All right. Hello everyone. My name is Ro Dewan. I'm the Co-Head of our Fintech Investment Banking practice. I'm pleased to be joined by Paul Gu, who's the Co-Founder and CEO of Upstart. Looking forward to the discussion today.
Great. Excited to be here.
To ensure that I don't get my wrist slapped, I'm going to read a disclaimer before we get into it. Today's discussion may contain forward-looking statements that relate to future results and events, which are based on Upstart's information available as of today, and are subject to risks and uncertainties. Actual results may differ materially from these forward-looking statements. Our discussion may also include non-GAAP financial measures, which are not a substitute for GAAP results. Please refer to the company's filings with the SEC and its IR website for additional information, including GAAP to non-GAAP reconciliations along with other disclosures. All right. Maybe with that, AI is the leading AI lending marketplace connecting consumers to hundreds of partner banks and credit unions.
Enabling an automated and streamlined borrowing experience, Upstart has expanded access to credit for millions of consumers and improved risk decisioning, leveraging its advanced AI models. Paul, you co-founded Upstart, what, 14 years ago now?
14 years.
2012. 2012. You previously served as the company's CTO, where you helped build and scale the core technology and AI-driven credit platform. Congrats on your recent appointment to CEO, and appreciate you being here today. Maybe with that, just take us to the beginning. You co-founded this company 14 years ago. You dropped out of Yale as a Thiel Fellow. What was the fundamental problem that you were trying to solve? Just think back over a decade. Did you set out to build an AI lending marketplace? What was the vision back then, and what, and how has that evolved over time?
Yeah. Way back, when I was 20 years old, I, as you said, I dropped out of college. I did the Thiel Fellowship. Before This was before Dave and I had met, Dave, my Co-Founder, who was CEO until very recently. For my side of it, I had spent a summer after dropping out of college at D. E. Shaw, and generally was very interested in playing around in the world of quant finance.
My observation there was that you had very smart people using really sort of cutting-edge computing techniques to go after a problem that I thought was a very narrowly defined problem that was like, how do you arbitrage securities prices to be a little bit more efficient, but to go after that problem with extreme amounts of accuracy, extreme amounts of sort of intelligence. It occurred to me that if you could just take these same techniques and apply them to a problem that would impact a lot more people, you could solve something that would be fundamentally very important for the world.
Naturally, you take just one step from the world of kind of maybe securities finance to the world of consumer finance, and you say, "Okay, are there problems that you could solve here by using these techniques?" I thought that if we could use very similar techniques as are used in sort of the world of quantitative finance, but apply it to this problem of how you understand consumer risk, you could get a lot more people access to credit that maybe they didn't have or they didn't have good enough of. That's what I wanted to do. I met my Co-Founder, Dave, while thinking through this idea and working on the very first versions of it.
We got together because we both thought that the right packaging of the solution would be something called an income share agreement, which is basically an idea that you give someone money in exchange for a small fraction of their future income. Kind of like a loan, but not really. What it had in common with the thing that the business later did was that, one, we were solving a credit access problem, and two, we would solve it by building models that could understand the financial trajectory of someone who didn't necessarily have a lot of conventional credit history. Pretty quickly, what we realized once we got into this business was that this was exactly the right problem to be solving. Tons of people had it. It was a huge market opportunity, and the incumbents just weren't going after it.
That also, we could solve this problem with this particular technology approach of building machine learning algorithm-based models on alternative data for consumer underwriting. We started to do that, and we discovered that, in the sort of conventional loan format where, you know, people understood what the APR was, what the repayment terms were, where you didn't have to explain to someone, like, what an income share agreement was or why it wasn't indentured servitude, it just worked much better. We did more and more in that business. We quickly discovered that it wasn't just people who were new to credit or didn't have a lot of credit history who had this problem. It's really people across the entire economic spectrum that are underserved in terms of credit.
Either they don't get approved enough, they don't get the right APR for them, or they get served an offer of credit that comes with a huge number of procedural steps and verification required. All of these people could be much better served by an AI first way to do credit. That's what we started to invest all of our energy and attention into, and that's how we built the business that we have today.
I like how you framed the problem. Again, think back 14 years ago. How is the problem similar versus different today than it was a decade ago?
Surprisingly very similar. One of our favorite metrics is a metric about the amount of inaccuracy in lending decisions. If you said 100% is a metric where you have, just a totally random model, you have no intelligence, you're just making lending decisions at random, 0% means that you have gotten rid of all of the inaccuracy and now you have a totally perfect model that can get everything right. A sort of textbook traditional model lands you at about 95%. That's how much of the error a traditional textbook model leaves on the table. Our model, after 14 years of optimizing and improving at a really, what we think is really a decent pace, has us down at 86%, which is to say that 86% of all the error in lending remains to be solved.
That means that, you know, if you were just to start from where we are today and look out, you would say that there is an enormous amount of opportunity for models in lending to get more accurate, to understand the consumer risk better. There's more opportunity to get more data into the models, more opportunity to build more sophisticated algorithms that understand the signals from that from that data better. It frankly, you know, the difference between 86 and 95, like zoomed out, doesn't look all that big. I think if I were starting a company today, it probably wouldn't be much different than the one that we started 14 years ago.
Let's maybe shift gears a little bit on your role. You're now the CEO, you were formerly the CTO. If I just think about that dynamic, Satya, Sundar both had kind of technical backgrounds and experience, became CEO. What's gonna stay the same? What's gonna change from your vantage point?
We always had a strong conviction that the answer to making consumer lending fundamentally better and different was gonna come from technology. I think that's probably always been our most contrarian take on this market, is that if you look at most players in this business, they don't have a technology-oriented thesis. They have either a capital markets or funding-oriented thesis, like they have some sort of better way to get a lower cost of funding, or they have a sort of marketing-oriented thesis. They have a better way to to sort of get the consumer into their ecosystem, to have customer loyalty or to get customer acquisition.
Our thesis from the very beginning was that those things are nice, but the most transformative thing that you can do in lending is if you can actually change your understanding of the risk, so you can get dramatically better separation of risk. That's what allows you to approve people that otherwise couldn't get approved or approve them at much lower APRs, and therefore have the pricing power to generate unusually high margins and build a really fantastic business. I think that comes from technology. You have to be a really good technology company. When I say technology, I don't mean in the sense of, you know, just building a website or, you know, writing a bunch of code that has features.
Really like technology in the sense of, I think being a research-first company that fundamentally is about building models and building more accurate models is sort of much deeper type of technology than I think your, maybe your typical software company. I think the DNA for doing that as a company is fundamentally a very technical thing. I think in that sense, you know, we've always had that conviction. I think that's even more true for me, you know, coming from the seat that I sat in before, and I'm gonna be very focused on making sure that we extend that lead, grow that lead, and keep investing in it going forward.
That's great. We've talked about the past. Let's flash forward 10 years from now. AI permeates the consumer finance space. How does the experience change for the average consumer? That part one. Then part two, just as you think about AI for your business specifically, are there certain things that you're seeing an acceleration in with respect to operational performance costs, et cetera? Or is it more driven kinda top-line basis?
Yeah. Today, I think we live in a world where if you want credit, a bunch of things have to happen. One is that you have to know that you want or need credit. You have to know which type of credit is the right one for you. That could be a HELOC, it could be an auto loan, it could be a refinance loan, you know, it could be an unsecured loan, it could be another kind of revolving loan. You need a lot of knowledge and sophistication as a consumer to know which one is the right one for you. Then you have to have the willingness to actually do the work required to go and get that loan. When you put all those steps together, it's no surprise that the vast majority of people just don't do it.
They don't get the right form of credit at the very lowest price at the right time for them. Instead, what you see is you see a lot of people sit on really large amounts of credit card debt for a large number of years accruing at a very high interest rate, not because that is the very best price for them in the market or the very best thing for their financial lives, just because of all of those steps that you have to go through.
I think instead, we can get to this world where credit can be what we call always on, just there for you, where you can have, sort of AI that helps people figure out what the right form of credit for them is, and then you can make it effortless and instant, as you know, the vast majority of our loans are today to actually get that credit so that there's no friction between, a consumer and doing the thing that's best for their long-term financial health.
As you think about that always-on credit, which I think is the right kinda frame, what are the different ways to potentially monetize that over time?
The beautiful thing about credit is that if your credit is actually differentiated, it's just a fantastic business, and we see this with our core personal loan business. You know, when we serve a personal loan to a consumer that's not rated conventionally prime in the market, this is a consumer that gets a huge amount of value from our loan, and as a result, we have a lot of pricing power in that product. We're able to earn very good margins in that product, and that basically happens because it's such a differentiated product and has a ton of value creation for the customer.
I think in a similar way, when you think about something like the always-on credit, it's, you know, it's first of all, it's credit, and if that credit has the same great underwriting attributes, the same level of technology differentiation as all of our credit products today do, it's gonna have the same margin profile, same margin tailwinds. It gets an extra benefit, which is that today, especially in our business, we continually have to acquire customers for each loan as if it's a new transaction, a new customer, and that comes, of course, with customer acquisition cost.
I think in a world where you have people that are living within the ecosystem and the credit is coming automatically as just a part of being in the ecosystem, then obviously, you know, you're gonna be able to amortize that acquisition cost over a much longer customer lifetime.
As you think about the products and you talked about the customer acquisition cost, top of funnel, do you lean towards one specific type of product or type of customer because you've seen the customer acquisition cost for that being lower all else equal, and as they move down the funnel and become multi-product customers, the overall opportunity is greater?
I'll say two things. First is we're very committed to having the best product for Americans of every category, people up and down the economic spectrum. Anyone should be able to come to Upstart and for any use case of credit. Obviously not, we're not quite there. We don't have all the products yet. For any use case of credit, eventually you should be able to go to Upstart and just confidently know you're gonna get the very best credit product. Having said that, a second thing is also true, which is that some customers are much less well-served by existing credit markets than others, and those consumers that are not conventionally rated super prime tend to have many fewer good options in the market.
Conventional issuer will rate their risk of defaulting much higher. The space to reduce their prices, improve the speed of their process, improve the sort of size of credit lines that they have access to is much greater, and therefore, our ability to build a great business there is much greater. We will increasingly going forward be focused on how we can grow the most in the segments where we have the most differentiation while maintaining the baseline fact that anyone can come to Upstart and get a very good product.
You talked about a few of your different products. Let's double-click on just the product roadmap and that journey. You started out as a single product company way back when, right? Unsecured consumer lending. It's now a multi-product platform that's had a great amount of success spanning auto, HELOC, and now revolving credit. As you reflect on the journey, is that kind of how you imagine things to happen sequentially? If not, maybe just share a little bit around the dynamics of how you decided to prioritize expansion into one product over another.
Yeah. I would really say, actually, this is a pretty new thing for us. Until very recently, we effectively were a one product company. We had a personal loans business and, you know, just a bunch of exploratory bets.
I think really as of the last quarter, I would say that we are really have grown into our own right as being a multi-product company. We have shown that we can deliver real growth and distribution in our auto purchase product and our HELOC product, those products are, I think now well past the exploration phase. They've sort of made it on their own, I think are going to be very good businesses for us. I think in terms of the order, probably if I could do it again, I would do the order a little differently. We just announced a new product called Cash Line. It's actually a natural adjacency to our core personal loan customer segment and core personal loan product.
If you look at the success that a lot of players have had in that market, it was just a really natural product for us to be in. Probably, you know, the lift to get there was lower than the lift to do something like an auto or a home. I think those are really, really good markets to be in. They have enormous TAM. We wanna be growing in them for a really long time to come. The lift to get from zero to one on those products frankly took longer than we would've thought. I think especially in auto, you know, we've been humbled a little bit. I think if you asked us three or four years ago how long it would take for us to get stood up in auto, we would've just been too optimistic about the timelines.
We're really, really happy to have the auto business working now, but it was hard work to get there. We had to really understand the dealer as a customer, and that's something that wasn't natural to us coming from kind of a direct consumer background as a business. We just were naturally attuned to thinking in terms of the end borrower, and we've had to learn kind of all the idiosyncrasies of how car dealerships work, what they need to be successful, how to think about them as really our customer and that's a capability that we've developed over the last couple years that's enabled us to be very successful in auto, but it took work.
Maybe just while we're on that topic, one or two lessons learned that you can apply to the rest of your product roadmap, and then maybe just spend a little bit of time on, like, how do you think about conceptually, like, where do you wanna go next?
Yeah. Just working backwards, ultimately, we want to have the very best credit product for every consumer credit need. I think at this point, we're actually not that far away from that. We're probably just a few major products short. There's probably not that many degrees of freedom in how we go from here to there. We're gonna build all of those products. We're gonna make it so that if you are an American consumer and you need credit, Upstart is the place to go, and whatever stands between, you know, here and there, we're gonna build those things. I think there probably were more degrees of freedom in the past when, you know, we needed to build everything than now when we've built many of the things. I think we are well on our way.
Having said that, I think we just now have a much better understanding of the different pieces of standing up a new product in terms of managing the R&D on the balance sheet, managing, the sort of credit models and, you know, how you wanna ramp those. Thinking about, when you're first scaling distribution channels, having realistic expectations about getting the, you know, about when those channels are going to scale, at what kinds of caps. There's a lot of learnings in there. I think we kind of understand the life cycle of a new product now, and we'll finish the consumer credit suite soon.
While we're on the topic, just build versus buy framework. How do you think about that given the journey that you've been on?
Yeah. I always think that the things that you want to be best in class at, that you want to be really differentiated at, you really are gonna be your source of alpha in the market. Those are things you have to build. You need to own them, and if you're gonna make them better than what else is available in the market, then of course, you can't just buy elsewhere in the market because by definition that won't be differentiated, won't be the very best. I kind of think for everything else that you don't care to be the very best at, there's no reason to spend your own effort building it.
When people talk about, you know, "Oh, everybody is gonna build their own Salesforce or something, and there's gonna be no more vertical SaaS," mostly I think maybe for some company will decide to do that. For a business like ours where the growth rates are very high and the TAM is very large and you can sort of do that growth rate and compound for a long, long time, it's just very hard to justify spending your resources anywhere other than investing back in the things that are going to make you special and different. For us, all of lending is that. We want to be differentiated in auto, home, unsecured. Every major category of consumer credit, we wanna have the very best product in, so all of those are gonna be builds for us.
Okay. Maybe just shifting gears to financial profile. There's a lot of debate in the space around growth versus profitability. How do you think about that today? How has that shifted over time? Just in your new seat, just any dynamics relative to just the way that the business was operated historically?
Yeah. You know, we've always, taking sort of a 10,000 ft view first, we've always felt that consumer credit in general is insanely competitive market. There's a countless number of players that want to be in loans and have been since forever. There's nothing inherently interesting or special about just being another player in credit. What's interesting is where you can find places in the market that you can be really different and have a lot of pricing power, we've built that and established that in what we call the core personal loan segment, core personal loan business for us. That is a fantastic business. If you were to look at that on a standalone basis, it's a very high margin business it's done really well for us over the years.
It's also a business that we think can continue to grow at a very high rate because its fundamental driver of growth is just better technology. We make the models better, we can approve more people at lower prices, with more automation. That leads to higher conversion rates. That leads to more of the market being addressable. The growth engine is one that we're really well understood for a long time and we think that that product can just keep growing. It has not been our focus to maximize growth in that product because we've been focused on so many sort of adjacent priorities around rounding out the product suite.
This year in particular, we expect to see growth return to this core personal loan segment as we've put the focus of our product technology and marketing teams back onto it, and it's actually the natural thing for us to do. It's probably easier for us to do that than all of these sort of new and different things that came less naturally to us. This product naturally, as it grows, it's very nice because it both comes with growth and it comes with profitability, it moves things on both fronts. As that happens, I think naturally, you know, you're gonna see that sort of impact, you know, the income statement up and down.
I think over the course of the year, gradually we will see a sort of re-expansion of some of our contribution margins from local minimums that were just the result of sort of some of these shifting mixes. As that sort of shifts back towards growth in core personal loans, we will gradually see it go back the other way.
Let's go a little deeper on the growth side of things. If you think about just new customers, expansion within your customer base, and then things that are kind of pricing related, how do you think about that dynamic between those as you think about just driving your future growth?
Well, our growth always, you know, we always think about growth as in a mature product for us, the most important source of growth is gonna be higher conversion through better technology, and that will always be the sort of number one thing we come back to. We think there's a lot of that left to do. We can just keep delivering 1%, 2%, 3% type wins, and we have a bunch of teams that's mandates to do exactly that. We will keep on doing that. There's our new products. New products have a slightly different dynamic where sometimes they have product-specific marketing channels that need to be experimented with and activated and understood.
If you think about something like the home loans market and HELOC, for example, there's a lot of home adjacent businesses that you can have partnerships with. Those are like very specific channels that you have to come to learn how to work with and understand. Obviously, our auto purchase business, as we talked about earlier, that business has very specific dynamics that's distributed through car dealerships, you have to understand the car dealership as a customer. That's a distinct channel that you have to come to learn. New products have some of their own dynamics in terms of mastering channels that has sort of a zero to one dynamic and zero to one motion associated with it.
Once mature, then it really becomes a technology compounding game, and that's the game that we play in personal loans and one that we've really built the whole business around being good at doing.
That's great. I have one more and then I'm gonna open up to the audience.
Great.
I have a few more topics that I'd love to cover. You reported Q1 recently. Strong results. How do you bridge kinda Q2 through Q4 of the business relative to how Q1 performed? Is there anything that you think that the street is missing?
Yeah. Our Q1 results, I think the real sort of fundamentals of them, you know, the actual growth in the underlying business, the sort of proof points on new products, we think, in a long run sense of the business are really, really good facts. It's like we have a core business that is doing really well, can continue to grow for a long time, is very profitable. We have these new segments in home and auto that have enormous, you know, relative to personal loans, almost like unlimited sized TAMs that you can just grow in and compound in for many, many years to come. That's a really positive set of things for the business. Now, I think the market was pretty surprised by the bottom line results in Q1.
You know, we didn't have a very high EBITDA margin and you know, we did reaffirm the full year guidance. We expect that to ramp back up over the course of the year so that we still get back to the full year guide on that. That is gonna be pretty heavily backloaded in the year. That's a function of a few different things. Some of those are kind of like not strategically interesting, just have to do with like the timing of when certain expenses are growing in the year. Basically, you know, we expect that fixed expense growth is you know, sort of like disproportionately growing at the very beginning of the year. Basically, you know, it will moderate in terms of its growth rate from here.
It's not like it's gonna have a big step down or anything, nothing terribly unnatural. Just like the relative growth rate was very high in Q1 then just gonna be very modest after that. Then there's gonna be, you know, there's basically back to like the actual, you know, business and what's growing in it. We have a real focus now on growing in the core personal loan segment, this is a segment that has much higher margins. So as that increases its sort of percentage of all the growth that's happening throughout the year compared to, you know, the last few quarters, that naturally is gonna have an effect that pulls margins up. That, you know, that's because it's a mixed type dynamic, it's not something that happens overnight.
It is an effect that happens gradually. You'll see it in the contribution margin line, you'll see it in the sort of EBITDA line and, you know, you'll see it sort of up and down. It will happen gradually as that sort of mix of, you know, as a percentage of the growth comes in and therefore affects the overall mix of products and segments that we're originating.
I have a few more topics I wanna hit, but maybe I'll just open it up to see if anyone in the room has questions. Maybe if not, let's talk bank charter. There's multiple companies, including yourself, that have, you know, announced moves towards a bank charter. Just walk us through the evaluation of that decision and maybe what you see as the tangible benefits for that near term, medium term, and longer term.
Yeah. Our rationale for getting a bank charter are really different than I think many players in the market. We have long held that we want to be very capital efficient. We don't want the growth of the business to be tied to growth in the amount of equity capital that the business has. One of the challenges with being, running a bank business model is that those things can get mixed up. We have no intention of doing that. While we will hope to have a bank charter in the near future, we don't intend to use a bank business model to fund our loans. We will still be predominantly third-party funded and run in a really capital efficient manner. That is super important to us.
What a bank charter will do for us is it will give us much clearer and faster regulatory ability to lend and lend where we want to across the country. Today, in our current business model of lending across, you know, across a network of partner banks that do the actual origination from a legal perspective, there are a lot of states where you have a restricted ability to operate. You can't quite reach all customers in all 50 states. You also have a lot of costs involved in using this structure that because you have other partners that are, you know, taking pieces of revenue along the way before it gets to us. You know, there's just more efficiency there.
Finally, I think in a most fundamental sense, you know, today we have a lot of costs and process associated with maintaining regulatory relationships via a large number of intermediaries. I think as AI becomes a more central topic, it just is natural that, you know, as the AI lending company, that we establish a firsthand direct relationship with the regulator to represent, you know, how AI is gonna help the American consumer. We're excited to do that.
That's great. While we're on the AI topic, just you've been an AI forward company for a period of time. How do you think about just the end state and working backwards from where you're at today? What do you wanna see achieved over the coming quarters with respect to AI being infused further in your business?
Well, we've been doing AI and lending for a really long time. The thing about what we do is that it's a very different kind of AI than, say, when you're looking at, you know, LLM models or foundation models that are now, you know, very popularly used in a bunch of applications. Broadly, I think the problem space divides into problems that AI is better at people at and problems that, you know, AI sort of started out being worse than humans at. It's like are humans good at this problem or are they bad at this problem? A lot of the problems that we deal with are like giant math problems. They're like problems of the form. You have a ton of data about a person.
You know, you can know thousands upon thousands of different facts about a person, and then you have to crunch that through a bunch of historical data to decide, based on the patterns if you think this person is going to pay back a loan. That is a sort of a giant math problem. It's actually a problem that, you know, humans have been terrible at forever. It is not, was never a problem you want to hand even to a really smart person to decide how to underwrite a particular applicant for credit. It always required a very different kind of model than what you use, say, in, you know, in Claude or ChatGPT or something like that.
It was always, it was always something that was going to be much more heavily numeric and fairly proprietary to the type of problem that is present in lending. We've spent the better part of the last, you know, 12 years or so building models that are optimized for this use case. There's sort of a lot of Upstart-specific innovations that have made that successful. There's a class of problem that humans are actually pretty good at and are more like multimodal, sort of general intelligence problems. Some of those problems I think are now newly solvable because of what's changed and advanced in AI. If you think about some of the problems associated with, say, verification of a home loan, you're looking at property records, they're coming from a fairly offline county system.
They might involve like hand sketches of, you know, property borders, and you have to like decipher that, interpret, and decide whether that matches up with the property that you're trying to verify and place a lien on. Those are problems that multimodal sort of foundation models are very good at dealing with and are suddenly possible now. I think that there's something really powerful about the idea of bringing these two types of models together so you have the sort of like crunches large amounts of data to do a giant math problem and get really high numeric predictive accuracy, but something that, you know, a human would just be terrible at, is not sort of a general intelligence AI problem, is instead a giant math problem.
You combine that with those types of problems like the sort of home loan verification problem that is much more of a general intelligence multimodal AI problem, and now you can really get much closer to this world that we talked about at the beginning, where you can have this kind of always-on credit that gives people much better rates, much better access, and does it effortlessly from their perspective.
Always-on credit, how far along on that journey are you right now?
We're early. We're early. We recently launched a product called Cash Line. It's our first product that really is always on in the sense that Cash Line is a product where someone can be a regular subscriber to the product, and the credit will always be there for them no matter what happens. As long as, you know, they're meeting their commitments to us, our product is gonna be there for them. That is in some sense a very small first step.
You can imagine that within the same kind of subscription membership ecosystem, there will be other types of credit that we can start sort of bringing into that and making, similarly always on and available to the customer, having the sort of like always on underwriting, always on sort of data access to what's changing a consumer's financial life. Those are the other pieces of it that are getting tied together. Of course, the, you know, the last step will be bringing the really big kind of secured credit products into that, into that ecosystem.
I think you have something that is really different and powerful and suddenly looks very different than the way that people normally had traditionally had to get credit, where they had to go looking for it, you know, and decide that, you know, now is the time to, you know, refinance particular debt or other.
We're coming up on time. Just one last question from me. What's one thing that's either been misunderstood or underappreciated about Upstart?
I think people always have always thought that Upstart was a business where you had one clever mousetrap, you found maybe one little arbitrage opportunity in a model, and it was over. In the earliest days, that took the form of people asking us, "What's the one secret sauce variable that explains your success?" We try to tell them there's not just one. You know, there's a bunch of variables. Nowadays, you know, I think it's reflected in the perspective that I hear a lot, which basically boils down to, you know, when you look at how people think about the growth of this business, they think, "Okay, well, you found this mousetrap. You're basically going to arbit out in the next year or two, and then I guess from here you'll just cash flow.
It doesn't look like you have that much cash flow, so it must not be that good of a mousetrap." The very strange thing about this is if you actually look at what's happening in the business, it's like we have a business that is growing at an incredibly fast rate into a market with an extremely large TAM, so large that, you know, we're just a rounding error in the size of it. By every, like, every measure, this is a business that I believe can compound at an extremely high rate for many years to come. It's just gonna be much more sensible.
The math is very clear that the thing you should do in a business like this is you should make investments back into the business, make sure that you can maximize how much of this TAM you can address, how quickly you can go after it, and that's what we're setting ourselves up to do. We certainly hope to be compounding in this business for a very long time to come, and I think we're going to prove a lot of models were too short-sighted in how long they thought this could go.
This was great. Thanks for being up, Paul, and congrats again on the new role.
Great. Thank you.