Upstart Holdings, Inc. ($UPST)

All right. Hello, everyone. My name is [ Rod ] 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. 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?

2012.

And you previously served as the company's CTO, where you helped build and scale the core technology and AI-driven credit platform. And congrats on your recent appointment to CEO, and appreciate you being here today. So maybe with that, just take us to the beginning. You cofounded this company 14 years ago. You dropped out of VL as a Thiel fellow. What was the fundamental problem that you were trying to solve? And just think back over a decade, did you set out to build an AI lending marketplace? Or what was the vision back then? And how has that evolved over time?

Yes. So way back when I was 20 years old, as you said, I dropped out of college, I did the Thiel fellowship. And before -- this was before Dave and I had met Dave my co-founder, who was CEO until very recently. And from 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. And my observation there was that you hired 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. And 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. And so 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? And 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 didn't have good enough of. And so 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. So kind of like a loan, but not really. But 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. And 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. And 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. And so we started to do that, and we discovered that in the sort of conventional loan format where people understood what the APR was, what the repayment terms were, 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. And so 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. And all of these people could be much better served by an AI-first way to do credit. And so 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 frame the problem. Just again, think back 14 years ago. How is the problem similar versus different today than it was a decade ago?

Surprisingly, very similar. So one of our favorite metrics is a metric about the amount of inaccuracy in lending decisions. And so 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. And that means that 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 and 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 data better. And frankly, the difference between 86 and 95 like zoomed out doesn't look all that big. So 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.

Now let's maybe shift gears a little bit on your role. So you're now the CEO, you were formerly the CTO. If I just think about that dynamic, Satya and Sundar both had kind of technical backgrounds and experience, then became CEO. What's going to stay the same? What's going to change from your vantage point?

We always had a strong conviction that the answer to making consumer lending fundamentally better and different was going to 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 sort of get the consumer into their ecosystem to have customer loyalty or to get customer acquisition. And 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. And so I think that comes from technology. You have to be a really good technology company. And when I say technology, I don't mean in the sense of just building a website or 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. And I think the DNA for doing that as a company is fundamentally a very technical thing. So I think in that sense, we've always had that conviction. I think that's even more true for me coming from the seat that I sat in before. And I'm going to be very focused on making sure that we extend that lead, grow that lead and keep investing in it going forward.

That's great. So 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's part one. And 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 kind of top line basis?

Yes. 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. 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. And then you have to have the willingness to actually do the work required to go and get that loan. And 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. And 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 the vast, 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.

And as you think about that always on credit, which I think is the right kind of frame, what are the different ways to potentially monetize that over time?

Well, 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. 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. And so I think in a similar way, when you think about something like the always-on credit, 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 going to have the same margin profile, same margin tailwinds. But then 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're going to be able to amortize that acquisition cost over a much longer customer lifetime.

And 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 multiproduct customers? The overall opportunity is greater?

Yes. I'll say 2 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, we're not quite there. We don't have all the products yet. But for any use case of credit, eventually, you should be able to go to Upstart and just confidently know you're going to get the very best credit product. Now having said that, the 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. A conventional issuer will rate their risk of defaulting much higher. And so 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 built a great business there is much greater. And so 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 road map and that journey. So you started out as a single product company way back when, right, unsecured consumer lending. It's now a multiproduct platform that's had a great amount of success spanning auto, HELOC and now revolving credit. Now as you reflect on the journey, is that kind of how you imagine things to happen sequentially? And if not, maybe just share a little bit around the dynamics of how you decided to prioritize expansion into one product over another.

Yes. 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 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 multiproduct company. We have shown that we can deliver real growth in distribution in our auto purchase product and our HELOC product. And those products are, I think, now well past the exploration phase. They've sort of made it on their own, and 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. And if you look at the success that a lot of players have had in that market, it just was a really natural product for us to be in. Probably 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 want to be growing in them for a really long time to come. But the lift to get from 0 to 1 on those products, frankly, took longer than we would have thought. I think especially in auto, we've been humbled a little bit. I think if you asked us 3 or 4 years ago, how long it would take for us to get set up in auto, we would have just been too optimistic about the time lines. And so 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-to-consumer background as a business. We just weren't 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 of years that's enabled us to be very successful in auto, but it took work.

Maybe just while we're on that topic, 1 or 2 lessons learned that you can apply to the rest of your product road map? And then maybe just spend a little bit of time on like how do you think about conceptually, like where do you want to go next?

Yes. Just working backwards, ultimately, we want to have the very best credit product for every consumer credit need. And I think at this point, we're actually not that far away from that. We're probably just a few major products short. So there's probably not that many degrees of freedom in how we go from here to there. We're going to build all of those products. We're going to make it so that if you are an American consumer and you need credit, Upstart is the place to go. And whatever stands between here and there, we're going to build those things. And so I think there probably were more degrees of freedom in the past when we needed to build everything than now when we've built many of the things. So 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 how you want to ramp those, thinking about when you're first scaling distribution channels, having realistic expectations about getting the -- about when those channels are going to scale at what kinds of -- and 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.

And while we're on the topic, just build versus buy a framework, how do you think about that given the journey that you've been on?

Yes. I always think that the things that you want to be, best-in-class at that you want to be really differentiated at that you really are going to be your source of alpha in the market, those are things you have to build. You need to own them. And if you're going to 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. And then 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. So when people talk about, oh, everybody is going to build their own sales force or something and there's going to be no more vertical SaaS, mostly, I think maybe some company will decide to do that. But 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. But for us, all of lending is that. We want to be differentiated in auto, home, unsecured, every major category of consumer credit, we want to have the very best product in. So all of those are going to be built for us.

Okay. Maybe just shifting gears to financial profile. So 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? And just in your new seat, just any dynamics relative to just the way that the business has operated historically?

Yes. We've always -- taking sort of a 10,000-foot 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. So 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. And 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 stand-alone basis, it's a very high-margin business, and 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 good at. We've really well understood for a long time. And we think that product can just keep growing. Now 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. But 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 on to 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. And this product naturally, as it grows, it's very nice because it both comes with growth and it comes with profitability. And so it moves things on both fronts. And so as that happens, I think naturally, you're going to see that sort of impact 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. And 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. So 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 -- we always think about growth as -- in a mature product for us, the most important source of growth is going to be higher conversion through better technology. And that will always be the sort of #1 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 mandated to do exactly that. So we will keep on doing that. Then 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. So if you think about something like the home loans market in 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. So you have to understand the car dealership as a customer. And so that's a distinct channel that you have to come to learn. So new products have some of their own dynamics in terms of mastering channels that has sort of a 0 to 1 dynamic and 0 to 1 motion associated with it. But 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 going to open it up to the audience. I have a few more topics that I'd love to cover. You reported Q1 recently, strong results. How do you bridge kind of Q2 through Q4 of the business relative to how Q1 performed? And is there anything that you think that the Street is missing?

Yes. So our Q1 results, I think the real sort of fundamentals of them, 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, it's very profitable. And then we have these new segments in home and auto that have enormous relative to personal loans, almost like unlimited sized TAMs that you can just grow in and compound in for many, many years to come. And 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. We didn't have very high EBITDA margin. But 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. And that is going to be pretty heavily backloaded in the year. And 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. But basically, we expect that fixed expense growth is sort of like disproportionately growing at the very beginning of the year, basically will moderate in terms of its growth rate from here. It's not like it's going to like a big step down or anything, so nothing terribly unnatural, but just like the relative growth rate was very high in Q1 and then just going to be very modest after that. And then there's going to be -- then there's basically back to like the actual business and what's growing in it, we have a real focus now on growing in the core personal loan segment, and this is a segment that has much higher margins. And so as that increases its sort of percentage of all the growth that's happening throughout the year compared to the last few quarters, that naturally is going to have an effect that pulls margins up. And that's because it's a mix type dynamic, it's not something that happens overnight. It is an effect that happens gradually. But you'll see it in the contribution margin line, you'll see it in the sort of EBITDA line. And you'll see it sort of up and down, but it will happen gradually as that sort of mix of -- 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 want to hit, but maybe I'll just open it up to see if anyone in the room has questions. So maybe if not, let's talk bank charter. So there's multiple companies, including yourself that have 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?

Yes. 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. And 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. So 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. However, 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 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 the structure that because you have other partners that are taking pieces of revenue along the way before it gets to us. And so there's just more efficiency there. And then finally, I think in the most fundamental sense, today, we have a lot of costs and process associated with maintaining regulatory relationships via a large number of intermediaries. And I think as AI becomes a more central topic, it just is natural that as the AI lending company that we establish a first-hand direct relationship with the regulator to represent how AI is going to help the American consumer, and 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 want to 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. And the thing about what we do is that it's a very different kind of AI than, say, when you're looking at LLM models or foundation models that are now very popularly used in a bunch of applications. Broadly, I think the problem space divides into problems that AI is better at people and problems that AI sort of started out being worse than humans. Like are humans good at this problem? Are they bad at this problem? And 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 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. And that is sort of like a giant math problem. And it's actually a problem that humans have been terrible at forever. It was never a problem you want to hand even to a really smart person to decide how to underwrite a particular applicant for credit. And so it always required a very different kind of model than what you use, say, in Quad or ChatGPT or something like that. 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. And so we spent the better part of the last 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. And then there's a classic problem that humans are actually pretty good at and are more like multimodal sort of general intelligence problems. And some of those problems, I think, are now newly solvable because of what's changed and advanced in AI. So 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 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 lean on. Those are problems that multimodal sort of foundation models are very good at dealing with and are suddenly possible now. And I think that there's something really powerful about the idea of bringing these 2 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 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.

And always on credit, how far along on that journey are you right now?

We're early. We're early. So 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 they're meeting their commitments to us, our product is going to be there for them. And 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 in the consumer's financial life. Those are the other pieces of it that are getting tied together. And then, of course, the last step will be bringing the really big kind of secured credit products into that ecosystem. And then 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 and decide that now is the time to refinance particular debt or other.

We're coming up on time. Just one last question for 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? And we try to tell them there's not just one. There's a bunch of variables. And then nowadays, I think it's reflected in the perspective that I hear a lot, which basically boils down to 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 2. And then I guess from here, you'll just cash flow. And it doesn't look like you have that much cash flow. So it must not be that good of a mousetrap. And 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 we're just a rounding error in the size of it. And so by every -- like every measure, this is a business that I believe can compound at an extremely high rate for many years to come. And so it's just going to 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. So 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 shortsighted in how long they thought this could go.

This is great. Thanks for being here, Paul. And congrats again on the new role.

Great. Thank you.