He added AI to parking lots—then raised $3.5B. | Alex Israel, Founder of Metropolis

Alexander Israel (00:00:00) :
I remember receiving that first ping, from Stripe that the first person had paid. My co-founders and myself were sitting in the parking lot watching and it was this interesting moment. Pablo, because it was seamless. It was the moment when I realized the technology that we were deploying was so seamless. Was so frictionless at first, that the user didn't realize they were using the product. Think about this, you know, eight years later. We still talk about AI in a similar context. We talk about AI in the context of LLMs. We talk about AI in the context of generative AI. We don't spend that much time talking about how AI affects our day to day lives. How could AI truly affect our day to day lives? And that's what we set out to build. And I remember sitting with my wife, and we used to hear these pings on my phone. Ping, minute later, another ping. Two minutes later, another ping and I remember it got to the point. Probably at the end of 2018, where the pings were so frequent that it would drain my battery and then at this point, we're talking hundreds of thousands of pings a day.

Previous Guests (00:01:03) :
That's product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is product market fit.

Pablo Srugo (00:01:18) :
Do you think the product market fit show, has product market fit? Because if you do, then there's something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders and it lets us get better guests, thank you. Alex, it's great to have you on the show, man.

Alexander Israel (00:01:35) :
Pablo, it's great to be here. Thank you for having me.

Pablo Srugo (00:01:38) :
Dude, so, I mean, you just raised. Literally, this is fresh off the press, like, last week. A massive, half a billion dollars, Series D. I think this is the, actually, least amount of time that has gone between a massive raise and somebody jumping on the show. So, you've hit a record, a hundred and fifty episodes deep.

Alexander Israel (00:01:56) :
I love it. Well, it's my pleasure to be here. Yeah, it was an exciting few weeks.

Pablo Srugo (00:02:00) :
When did you start this company?

Alexander Israel (00:02:02) :
We started the company right at the end of 2017, myself and three other co-founders.

Pablo Srugo (00:02:07) :
So eight years in man, did you ever imagine it would get to this size? Did you ever imagine you would be in it eight years later?

Alexander Israel (00:02:13) :
No, you know, it's one of those things where you found a company, you have a vision, the vision takes turns and you hit curves, and you hit walls, and different permutations. When we founded the company, myself and my co-founders. I think we all knew that we wanted this to be our last job and our last job, not in the context that we were going to make phenomenal wealth, and then retire. But in the sense that we wanted to build products and culture that we would never want to leave behind. That this would be our home. This would be our final employment. So I think it's played out in that exact way, and we're still building as a founder-led organization. But no, I could not have imagined that we'd have twenty-three thousand employees, that we'd operate in multiple countries, that we'd have raised this much money. No, I couldn't have fathomed in my wildest dreams.

Pablo Srugo (00:03:00) :
Take us back 2016, 2017. What were you doing before you decided to start Metropolis?

Alexander Israel (00:03:05) :
So I had founded another company in the navigation and mobility space. I had founded a company called ParkMe. We invented a technology called real-time parking that told consumers all over the world, where parking was available and licensed that data into most of the largest navigation companies globally. From Waze to Google to Tele-Atlas and to Ford, and Porsche, and BMW, and everyone in between. Built that company from 2009 to 2015, exited that company to a Microsoft spin-out called INRIX, was two years into my earn out.

Pablo Srugo (00:03:38) :
How big was that company at its peak?

Alexander Israel (00:03:40) :
So we had around a hundred and twenty employees. Actually probably a hundred and fifty employees, and we're licensing our data for kind of tens of millions of dollars. It wasn't a massive company, it was significant play.

Pablo Srugo (00:03:54) :
It wasn't Metropolis, but it was a it was a big success nonetheless.

Alexander Israel (00:03:57) :
It was a success. I'm really proud of what we built, but all things considered a relatively small, small company. You know, we got through our Series B and then sold the company to INRIX. But realized alongside my co-founders that we wanted to build something unique and there was real opportunity to look at the ecosystem. Look at mobility even further, and founded Metropolis right at the end of 2017, beginning of 2018.

Pablo Srugo (00:04:24) :
What was the original? What gap, after having mapped all the parking lots, sold that data, it's mapped everywhere. What are some of the things that you're seeing in that time frame, that make you decide to kind of go into this space again?

Alexander Israel (00:04:36) :
You know, I think there were two things, Pablo. One, I saw a significant opportunity within parking. I don't think at that time we had cracked the proverbial nut. While we had product market fit, while we had unit economic fit, I don't think we had made enough of a disruptive impact into the industry itself. The parking in the United States, $130 billion industry, and we had only captured or taken on a small part. We hadn't been disruptive enough and on the other side, I would say we saw two massive secular trends. One, the future of mobility, future of autonomous vehicles, and how that was going to reshape our cities. But also the future of computer vision and artificial intelligence. We were really early adopters in how we could apply artificial intelligence to the real world. This idea of applied artificial intelligence and we realized that there was going to be a growing trend in how computer vision, and AI could affect our day to day lives. This was the rise, this was kind of the heyday of companies like Amazon Go, Standard Cognitive.

Pablo Srugo (00:05:39) :
Yeah, this was the first wave or maybe the latest pre-gen AI wave of AI. I remember that 2015 to 2019 phase as being kind of more computer vision type AI. Yeah, Amazon Go being a great example.

Alexander Israel (00:05:51) :
And look, it's actually interesting, Pablo. Think about this, you know, eight years later. We still talk about AI in a similar context. We talk about AI in the context of LLMs. We talk about AI in the context of generative AI. We don't spend that much time talking about how AI affects our day to day lives. While we talk about autonomous vehicles, we don't talk about AI outside of the palm of our hands or outside of a desktop and that was the same kind of secular trends we were looking at. At the end of 2017, 2018. So how could AI truly affect our day to day lives? And that's what we set out to build. And that was the original founding catalyst, this idea of the future of autonomous vehicles, future cities, and future of AI and computer vision. And how could we build something truly unique within the market?

Pablo Srugo (00:06:37) :
So those are kind of the trends that are maybe in the background, right? Fueling you but what was kind of the exact product or service that you wanted to build at the outset?

Alexander Israel (00:06:45) :
So the original product was really tied to how we could leverage artificial intelligence. So neural networks and computer vision to create seamless checkout free experiences in the real world. So we wanted to start with mobility and within mobility we started with parking. But we immediately realize that our vision or our product could extend across the mobility landscape. Whether it was gas stations, car wash, quick serve retail, tolling or parking. We could leverage computer vision to create personalization, and seamless checkout-free experiences in the real world. So the perfect example was parking. You could pull into any Metropolis-enabled facility anywhere in the United States, get a text message when you arrive and get seamlessly charged when you leave. And the way we did that was through the deployment of neural networks. We could create a fingerprint of your vehicle, leveraging computer vision, sending you this text message, identifying you when you arrived and then seamlessly charging you when you leave.

Pablo Srugo (00:07:42) :
It's interesting, actually you say this. I'm really curious, we'll dive into exactly what you did and how you did it. Because this is one of the things that, as you're describing, I'm like, yeah, okay, checking in and out. Let's say to a parking lot without having to do anything sounds like a bit of a no-brainer. Something you should definitely do. At the same time, I'm like, I could easily see this being too much for the industry, right? You go to a parking lot administrator, you're like, hey, imagine you could have all this computer vision and you could see this and people wanna. And they'd be like, yeah, but so what. Now, knowing the future, obviously things have worked out and you build a big business on the back of this. So I'm interested to see kind of how that all happened, but maybe take us back to that point. This is what you want to do. What's your first move to get, you know, maybe some of these parking lots online?

Alexander Israel (00:08:25) :
So, it's really interesting, Pablo. If you think back to late 2017, early 2018. We spent a lot of time as an organization, conceptualizing a revenue focus as opposed to a cost focus. So, our go to market strategy was going to be tied to how we could drive incremental revenue to our partners and in order to do that within the real estate industry. It was a very complicated go to market in the context that real estate owners are the last to test new products. They're the last to be the first hamster on the wheel. They don't really want to experiment with picks and shovels. So, we spent a lot of time thinking about what our product that we bring to them initially would be and how it would conceptualize. So we focused on this idea that we can not only amenitize the experience for real estate owners by deploying computer vision technology. But that we could actually shift the underlying economic value of the dirt. We could actually drive incremental revenue to the real estate owner, by not only reducing the cost to operate facilities. But also capturing more revenue, therefore shifting the value of the underlying real estate and through that conceptualization. We came up with a go to market, we came up with a product market fit that would effectively allow real estate owners to adopt our technology early on.

Pablo Srugo (00:09:47) :
How do you? Because the cost reduction makes sense. Although even there, obviously, if you just think about a parking lot, right? In simple terms, you might have the attendant there in person. Which is obviously a cost but then you have, okay, not computer vision. But you just you put your credit card into the outset, you put in at the end. Now there's no human there, hard to cut the costs from that but the revenue lift. That's an interesting angle, what were you doing on the revenue side?

Alexander Israel (00:10:08) :
So the revenue lift was at first really primarily focused less on this idea of how could we coalesce a membership base. Which at this point we have twenty million members on platform. Which has a profound amount of value that can be driven to these real estate locations. But at that time, we were really focused on how could we leverage technology to capture more of the revenue that was actually flowing through the location. So what we created was a better mousetrap. If you think about parking today. Historically, it's been this idea of you press a button, you take a ticket. How inaccurate is that solution to actually capture revenue? By deploying computer vision, we could have a more perfect mousetrap. We could actually understand what's happening at any given moment. What that revenue leakage actually looked like, and because of that, we're able to drive significant revenue. Incremental revenue to the real estate owner.

Pablo Srugo (00:10:58) :
So this means what? Charging me, let's say by the minute versus having to round it off? Kind of optimizations like that?

Alexander Israel (00:11:04) :
It's less about the optimization of how we charge the consumer and it's more how many times you've been in a parking facility, and you've just driven up, and driven out. Because the gate was up. So think about the inefficiencies that manifest itself in the market and we were just, because we're vision. Because we actually understand what's happening in a parking facility at any given time. We're able to capture revenue and both provide for a better experience for consumers, is the gate simply goes up. And for real estate owners, we're actually able to capture the revenue that's actually driving in and out of the facility at any given moment.

Pablo Srugo (00:11:37) :
So that's all super helpful. So just going back to the timeline, when you decide to start this business. I mean, at this point, you've you had an exit, you're kind of a proven founder. What's step one? Do you get the team together? Do you raise some money? How do you set things up?

Alexander Israel (00:11:48) :
So early on, it was myself and three other co-founders. And we were balancing, back and forth the idea of starting a technology company. And while I'm dangerous, technically, I'm also not an engineer. So early on, we brought in our CTO, Eddie Thomas. Who is kind of a renowned Southern California entrepreneur and we started to assemble a team. And we started to put together not only our go to market strategy. But really, what was a best in class team going to look like? And we went out, and we raised our initial seed financer. So that was, I think, $7.5 million and we thought we were rich.

Pablo Srugo (00:12:33) :
When was that?

Alexander Israel (00:12:35) :
Right at the end of '17.

Pablo Srugo (00:12:38) :
How do you think about building out, you know, what does the MVP look like? How do you set things out? Do you just have a bunch of design partners? Because it's actually, now that I as I think it through, it is a big ask in that if this thing fails for whatever. You can't really just test this, you know? You think about normal software, you just kind of have it on the side. The stakes are kind of low here. It's like, we're going to take over the charging of your parking lot. So as much as the idea is simple, the ask is pretty big.

Alexander Israel (00:13:06) :
It's really big. I mean, especially if you consider the amount of revenue that's going through these locations at any given time. You're asking a real estate owner that is often part of a conservative cohort, to hand you over millions of dollars in revenue.

Pablo Srugo (00:13:20) :
Well, that's by the way. That's an interesting point. Status quo is powerful and you have very serious status quo you're going against. Because things are working, like they've got a parking lot. It's been making money for a long time. It's not like they have a hair on fire type problem, where things are breaking and they're like, please come in here and just change everything, right? It's like, OK, I've got to think that's pretty good. You're saying going to make it better. But, you know, I don't know.

Alexander Israel (00:13:41) :
Yeah, it just works. It just is working. So yeah, you're right. That barrier to move from status quo from the existing operating paradigm to an entirely new model, is a massive hurdle. It's a massive hurdle. So, to your question, we hired a creative director. We brought in engineers, and we handled business development internally by ourselves. But we also understood that there was going to be this dichotomy of what technology we wouldn't be able to build on a proprietary level ourselves. So we also started to leverage third party. So we use third party cameras and we use third party technology to really start to bring our go to market to fruition. We didn't have our own proprietary AI stack at first. We had components, we didn't vertically integrate the entire industry and we went out, and we started to identify early adopters. Customers that we thought we could start testing the product with and the easiest way to go about doing that was to actually lease locations. So instead of asking an asset owner to take risk, we took the risk and we leased locations. We started with a location in two locations in Venice. That we leased from real estate owners and we actually took over those locations. And we deployed our technology just to see what that consumer adoption would look like. And to see if this idea that we could drive not only the cost down to operate these facilities, but if we could actually tangibly drive incremental revenue.

Pablo Srugo (00:15:16) :
Was that your first choice? Or was that the result of you trying to pitch parking lot operators and realizing, okay, this is a bit of an uphill battle unless we either do it ourselves or prove it ourselves?

Alexander Israel (00:15:27) :
You know, I don't think we knew the answer at the time, right? I think that we were talking to real estate owners. We were hitting a little resistance. We definitely knew that we weren't going to at first get the class A office building owner with a location that was collecting $5 million of revenue to hand us their keys. We knew it was going to have to get there gradually. But I don't think we knew that initial complexity. So we quickly, at least initially, pivoted to leasing locations.

Pablo Srugo (00:16:01) :
And then how did that go? Tell me a bit about that first deployment.

Alexander Israel (00:16:04) :
It was awesome. I think back to that all the time. We called the location M1, Metropolis One. It was our first location and there was a real sense of pride for myself, and the founding team. Once we had, we launched our computer vision, capturing our first vehicle, that first transaction. I still remember to the day, that first moment, that first ping. When someone registered with Metropolis, became a member and then transacted for the first time.

Pablo Srugo (00:16:31) :
What is the membership thing? I would assume you're just taking the picture of a license plate in and out. Is it more than that?

Alexander Israel (00:16:37) :
Yeah, so at this point, you know, you can think about it in the context, Pablo. That we have somewhere between sixty and eighty thousand people sign up with Metropolis every single day. And what that means is they're becoming members. So the way someone becomes a member is they enter their credit card, their license plate, and their phone number. They create a profile and from that moment forward. They can pull into any Metropolis-enabled facility anywhere in the United States. Get a text message when they arrive and are seamlessly charged when they leave.

Pablo Srugo (00:17:06) :
And how did you do it at your first location? Just walk me through that, like, somebody drives in, you have signs there. How do you get them to download this app and create this profile, and all this stuff?

Alexander Israel (00:17:15) :
First person drove in, they scanned the QR code, they entered their credit card, their license plate, their phone number, they parked. In this case they probably went into an Erewhon. They went into Erewhon. When they were done shopping, they drove out of the facility and I got a text message on my phone that, that first transaction actually was just a ping. It was a Stripe ping, saying that first transaction. That first consumer went through our system and became a member on the Metropolis platform.

Pablo Srugo (00:17:42) :
And how are you measuring success? Obviously, zero to one is always amazing, and that first transaction is always great. But then, OK, a few weeks go by, a month goes by. What does success look like in this first deployment?

Alexander Israel (00:17:52) :
So for us at the time, it was about revenue capture. So how many people were actually coming in, they were actually capturing, converting to members and then as people were converting to members. How were we thinking about this idea of repeat utilization? So we knew we had a new member. and then how do we start to conceptualize this idea of an active member. Where they were actually come back, time in and time out.

Pablo Srugo (00:18:15) :
Did you compare that to other, status quo to benchmarking to say, OK. Because your technology things are ten percent, twenty percent better in some important vector?

Alexander Israel (00:18:24) :
You know, it's interesting, Pablo. The baseline is actually not there, right? So if you think about traditional parking, there's no data. All you have is tickets in and tickets out. It's a really imperfect mousetrap. So there really wasn't a baseline for us to compare to. So we knew that we didn't have data historically, and now through the deployment of our technology. We did have data and then we started to think about what was the frequency of those members coming back, and utilizing the location? What was the lifetime value of those users? So, at that moment, I have this affinity. Because I remember we set up the location, and I remember receiving that first ping from Stripe. That the first person had paid and I remember sitting with my wife. And we used to hear these pings on my phone. Ping, a minute later, another ping. Two minutes later, another ping and I remember it got to the point. Probably at the end of 2018, where the pings were so frequent. That it would drain my battery and then at this point. We're talking hundreds of thousands of pings a day. So it just, it's no longer viable to have that ecosystem on our phones.

Pablo Srugo (00:19:34) :
When did you decide to get the second location? How did you kind of scale, let's say from one to ten locations. Over what time frame and was it all through this leasing model?

Alexander Israel (00:19:43) :
So we probably started using the leasing model until we had eight locations. We had M1 through M8 and at M8. I think I started to lose count, you know, now we're at M4,600 and it's untenable. But I think through M8, we were leasing locations and I think by M9. We started moving to a model, where we were just managing the locations.

Pablo Srugo (00:20:10) :
Okay, but just walk me through today, for example. How much is this a software sale to a different operator? How much is it you're leasing this space? How much is it you are the operator on record? Maybe walk me through just the setup. I mean, I know nothing about the parking lot kind of industry.

Alexander Israel (00:20:24) :
You know, it's really interesting, Pablo. We had a really unique go to market and product market fit. So we founded the company. We scaled to about fifty locations organically and relatively quickly. And what we found is that we had product market fit and we had unit economic fit. But what we didn't have was go to market and what I mean by that is we'd go in, and we still had this problem. Where we'd sit down with a commercial real estate owner. We'd ask to take over their locations and they'd basically look at us, and say, cute startup. Come back in 30 years. Back to this idea that the real estate owners didn't want to test new technology. As an asset class, it's so risk adverse, that customer base.

Pablo Srugo (00:21:09) :
We have tens of thousands of people, who have followed the show. Are you one of those people? You want to be part of the group. You want to be a part of those tens of thousands of followers. So hit the follow button. They're a real estate owner, they own that lot. They're managing it themselves or are they paying a third party? Usually to manage that space for them and they split it or something like that?

Alexander Israel (00:21:26) :
Normally they're paying a third party. So you think about parking operators like staffing agencies. Think about parking operators like janitorial agencies. The only difference between a parking operator and a janitorial agency that would clean an office, is in this case. The parking operator still cleans the lot. But in this case, they collect all the revenue and remit the revenue minus their expenses back to the owner.

Pablo Srugo (00:21:48) :
So that was the traditional kind of industry that you're walking into.

Alexander Israel (00:21:51) :
Yeah, exactly and we were going to come up with a SaaS platform. And we did come up with a SaaS platform that also had managed services on top of it. And we would replace the traditional parking operator. And in this case, while we had a better mousetrap. While we had unit economic fit. While we had product market fit, the go to market was slow. The go to market was slow, because this risk-adverse counterparty. A risk-adverse customer that didn't really want to hand the keys over to the multi-billion dollar building to a startup. So we shifted and pivoted strategies, Pablo. We moved from this organic model to what we qualified as the GBO. The growth buyout. This idea that a technology company could actually buy an old world business and use that to catapult, streamline, and accelerate our go to market. So that's exactly what we did. We bought a parking operator based out of Nashville, Tennessee. Probably the eleventh largest parking operator in the United States. Company was doing around $10 million of EBITDA and that had never been done before, right? You don't have technology companies buying these old world businesses.

Pablo Srugo (00:22:58) :
How did that pitch go to VCs or whoever it was that was backing you at the time?

Alexander Israel (00:23:02) :
Completely over their head, absolute non-starter, absolute no interest.

Pablo Srugo (00:23:08) :
Are you kidding me?

Alexander Israel (00:23:08) :
Yeah, diluted, crazy co-founders. Absolutely not, right? And that's the capital markets, you know, venture. Everyone else works in these very narrow boxes and it was a hard pass. Luckily, we ended up putting together a best-in-class group of investors. Those 3L, Dragoneer, Silverlake, Eldridge, put together a syndicate and acquired the company. And this company called Premier, and really brought a new strategy. That's now kind of proliferated across the industry. This idea that venture capital firms or venture capital investments couldn't go out and actually buy their growth. So that's exactly what we did. 

Pablo Srugo (00:23:49) :
When you buy this operator. What you get is their leases, their contracts. You just, you become the kind of the operator. Well, they're the operator of record, but now you can just force-figure technology into them.

Alexander Israel (00:23:58) :
Exactly, so they. There are two structures, Pablo, within the industry. There are leases and management contracts. Ninety percent of the industry is just management contracts. So it's all asset-like, but you're right. They had about four hundred locations that they managed and allowed us to buy them, and then install our technology across their base. So that's exactly what we did.

Pablo Srugo (00:24:17) :
The thesis is whatever EBITDA they had, you would get a lift on that. Because you would be able to generate more revenue. Lower costs kind of across the board.

Alexander Israel (00:24:25) :
Exactly, that it would be a much more efficient mousetrap and it would be a rapid go to market.

Pablo Srugo (00:24:30) :
By the way, just one aside. I'm curious about what happened here. One of the things you mentioned is when you go and you talk to the owners. It was hard to get them to kind of buy in. What about these operating companies and them just using your tech versus you buy them out? How did those discussions go?

Alexander Israel (00:24:45) :
It was for us at our core. We knew that was going to be an impossibility. One, these organizations as a whole are staffing agencies. They are primarily driven by the revenue and cost plus model they can associate with their staff, with their human capital. So they were not going to be interested in deploying technology and they weren't going to maintain the SLAs that we wanted to provide this remarkable experience for consumers. So it didn't provide for a go to market solution for us.

Pablo Srugo (00:25:17) :
It's interesting this model. This is something I'm going to take you completely off tangent, but in the world of AI and AI agents. It's something I've been thinking about a lot, which is a lot of times these companies are, for example. They have some new AI agent that'll replace, you know, a certain layer of work and they'll go to the companies, the buyers, and they'll say, look, you've got ten employees doing task A. What if you could just put my technology and instead of having ten employees. You'd have like three, but it's kind of half of the solution. The other thing, which is kind of what you did in beginning this tangential world is like, just pay me. Don't pay these ten people at all and I'll just do it all. Now you have, you don't have tech margins, but you're delivering the entire value. It's kind of similar in the sense where you just become the operator. Instead of trying to get operators on board or instead of trying to convince them to swap out the operator. Just take the operator. Yeah, sure, you won't have your perfect, ninety percent SaaS margins. But you just deliver the full value and you kind of force the market to see the world the way you see it.

Alexander Israel (00:26:15) :
A hundred percent. I mean, you're starting to see, Pablo. Companies like, General Catalyst buying hospital systems. You're going to start seeing this across the venture capital and private equity ecosystem. Whereby you're taking technology companies, best-in-class technology, best in class artificial intelligence, and you're pairing it with best in class operations or relatively old-world businesses. Where you can see that immediate synergistic value. Now, it doesn't really exist within the capital markets. You don't have venture companies that often want to take M&A risk and you don't have private equity firms that want to take technology risk. But I think you're going to see a new emergence around this GBO. This idea of a growth buyout, that you can merge these two risk return profiles into something very compelling and very unique.

Pablo Srugo (00:27:07) :
The idea of product market fit definitely changes in this construct. Because you're taking an existing business that obviously has product market fit. It has EBITDA, it has profits, and you're just making it better. But obviously you're not going to lose the PMF element of it. As you do these first buyouts, right? You buy this company you're mentioning. What are you looking for that tells you, that you have success? Because you can't just look at revenue. Revenue is going to jump crazy up, because you just bought the company that has revenue, right? EBITDA is going to be higher than it was before the acquisition. Because you just bought all the EBITDA. What are you looking for in terms of improvements that tell you, yeah, this strategy of mixing one plus one really equals three?

Alexander Israel (00:27:40) :
So Pablo, for us, we focused on what we qualified as gross profit uplift. So you take location A and it has a gross profit profile. And then what does that gross profit profile look like through, and after the deployment of technology? And do you see an uplift? That's really the variable that we look to in the context of the viability of these M&A strategies or these growth buyouts.

Pablo Srugo (00:28:08) :
What kind of lift were you seeing?

Alexander Israel (00:28:10) :
So we were profoundly shocked by the lift. You know, it was interesting. I had one of our investors at Eldridge, Tony Manila. Look at me six to twelve months after the first transaction and ask me the value of Premier Parking. What was Premier Parking worth nine months, twelve months later? And I had no way of conceptualizing the value. Both because we had merged the companies, but also because I was a hammer seeing a nail. I really wasn't focused on the value that I was driving to Premier. I was focused on the value Premier was driving to Metropolis. How Premier accelerated our go to market. How Premier accelerated our product market fit. How we could drive significant adoption of our product through the deployment across their network. So we spent some time running an assessment of the value that we in fact were able to drive to premiere and we quickly found that we had 2.1x the gross profit over that period of time.

Pablo Srugo (00:29:11) :
Wow.

Alexander Israel (00:29:11) :
And I think to your wows. I think that's exactly how our board and how we conceptualize the opportunity in front of us. That if we could do that, we should go do it again. So that's exactly what we did and we partnered with Goldman Sachs, and BDT MSD. Did an assessment of the market, partnered with Eldridge, looked at all the players across the United States and identified that the largest player in the United States in parking was a company called SP Plus. Largest publicly traded parking operator in the United States and we should acquire them. So we set out to do exactly that, to truly accelerate our product market fit and our go to market strategy.

Pablo Srugo (00:29:49) :
When did you acquire them?

Alexander Israel (00:29:50) :
We announced that deal at the end of '23. Closed in May of '24.

Pablo Srugo (00:29:54) :
How many people in this organization? How many new employees did you inherit?

Alexander Israel (00:29:58) :
We have twenty-three thousand employees now.

Pablo Srugo (00:30:01) :
What happens the day after? You buy a company with, I mean, tell me how many thousands of employees that company had and you try to absorb them all to your organization, with your culture and all these sort of things. How does that work out?

Alexander Israel (00:30:11) :
It's complex, it's nuanced, I spend a lot of time talking about culture. There's this Drucker line about culture eating strategy for breakfast. But once you have twenty-one, twenty-two, twenty-three thousand employees. You realize that's so true, right? If you don't have alignment, if you don't have one organization around one value set, around one objective set, everything falls to the wayside. So we spend a profound amount of time building one culture or one strategy across that employee base and it becomes a core focus of mine. And the executive teams on a day in, and day out basis.

Pablo Srugo (00:30:53) :
What are some of the specifics around that? How do you integrate different cultures together?

Alexander Israel (00:30:57) :
So what I would say is core are two primary variables. One is strategic clarity and the other is role clarity. So on strategic clarity is, what is the mission of the organization and does everyone know it? And then two, what is everyone's role or respective role in driving that strategy? So how do I participate? How do I feel that I am valued as a piece of a larger strategy? And how do I understand my performance in driving the overarching objectives of the company?

Pablo Srugo (00:31:31) :
And if you buy in one of these companies. Do you typically kind of go in and reassess? Especially because you're using technology. How many of these people you need? Or do you just absorb everyone? How do you think through that?

Alexander Israel (00:31:41) :
So back to this idea, Pablo, of cost synergy versus revenue synergy. We spend a lot of time internally leveraging technology to elevate, escalate, and really enhance the individual. It's less about replacing the individual with technology and that's the same with our real estate partners. How do we leverage technology to drive incremental value to the labor that's already being deployed at the location? It's less about removing labor from the ecosystem or from the funnel.

Pablo Srugo (00:32:11) :
Now you started talking about kind of computer vision is one of these big trends that you were kind of walking into. Where else, we're talking a lot about parking lots, is that all that Metropolis does? Or are there other elements of a city that you plan to kind of put computer vision into?

Alexander Israel (00:32:28) :
So Pablo, that's a great question. So what we're finding is we're building out. We propagated thesis one, which was this idea of the GBO. This growth buyout. A new paradigm as it pertains to go to market and product market fit. On the other side, we quickly realized that as we were scaling to at this point, twenty million Americans on platform. As we scale to every one to two seconds, someone signs up on our platform twenty-four hours a day, seven days a week. Sixty-five to eight thousand new members every single day. At this point, we're scaling to north of one million new members every single month. As we've propagated this growth buyout strategy. Now we're propagating, what I would qualify as the recognition economy. This idea of how you leverage computer vision technology, both in mobility and outside mobility. To create these seamless checkout-free commerce experiences everywhere. So we started with parking, but now we're moving past parking and we're moving past parking to every mobility vertical where payment is present. Gas stations, car wash, drive-through, tolling, to facilitate that same seamless experience that our members expect of us. Time in and time again. So you pull up to any Metropolis-enabled facility, whether it's a drive-thru, whether it's a gas station. Get a text message when you arrive and are seamlessly charged when you leave. Not fumbling with credit cards, not fumbling with receipts, seamless checkout for e-commerce everywhere you go. Starting with mobility, but now we're moving past mobility. How do we leverage computer vision technology to move with you? How do we leverage computer vision technology for a hotel? For a coffee shop? Think about how many times you order that same cup of coffee time in and time again at the same coffee shop. Think about how many times. Pablo, you go to a classy office building in New York City. You have to present your identity, you have to present your belonging, you have to present your ID. What if you just seamlessly walk in? So how can we leverage computer vision to do all of that, to really give you back what matters most which is time?

Pablo Srugo (00:34:33) :
How do you go to market with that? Take a gas station or a drive-thru, does the growth buyout model work or is it a completely different model for these verticals?

Alexander Israel (00:34:41) :
Honestly, Pablo, it's back to our basics. It's back to direct B2B SaaS. So you go directly to the largest chains of quick serve restaurants, you go to the largest chains of gas stations and how do you take our membership base. That twenty million members, and how do you bring them along with you to create those seamless experiences in gas stations, in car washes, and in drive-thrus.

Pablo Srugo (00:35:04) :
Awesome Alex, let me stop it right there and I'll ask you the last kind of three questions. That we always end with. The first one is, when did you feel like you had true product market fit?

Alexander Israel (00:35:14) :
I would say it was relatively instantaneous. That first ping on my phone. My co-founders and myself were sitting in the parking lot watching and it was this interesting moment, Pablo. Because it was seamless. It was the moment when I realized the technology that we were deploying was so seamless, was so frictionless at first. That the user didn't realize they were using the product. That we had to insert more friction into the funnel. Think about how unique that was. Think about how unique it is that as a product we don't measure ourselves by how much time you spend using our product. We measure ourselves by how much time you spend not using our product. We want you to sign up one time and never use Metropolis again. In the context that it's always running in the background. It's always a super intelligence with you everywhere you go facilitating seamless commerce. You don't have to pull out your phone. You don't have to pull out your credit card. So it was that first ping, that first transaction.

Pablo Srugo (00:36:16) :
And was there ever a time kind of on the opposite side of the spectrum. Where you thought things might not work out and just completely fail?

Alexander Israel (00:36:25) :
Look, I think that's the double-edged sword of being a founder, right? This idea that you have to be deluded. That you have to run face first through a wall, time and time again. So look, I always am humble. I'm always worried that we're going to fail. I always have that anxiety, that angst. But as we build our products as more and more, and millions of members interact with our product every single day. And we build such a remarkable team. It gives me confidence, it gives me a sense of ease, but I always remain defensive and I always remain offensive of the context that. I think Bezos said it best when he talked about the life cycle of a business. That you're trying to starve off the failure of a business. So we're always trying to build innovative products to drive remarkable value to our members and our customers to do exactly that. To push back the inevitable failure of every company.

Pablo Srugo (00:37:23) :
If you could go back to when you were starting Metropolis in 2017, with one piece of advice for yourself. What might that be?

Alexander Israel (00:37:29) :
One piece of advice for myself at the beginning of 2017. Get more sleep! I don't, you know?

Pablo Srugo (00:37:38) :
Well, here's the other way to formulate it. If you had one piece of advice for an early stage founder, that's either starting or trying to find product market fit. What could that be?

Alexander Israel (00:37:45) :
I think as I talk to many founders starting their first company or founding their second company, or maybe the third company. We always talk about the roller coaster of founding a startup, right? On the same day, you're going to have your highest low and your highest high. And that tenacity, that resolve is what's going to keep you going. And I'm a huge advocate of finding co-founders, that you can work with day in and day out that become your brothers in arms. That you sit in the trenches with and you're working through problems, and coming up with remarkable solutions. For me, it's about that tenacity, that drive, that resolve and that belief that you can do something that is profoundly difficult that many other people would fail at.

Pablo Srugo (00:38:33) :
It's funny the lows to lows, the highs to highs and what co-founders complain to that. Because it's almost like the day that you're feeling the worst, you go and you talk to your co-founder. And they're just not feeling as bad. And then the day that you're feeling great, and you think you're gonna go tickle it over the moon. You talk to your co-founder and they're not feeling as good either. There's almost this balancing act that happens in that dynamic.

Alexander Israel (00:38:51) :
It's critical. I have a dear friend, Jamie Siminoff, that founds companies on his own. I don't know how you do it. I don't know how you do it.

Pablo Srugo (00:38:59) :
Cool, well, Alex. Thanks so much for jumping on the show, man. It's been great.

Alexander Israel (00:39:03) :
Pablo, this was wonderful. Thank you for having me on.

Pablo Srugo (00:39:06) :
Wow, what an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me, they will love you for it.