putting it lightly, but it was, ah. I mean, there's no way anyone could have predicted it. Like, you heard rumblings of this already in early, uh, 2020, but not really sure what was happening.

Speaker A: It happened fast.

Speaker B: And once it happened, it was just like, uh, everything was kind of just trying to react. And so there was some panic induced. But then I think it was then the focus on like, what do we need to do to kind of manage the business, manage the team, navigate this new world that we're living in, managing a whole workforce remotely. All these amazing, like, things like as. Even though I was panicked, like, I almost didn't have time to think about it. Like we then had to just go into what do we need to do to keep things moving. Uh, and that was, uh, you know, uh, as scary as it was, I, uh, mean, everyone was kind of going through it. It was quite an experience.

Speaker A: Yeah, I can imagine. It's interesting that I'm just reflecting on on that, because that was 2020. And we sold the previous business data leaders in London, 2020, January. Right. So I was in the process of selling it, and then I remember that our. Some of our main offerings were in person events. Right. So actually, that product, like, I developed that product. I was, like, heading the team that was developing that product specifically, which were these roundtables across Europe for data analytics and AI leaders from, you know, big companies, similar to what we do, but it was more like, you know, one days in London, Amsterdam, Copenhagen, uh, Zurich. Right. And we handed over their business and the ink dried on the paper, and the next day is like, okay, travel is shut down. And the company thought that, well, okay, we'll need to move the march eventually to June. Huh. Okay, well, it will be probably a little bit more of a skip, you know. And I had my whole plan of, okay, let's sell the company in, Jen. And this is what I'm gonna do for a year.

Speaker B: Right.

Speaker A: And no, that's a pandemic. So. But it. But it kind of worked out well because I wanted to do consulting. It was an interesting time to do, like, small business consulting during that time.

Speaker B: Right, right.

Speaker A: So in the second stint then. It's funny that this whole situation turns from a big liability into an opportunity. Yeah, right. For you guys. Of, okay, now we're best positioned to actually provide people who have a need for a car.

Speaker B: Yeah.

Speaker A: Right. We can move faster than the car rental company where their business model is about actually maintaining fleets.

Speaker B: Absolutely.

Speaker A: Which they downsized. Okay, so is it safe to say that you moved into, like, a different type of offensive mode? Offensive meaning that you are now on the attack to, like, actually develop solutions for the company to aggressively drive that growth.

Speaker B: Yeah. 100. It went from now that we had this great backbone of. Of, uh, addressing and focusing on profitability in 2020. Gave us this. This comfort that, okay, now we don't have to worry about fundraising. We kind of are in control.

Speaker A: You establish that position in the marketplace.

Speaker B: Exactly.

Speaker A: The market believes in you.

Speaker B: That's right. Uh, and now that. That, uh, we needed to grow supply, uh, this is the part that we saw kind of the fundamentals of our marketplace really shine through. Because then it was something where naturally folks, uh, like in Hawaii, where you couldn't get any cars, like, travelers were booking U Hauls because they just needed a car. Um, our community rallied in terms of the host community, and they were starting to then just get cars from their family, their neighbors and everyone. And the scalability being able to ramp up the Supply. We were able to then really uh, focus on how do we operationalize, uh, that playbook of like how do we scale supply. Um, and that then that created another challenge in 2021 when uh, we had all this supply and demand was kind of evolving in this post, uh, Covid world where people weren't just doing this revenge travel, uh, uh, sorry, 2021 to 2022, um, and so then it focused uh, again a lot of the efforts on the demand side and it pushed us to really continue to attack kind of the problems, uh, kind of one at a time, but really using data as much as possible along the way.

Speaker A: So it's also interesting to me because there is this talk today about how AI will replace this and that. And now we will have AIs building companies and selling companies and you know, okay, which are, look, no one knows what's gonna happen, but we have our opinions and then we're trying to assess what's actually happening. I'm sure that also, uh, later on today we'll talk about some of that, of the AI conceptions. But with basically growing a company, making a company successful, that's about navigating that story, that very dynamic space of what's happening other industries, the world in business. Right. Um, when you think about creative production, AI is the same that it can really facilitate creating something that is beautiful to watch and it's engaging to experience. But we don't necessarily know the exact recipe of that success. So when people say that, oh, now AI will direct movies and put together movies and release movies again, we don't know what's going to happen, but we can be skeptical. So that human element during that whole phase, do you think that will be replaced in the future as AI progresses? Right. Uh, what is your view on that? And we can transition into that kind of AI conversation too. But maybe first on a philosophical level, yeah, it's interesting.

Speaker B: I mean there's so many different opinions on this and uh, I'm happy to share mine, but I kind of see it as uh, uh, maybe the parallel since I was living in the car space at Turo, uh, is like this, this vision of L5 autonomy. Everyone's like, oh, everything's going to be self driving and all that. And at some point, uh, maybe we'll get there. Who knows exactly when we'll get there. But same thing with AI where AI is going to take over everything and do all this decisioning. In reality, I think, uh, the way I think it was like there's really kind of this hybrid approach of how things are going to happen, especially in the creative side, uh, where there will still need to be an element of the kind of gut checking or ground truthing back to uh, not fully relying on things but still having trust. And I think this goes back to again m my foundation of everything coming back to trust. How do you have trust in what's coming out of uh, the engine? I think this is where depending on the use case and there's so many different use cases for AI which makes it so exciting. Um, how do you think about trust in that aspect, uh, and not just taking whatever it's giving you as truth or um, the idea and the risk of hallucinations, uh, that is still very real and will continue to be real. Um, and so I think why I get excited is that as more and more use cases and the rate that AI is evolving there's this interesting things where you can start to see all these opportunities and these ideas of how to kind of merge the two worlds of the way that we used to work, uh, and this idealistic way that we could be working. And I find it exciting and beautiful, scary but uh, opportunistic. Uh, and so this is where the curiosity side on my side, like I'm excited to kind of explore this further on the data side. But I think there's so much to do with leveraging AI in a way where it's ingrained in how we operate, it's not replacing it. And I think that's my philosophy is like to get the best of both worlds. It needs to work together but it should make your life easier. So it's not like those things that maybe have done that manual process like yeah, let's automate. Um, but there's the part where there's still a little bit of creative element um, that right now I don't see a world where we can trust that's coming out of um, some of these systems yet. But then you can harmonize the best of two worlds where you have the time to be creative but you can kind of reason with these models in a lot of exciting ways. And uh, I'm now ingrained where I use it on a regular basis now.

Speaker A: Uh, so I'm really glad that I asked this question because this is the perfect way to kind of finish this session too. So we are actually, and challenge me on this. Right. But we're actually terrible at predicting if something will be replaced. Yeah. Right. So let's say this self driving car example, I think it's perfect because I do remember in 2014. Right. I remember being on 2014 LinkedIn when I started my, you know, professional career in business analytics. A lot of time on LinkedIn. It's very different LinkedIn from what it is today. Right, right. And it was a fact that self driving cars are replacing all trucks within two years. It was a fact. Yeah, right. It was pretty much in the bag. It was just about how we're going to manage that transition. That's right. Didn't happen at all. No. Right. It's like 10 years and we're, we don't have like, we're not significantly, we don't have a significantly higher number of autonomous trucks on the highways. That's right.

Speaker B: Right.

Speaker A: And we were terrible at predicting that. I see that even with AI, almost like the same is happening. So. And it's obvious on many levels. Let's just talk about growing a company and selling a company. Now we have, oh, AI cannot build companies and sell companies. Uh, ask any investor, when they buy into a company, what are they buying? It's a very easy thing to look at what they're buying because they are not hiding this fact that sure, you need a good core idea, sure you need a good narrative around an opportunity, for sure. But what do they buy? Mostly it's the people, the founders, you know, their experience and all that. So the same reason why most trains which are basically tied to a track are still driven by humans, even though it's like a closed, simple system. Right. That is not a technological barrier there. But there's something that we still want. A person, due to all their faults and unpredictability of a person is like way higher than a machine, but we still don't really trust the machine. And also I remember in 2014 too, they were like, oh, now with the chatbots, this was like pre gen AI, uh, chatbots. Right. With the advent of these chatbots, the sales professions will be wiped out in like three months. Three, three years. I'm like, you have no idea about sales. Sales is not about like providing the right information. Right. It's a whole different game. It's like you need the human entities in the world and we still don't understand this. Right. So. And I think a lot of people see it as, this is a contradiction to. Either you're an AI optimist. Right. Or you're an AI pessimist. Because you don't believe it. No, I think it's like, you know, being AI realist.

Speaker B: Yeah.

Speaker A: Right. And for that you kind of at least need some level of understanding of the underlying technology. So maybe I would love to get your take on this right that today when business leaders are investing into AI and they're incorporating it into their business strategy and they're going all in on that, what do you see as the greatest, like hidden opportunities and maybe the hidden blockers. Yeah, that might, you know, result in market corrections.

Speaker B: And I think the, the opportunities, the, the starting point here is this. It's, it's getting the entire company and everyone uh, comfortable with it. I think this is where AI is such a, it's a big concept. It can mean different things if you ask any single person in the company. Uh, but getting comfortable with how to just using it from small task, uh, whether it is just like a ChatGPT or Gemini or whatever it is to then evolving that will open up the dialogue in terms of identifying the use cases. Um, and I think that's where uh, again I feel it's challenging right now where everyone is trying to think about all of these uh, shortcuts because everyone loves to hear the buzzword, oh we, we implemented this solution, this AI solution and we cut our cost by 50%. And it's like sure, that may have been true. Uh, there may be some caveats to that. But I, I don't think there, there's, it's like all these other gimmicks. Nothing's that easy all the time. But you can be really thoughtful on how you're approaching it. And this is where as, as long as folks are starting to build that strategy around uh, again starting with small use cases but then ah, really evolving to uh, kind of how does that mean in terms of the org structure? How does it think about like how do we think about data? Um, and that's the part where I get obsessed about is that um, it still requires a lot of uh, interesting personalized stylistic, kind of analytical approach on how you are solving problems. Um, and how can you trust an output that's just coming if is it automatically going to give you an output? And um, again that's the area where I'm looking to explore more of this idea where I don't think it's uh, the right concept of here I'm just going to pass a bunch of raw data to an LLM, um, and expect it to give me all the answers that I've been looking for. Um, there's a lot more thought that needs to go into it. Um, I think this is where this trust in data is of good foundation because if you're going to start relying on Some of these outputs you need to understand what you're feeding into it. Being able to uh, assess the accuracy and completeness of what you're getting back and then kind of building on top of that with more and more solutions.

Speaker A: Um, so it's great because here I would love to ask you a question about AGI. So now we kind of walk through your career and uh, it's kind of an exciting spot where you arrived today too. Right. But I um, want to ask you because recently I had a great conversation with the head of credit infrastructure from PayPal.

Speaker B: Mhm.

Speaker A: Who's also data and AI VP Noor Takin Savas. Shout out to him. And we did talk about that whole topic of AGI to be touched on. It's something that's interesting to me, right, that in the 80s when we were talking about AI, we meant a whole different thing to what we mean by AI today. Right. Because we did not know that the gateway into something that resembles the kind of AI that we thought about will be gen AI and LLMs. Right. So what is your take on AJ? Do you have uh, do you think about that question? You know, maybe in the shower, maybe you know, on your walks or in the gym when you're like okay with AGI, how will that look like? What will be? Will we ever reach that point? Right. What role will LLMs play in AGI? Do you ever think about that?

Speaker B: I don't get too caught up in it. I mean there's this, not that I don't think about it, but I get more grounded in what does. I feel like we're jumping ahead, uh, way too far before we even understand the opportunities ahead of us. Um, and that's where I think where I spend probably most of my time thinking about AI is what are all the practical use cases of how companies, businesses, teams could use this now to like 10x their productivity or 10x something and thinking of it as like this unlock and it's, it's a lot more shorter term focus. But I, I feel like that there's a lot of, and I'm not an expert be able to think about uh, where uh, AGI will go. But uh, in the short term, like there's so much advancement that's happened in a matter of years, months, weeks, um, that I, I don't like. It's developed so much faster than people are able to actually come up with tangible solutions. Um, and I think that's where I'm trying to catch up. Like I'm thinking about how can we take Advantage of all these exciting new developments, um, and get creative with different use cases, uh, different applications and stay

Speaker A: focused during a time when there's so much noise.

Speaker B: Absolutely.

Speaker A: Because it might be an interesting conversation, this whole AGI thing, but so many wrong assumptions are baked into those discussions that almost feels like pointless. Right. Um, so what are you excited about today in your world?

Speaker B: Right, yeah, in my world as I think about um, how a lot of uh, AI coding and uh, copilots and things are doing for like software engineering, um, where a lot of code bases are automatically written uh, through uh, a lot of these different AI tools. I'm challenging myself to think through how do we do that on the data side. Um, and the challenge here is that uh, unlike code, if you're generating code, there's like thousands and thousands of repositories that can generate a bunch of code. But to understand an analysis, there's a lot of business context, there's a lot of data context and those two things are not always documented. For an LLM to um, automatically have and really what's in the back of everyone's mind, an analyst, a business person has all of this context in their head. But we need to find a way of uh, really being creative. How do we share that context in a structured way or sometimes unstructured way into an LLM so that you can create um, some of these analysis, uh, and thinking about uh, analytics from an AI standpoint. And this is where I super excited about what the opportunities here. Um, a lot of companies tout uh oh I'm going to throw this AI layer on top of a BI tool and it's great, it will give you summaries of that. But it's all superficial at my standpoint. I don't think it's anyone's really cracked this. Uh, how do I connect the dots between different tables and connect that to a presentation someone just did around m why revenue went up and doing the diagnostics around that and I started to obsess on. There has to be a way, uh, leveraging AI. Maybe it won't get 100% of the way there, but how do we think about that semantic layer, um, and how do we think about the insights layer? And the two of those combined are going to provide the appropriate context. Uh where you have the business equation, you have the revenue equation, you have where all the data lives so that it actually can inform uh, not only the insight, but because I have my kids at home, I also have this notion of like how do you show your work so not only here's the insight coming from an LLM, but here's then all of the work that it's going to show you back so you can build that trust. Uh, and, or hey, it's done 80% of the analysis. Great. I can, let me pick it up from here. And it's giving me the entire workflow of the analysis of how to approach it. It already has the context of what tables I need to hit or how to approach the analysis. And then you can feed that back. And this is where as we think about this iterative process of leveraging it, um, I think it get really exciting because then once it has that context and you need to do the analysis again, you can start to automate certain things. Uh, then you'd be like, all right, great, we've done this analysis. Let's create an agent to make sure to automatically detect when some anomalies happen with that insight. And so it becomes these things where sometimes we do analysis. That's great. It's sitting on a shelf. Well, what happens when we need to refresh that? Someone needs to do that today, uh, manually. Uh, but there's 100 other priorities. So it gets super exciting to think about just the endless opportunities of how you can scale data, but scale business insights. And again, I don't think anyone's cracked this yet, but I, I'm excited to explore.

Speaker A: Yeah. And it's interesting that again, we have an assumption about how AI will impact the world and impact business and all that, but just based on historical data, we're probably getting it wrong. Like it won't play out as we think, as most people, you know, it's like the. Because most people, even if you just look at the market of, of the evaluation of these AI companies. Right. Obviously the belief is in something that's kind of like, you know, that's in the direction of the super intelligence, but the real opportunity is in actually helping these LLMs get closer to truth with more accuracy.

Speaker B: That's right.

Speaker A: Yeah. It's actually that simple. Um, so it's fascinating. I've been really enjoying the past, uh, I guess three years of working with you. So I'm excited to see what you do in this role. And yeah, it was a lot of fun, sir. I really enjoyed the conversation.

Speaker B: Absolutely.

Speaker A: And I'm sure it wasn't the last one.

Speaker B: Oh, yeah. I'm looking forward to many more. Thanks, Leslie.