All right. Good morning, everyone. Thank you for being here. My name is Matt Kost, Morgan Stanley U.S. Internet team. Very happy this morning to be joined by Adam Ferrogi, Matt Stumpf, the CEO and CFO of AppLove. And thank you for being here, guys.

Thanks for having us.

On the Morgan Stanley disclosure size, please note that all important disclosures, including personal holdings disclosures and MS disclosures, appear on the MS public website at morganstanley.com slash research disclosures or at the registration desk. All right. And with that out of the way. Matt, maybe let's start with you. I want to revisit the 20 to 30% growth target that you've talked a lot about over the past couple of years for gaming ads. You reiterated again at the fourth quarter, and obviously you've exceeded it quite a bit over that whole time period. Has your thinking on that benchmark changed at all in terms of the baseline level of growth for the gaming ads business? And is there a potential upside to that number as we

look out? Yeah, I mean, there's definitely potential upside. Look, when we initially mentioned investors in the public markets around the 20 to 30 percent, we just wanted to frame for investors, you know, that there's a lot of opportunity there for continued growth with the core technology on the mobile gaming side of the business. We broke it down for investors very simply that between directed model enhancements and recursive learning that's happening on an ongoing basis, we should be able to get at least that 20 to 30 percent. At the time, I think when we initially set it a couple years ago, we weren't even getting credit for 20%. I think people didn't think we could grow at all. So it was just a baseline that we wanted to kind of level set with investors. And look, over that period of time, over the past couple years, to your point, we've grown at a pace that's much greater than that. What investors need to understand is that this technology is very nascent. So our engineers are continuing to come up with directed model enhancements to make the technology better. And as we grow and scale, what that means for the technology is that we're getting more data to feed into it, which then improves the technology over time and continues to stack and compound. And as we expand out into things like e-commerce, that'll continue to grow the technology's ability to scale and drive spend at the return goals of our advertisers. Got it. So you mentioned e-commerce and web. Maybe

i'll go to to you adam on that one so on the last earnings call you talked about expecting a faster ramp for web advertisers compared to gaming i guess let's let's talk about what the drivers of that faster ramp are the scale of the opportunity and how that business is progressing as you move closer to the ga launch in the first half this year yeah i felt like with this question it'd be

good to just talk through how do we do so well in building products in the markets that we operate in because there's always a question with our business of how do you out compete everyone in this niche that you've become so excellent in. And over 13 years, we really understood with game developers what they needed from a marketing platform. The game development market is much easier to tackle, at least for us it was, than the e-commerce or these incremental markets because it was more niche, the developer mix was smaller, and we were able to tackle it over a long time, really understanding that their biggest issue was how do you spend a dollar and ensure you make more. These were not VC-backed companies. They had to know that they were going to get a profit on the marketing. In the absence of that, our business never would have scaled. So over the 13 years, we became excellent at building a recommendation system model to automate that problem for them. And we do it today at larger scale than anyone else in the world. We became the number one destination for game developers. That required us to know our market. Now, as we've gone outside of gaming this is a new market for us we got into e-commerce we talked to clients we try to understand the complexities of the market it's a much more complex market because you've got two parts to maybe three parts to e-commerce businesses they try to find new customers on discovery platforms meta is a fantastic example of this we are trying to be another very large example of this but they also mix in search advertising soon there'll be large language model advertising, typically called bottom of funnel. At the end of a conversion funnel, you run an ad, close the gap to conversion. And then they've also got CRM. They get a customer, and they can convert their own customer. So when you've got a more complex mix like that, you've got to know what you're building towards. And when we got into the market, we rolled out one type of targeting in our system. We did this a year and a half ago. We took our expertise in building a recommendation system, and we were able to use the billion-plus daily active users we had and games to convert them to new e-commerce brands that came onto the platform and ramped it really quickly. Now, what we didn't talk about was what that first product was. We call it a universal campaign. But that product is a mixture of discovery for these companies and retargeting. Now, when you talk to the customers, they all want incremental value first, retargeting second. We started with the first type of product offering, but over the last year and a half, we built out more. A few months ago, and I think it was late October, we rolled out new customer campaigns. What this targeting did, all model-based, is have a model be able to find them new customers that never bought on their site before, but might have visited their site. So if you think about what our offering is, it's a full funnel offering. Have customers discover the product, but price it all the way to the point of transaction for them. We started with something that was further down funnel. We moved to the middle of the funnel. Now, last week, we rolled out new visitor campaigns. This is super important because now we're able to drive a customer to their site that they've never seen before. That's the most powerful form of advertising for anyone. No one can debate how incremental something is if they find a customer they'd never seen on their website before. We're able to do it really effectively, and in pilot on that product, we got great results. We rolled it out a week ago with a product blog, and adoption's been really swift. Now, the reason I cover all this is we think about building product over time, and it's an iterative process. It requires us to really understand the clients that we have on the other side, deliver products for them that give them huge value from the marketing, profitable marketing that's measurable. If we can do that, we're able to scale our product out. We take our time developing the products. We're not rushed. As I think investors have seen, we really operate as a private company in the public markets when it comes to product development and product roadmaps because we're building for 5, 10 years from now. Now, when I talk long term, it does not mean I'm not excited about the short term. In the short term, we're seeing fantastic metrics in an addressable market that might be 5 to 10 times the size of the gaming market. As we go and get to a point where we open up our platform, it's not lost on us that now we're going to be able to make a much bigger impact on the overall economy and on our business and for a much bigger set of advertisers, something that gets us really excited about. But I do want to remind investors that my role here is to build the biggest company possible 10 years from now, and I direct my team to think that way. We try to really understand our clients. We try to build great technology for the market that we operate in. we have one of the world's most powerful recommendation models we've got amazing data inside that model we're able to at very large scale number one in the market be able to provide value that's immense for the game developers we're now starting to really ramp outside and we think that's only going to accelerate as we get further got it so maybe

building off of that you're talking about the long term i think when i talk to investors about your web business i get a lot of questions about the short term so people are asking about the number of customers being added, about sequential growth, dollar revenue. And I think you pushed back a little bit on the earnings call in particular in that way of thinking about the business. So I want to ask you, what should the market be focusing on? How should they be defining success? And what are the milestones they should be looking at for this business? Yeah, it's funny

because I push back on short term for the reason I just gave, because we think long term. But more importantly, the platform itself, the goal we have is to improve how well we monetize the impressions that we serve. We're serving over a billion users a day. It's a big audience. The ads are very much attention-grabbing ads. Our ads, on average, are watched over 30 seconds. Roughly half the ads we serve, the user's actually opting in to watch an ad. That's called a rewarded video. Nowhere in the world are you going to find an ad that someone's asking to watch, except on our platform at the scale that we operate. Now, what that means is we've got this opportunity to really expand the business if we execute as we go forward into these new categories. And let me break that down for you. We today have a 1.3% conversion rate on our ads that we serve. So said differently, we make money on 1.3% of the ads we serve. We lose money on 98.7% of the ads we serve. Now when the model knows a user is really primed for gaming, they're likely to churn the current game, they're likely to go to another game, we have well over a 5% conversion rate. This is what I call a high-value moment. Most of the impressions that the model serves for gaming are not high-value moments. The reason they're not is that the user's playing a game that they like, and if you serve them 100 gaming ads in a row, at some point that becomes annoying. Well, a very powerful recommendation system is meant to personalize content. But in the absence of content diversity, it has to stick to what it knows, which in our case originally was just gaming. now it's gaming plus so we've got the e-commerce brands and we've got some other lead gen brands but not a lot of them yet not a lot of density in every single category now fast forward five years let's say we have hundreds of thousands of customers which i fully believe we'll get to what's going to happen in our platform well this powerful technology which inevitably is going to get much better over time is going to be able to serve a different product in every single ad impression it's going to be able to take the thousand impressions that it serves on a unit And when it believes that gaming is a good ad, nothing else will beat gaming in that moment. And if the conversion rate of that is over 5%, you can think of some of the impressions that'll be stuck on gaming, that'll drive a lot of value for the gaming customers, so they won't face cannibalization. But on the rest of the impressions, the model will get more precise, more personalized, and drive a higher conversion rate. Our effective yield will go up materially because of it. And we fully expect if we're able to bring density to the auction, we'll get over a 5% conversion rate. Now, we're starting at 1.3%, so just apply a multiplier on that. That doesn't necessarily mean that in our business, that's a 4x to the business, because you also then have to understand the dynamics of how we operate. We live in an auction where we're paying out some portion, majority of the revenue that we generate goes out to publishers, and I should say majority of the spend that we generate It goes out to publishers, so we pay for their ad space, and then we have spend on top of that, and the spread is what we report as a public company as revenue. Now, if we 4X that, and as an example, last year we gave $11 billion run rate in our business in Q1 last year of advertiser spend. We're materially bigger than that, as you all know we've grown. And to conceptualize just how big, bigger than the sum of everything that's happening on snap pinterest twitter reddit combined falls into the advertiser spend on our platform now if you take that number and remember we're 1.3 conversion rate there what happens when you get over five percent it becomes one of the largest advertising platforms around we'd be able to generate tens of billions more of ad spend on our platform than we do today the expansion opportunity both to the economy gdp job production everything from that is huge gets us very very excited but the other thing that happens there is as our conversion rate goes up we're not a fixed share to publishers we end up in an auction price dynamic possibly paying the same amount out to publishers in which case you'd say the business would grow quadruple possibly being able to take even a larger spread because we're accelerating so much ahead of peers in the marketplace which you'd then say the business is going to more than quadruple that's the growth opportunity in front of us. It requires us to get customers in, and it requires us to improve our technology. We're super excited we can do all of that over the coming years. Let's talk, Matt,

about the investments necessary to improve the platform and the technology, obviously critical to the vision that Adam just laid out. What does that mean for the company in terms of investment, and particularly headcount growth? I mean, I think you're notable as a company that's been very prudent and aggressive in managing headcount through a tremendous period of growth. So how

you thinking about that going forward yeah i mean when you when you think about investment really the kind of core components of our cost structure that are important to understand are our data center costs so we've mentioned previously that what we've seen over the long over the long term is that those costs have grown at about 10 of the overall revenue growth really in fact we're beating that now um and that is just really the the efficient way that we run very disciplined as our team is launching new models, like Adam mentioned before, the prospecting model, or making improvements, they're looking at the cost impact of that and making sure that those model changes that they're making are profitable in real time. Because we run so lean, they're able to do that and really very closely monitor the cost impact of the changes that they're making. So we don't think that that should change going forward. Then the other component you mentioned at headcount, right? So, um, you know, I, I think we, we have in total around 900 employees of the company, but really when you, when you drill into what that is about 400 only, uh, of the 900 are associated with the core ad tech business and all of our back office. So we already run extremely lean. We've got a business development team for the core mobile gaming side of the business. That's like a hundred people. I think we have something around 15 people for e-com. So very, very, very, very small team. So as we think about investment to grow, to support some of these initiatives, like the web-based advertising e-com, we'll definitely add head count, but we're talking tens of people. So there really isn't any impact on the overall cost structure. The last category that we've looked at is, and we mentioned, I think on the earnings call as well, is performance marketing. So now that we've got the product in a place that's very good. You know, we're adding advertisers. We want to accelerate that. So we've started focusing on performance marketing to bring in new advertisers. And that is just as you would think. I mean, in our business model as well, it's running campaigns to drive users into the platform, but we're doing that similarly in a very disciplined manner. So we don't think that any of that should change the cost profile going forward either. I do want to add a point too here

for having a CFO that watches every penny we spend. Matt loves that our EBITDA margins are where they are but you could take what i just said and you hear massive revenue opportunity massive growth opportunity sitting right in front of this company and he's saying go get customers well you could then follow that up with what are they nuts why aren't they hiring a bunch of business development people ramping a sales force and getting out there and doing it and i get this question all the time and there's a couple reasons why we don't aggressively approach it with head count one is there's a whole bunch of platforms that ramped up sales brought in new customers and hit a wall and never grew their business. And why did they hit that wall? Because the product itself was not good enough to provably drive value for the customer where they could scale in a way that they knew was profitable. And if they have that in front of them, they don't need to be sold. The advertisers are exceptionally smart in performance marketing. They know what's going on on the other side of the dollars that they spent. We didn't scale gaming by begging for dollars. We scaled gaming by showing them that they make more money with their dollar spent on our platform than anywhere else in the world so by constraining team we're not front-running an opportunity we're forcing ourselves to build the world's best product for the customer on the other side inside our space if we can do that the scale of the business is going to come we're not rushed to go get there because we think it's going to organically come through word-of-mouth reality because our products gonna end up so good that these advertisers are just going to come into the platform and ramp on their own, we will pair that growth in product improvement, that growth from customers coming on with headcount, but never with that many. The other reason we constrain it is because we live in this world where we now have large language models to make people more efficient. A couple years ago, we went through, and with the EBITDA margins we have and the growth that we've had over the last couple years, we cut roughly 40% of the headcount. It seemed insane to people at the time. Now, why did we do that? We did it because we wanted to make sure the people that were at the company were the people that could take these tools and make themselves more effective. We've seen a 10x engineer become 100x in terms of output because of large language models that they pair with. Now, not every engineer is created equal. 1x might become 2x because if they're 1x, they're not as capable of using the tools. They're still more effective. But then you have to ask, do you want 50 1x engineers around or do you want 5 10x engineers around and so what we decided is that we want a team of really high caliber people who are going to learn how to use the tools make themselves more productive so that they're not automated away across the organization we made that the focus we leaned up to those people and that makes us really effective in the world we

live in today. Great. I guess thinking about competition, Adam, you've pretty consistently framed competition as something that can expand the overall ad opportunity rather than compress it. As web advertising becomes a bigger part of the business, how should investors think about your differentiation versus platforms like Meta or Google? And what are some misunderstandings perhaps about the way you interact with those companies? Yeah, I think when we started the

business, we were one of the bigger VC misses because we couldn't raise a million over four 13 years ago. And the constant feedback I got is competition is going to eat you alive. 13 years later, we've been competing against some very big platforms and we obviously have a great business. And people, it's easy to believe that we have disadvantages to the largest platforms, therefore we should lose. That's the easy answer. No one ever asks, why are you winning? How do you do what you do. Well, let me break that down a little bit. One is, as I talked about, we really know our customer. If you talk about inside games, it's not like we're now competing with the largest companies in e-commerce. We've been competing inside games with the largest companies out in the world for a very long time and done it well and become the leader. Inside games and inside e-commerce and every category we tackle, we want to understand our customer and we want to solve their problems in our space. Our space is a niche. It turns out this niche is pretty big. It's a billion-plus daily active users on our platform. It's still a niche. It's different than the other access points for the consumer. The ad formats are different. The experience is different. The type of user is different. This is not your high-frequency user of social media properties. The person who's playing a Mahjong or a Candy Crush is a different type of user. Fortunately, they're adults and they're great shoppers and so you've got this great audience you have this differentiated ad format you've got a different reason why they're here people who play games are doing it to relax and they're doing it to get psychological benefit in that moment that's our framework that gives us a very good framework now we went in and we built technology to execute in this space i think there's in maybe in the investor community or people that don't understand the complexities of these models, an oversimplification on the problem set that we're solving. If our job was just to place an ad on behalf of the advertiser, a single prediction, analogy would be in a large language model that just had to predict the first word. Well, that's a pretty simple thing to do. If you just place the ad, you would lose a ton of money for yourself as a platform and the advertisers that exist on your platform. What are we actually doing? Inside gaming, for instance, we predict revenue all the way out to 28 days. If you back up what that means, these are companies that probably run 20%, 30% margin. Our model has to be precise that far out into the future, predicting revenue from a customer that didn't know they wanted this game to begin with. You have to predict engagement with the ad. You have to predict that the download's going to happen. You have to predict usage patterns. You have to predict propensity to spend. You have to predict amount of spend. you can't get any of the sequence of predictions wrong to deliver the value that you're trying to deliver for the advertiser. Now, that's a really deep problem set to go solve. It took a lot of technology. There's a belief today, I think, in AI, and I don't tend to throw the two letters around too much, that large language models are the model that wins. Recommendation system models are one of the best commercial uses of deep learning models. And a lot of the same architecture and technologies apply. We've just built one of the most sophisticated ones to solve a very long list sequence of problem set. Now, when we do that and we scale our business, it gives us fantastic data inside that model that no one else has access to to make our model better. The model itself gets smarter as it looks at that data. And so not only were we able to release something that was cutting edge in our space, we were able to solve very large problems for our advertiser set both in gaming and now moving into e-commerce again in this space but our model is getting smarter every single day the ads it serves are data that no one else has access to the conversion funnels it builds and sees are data that no one else has access to and that differentiation is very large when you think about modeling verticals some verticals are predicated on same data sets different machine learning techniques. That can create a ton of value, as we've seen. Our verticals predicated on different data set and differentiated machine learning techniques, and our models are exceptional

at solving the problems that we do. I know you don't like to throw AI around, but let's stick with that for one second. It's a major topic, obviously, but particularly in the past month or two for the video game industry, and I think we've seen some of those concerns spill over into the narrative about app-loving. So I wanted to ask a fairly narrow question, frankly, which is, what would it mean for app-loving if there were many more games being created by many more people using AI? And what if some of the incumbent game companies were disrupted, what would it mean for you?

Yeah, I mean, look, the count of content is not really a KPI that matters, because you can build a bunch of slop and it doesn't do anything. As we've seen internally, and I mentioned a second ago, Our 10x engineers became 100x. Our 1x engineers might become 2x. And we're mostly built around the 10x engineers. The efficiency gains are really much more magnified when you're sophisticated at what you do. So rather than think about a child or an adult who doesn't know how to code or is not a game developer vibe coding a new game and somehow thinking they have a good experience, the application is much more likely to make the current game developers, who are very, very sophisticated at what they do, get better. What does that mean? Content development inside their current games is going to get cheaper. That's going to allow them to continue to expand their LTV. As their LTV goes up, their marketing dollars go up. That's very beneficial for a platform like ours. And the consumer experience expands from that. Those same game developers are almost certainly going to be the winners when it comes to new game development as well. As that cost of content goes down, the count of high-quality games from those game developers goes up. The category's going to be able to use this technology to get to another inflection point of growth. Now, you may also have someone like my 16-year-old envision a game, go write it natural language, and create something that's cool. And it's not slot. What happens at that point? No one in the world is going to play that game unless that game gets discovered. And how does content get discovered today? The best form of discovery is natural to believe is search and large language models. That is absolutely one. But the other best form of discovery is through display advertisements that allow a consumer to take a break, look at content, and see if they want to go engage with that new content. When we've got 30 seconds plus to show new content to the consumer on our platform and we've got this powerful recommendation engine behind it, all of that new content is going to have to come to our platform to get discovered. Got it.

Let's talk about generating creative. I think it's something you've been piloting recently in terms of creating ad units or rather creating ad creative for some of your advertiser customers. So what feedback have you been getting from the people who are piloting that technology? And what performance or level of impact are you looking to achieve before rolling that out to a much broader group?

Yeah, first of all, stating the problem. The problem we've seen is that game developers extremely high-frequency traders of marketing platforms. The game developers that spend the most on our platform spend a lot, but they've got over 50,000 ads in a single campaign. The new category, e-commerce and then onward, at best, we saw 1,000 ads in a campaign at the high end of spend. Now, if you think about that differentiation, our model's built to go test ads programmatically and find expansion of conversion rate and return on ad spend and more spend if there's more diversity of ad. That is the most important variable that the marketer has at their disposal, but you've got a 50x differential. The e-commerce companies are not going to be able to spin up creative resources and production cost to go 50 times their creative output on our platform. So how do you solve that? Unfortunately, the large language models have gotten really good at image generation and video generation. It's not so good that out of the box you can solve it for a brand. If it was that simple, the brands would already have 50,000 ads. so we had to bring to market multi-agent approach on top of the large language models to go in and figure out how to create content both in static images and video that satisfies the brand's needs we rolled this out in pilot on the the static part of the ad our ads go from a video to follow up to the video that think of it as like a one page animated gif format and inside that we're already in pilot we're seeing a lot of interesting success there with the customers that are adopting it because it allows them to have a lot more velocity the video models on the way as well and people try to ask you know when's that going to come when are you going to roll it out well i talked about when we go to general release of our platform which we're still in a closed state on the platform we fully expect to be able to give tools to the customers to create ads for our platform and i i locked that data in as first half of this year so if you think about first half of this year we're now we're four months away from that um at the end some point in the next four months we will have both these types of problems solved with generative ai based creative once that happens the front end of the experience for the customer will be more personalized it's only going to get better over time but as a starting point it's going to be much better than where we are today which is a 50x handicap to the gaming developers who are super sophisticated as we see that we fully expect that these customers will adopt it they'll get a much higher conversion rate of their ad to user engaging with their product because of the diversity of content at the front end that'll drive up their spend that'll drive up their return on ad spend and should be a material

unlock for us great um maybe turning to to mediation so there's been some news recently about a new player in the mediation market i think a lot of investors have interpreted that as a competitive threat to max which is your mediation product so what attributes does max have that you expect to help but maintain its leadership and how do you think that competitive ecosystem will change going forward if at all yeah mediation is not a really well

understood um technology or concept so breaking it down a little bit the the market in mediation we we got into in, I think it was 2018 when we launched Max. And we were competing at the time with Google's mediation layer and IronSource, which is now Unity's mediation layer, and a few other ones. So this space has always been full of competition. The mediation plays two roles. One is this technology is meant to allow a publisher to serve ads to gain access to all the demand in the marketplace. We're a very large demand source for the publishers. as we all know, were maybe the largest. However, there's a lot of other ones. There's Facebook, there's Google, there's Unity, and go down the list, 15 to 20 to maybe 100. And they get really small at the end, but at the head, there's a lot of diversity there. The tool has to give a completely fair, unbiased auction approach to the publisher to get the best ad from the highest-paying network on every instance to be useful. we built max completely unbiased completely transparent on data to the partners that we have fully audited solution and when we brought it to market we were 10 years later than ad mob solution for publishers we were maybe eight to nine years later than iron sources solution from publishers we were also competing with mopub at the time max went from zero to probably a third of the market in two to three years this was before we had our demand side platform strength so if you just then go grade the technology that we built. We then bought Mopub, took over more of the supply in the space, threw Mopub's technology out, replaced it with ours. Publishers came over. They had every chance to go to any other platform back then. They all came to ours. The reason was is because the technology gave them the most yield. Now then fast forward to today, what's happened since then? We now not only have the highest monetizing and most dense auction inside the max auction, and we paired it with the best buying tools. The vast majority of every publisher's spend comes on our platform. Now, this isn't the advertiser that is the in-app purchasing game. This is a publisher that's running ads inside their game. In order for that publisher to grow, they better be able to buy ads. We're the best destination for them. We built the best tools. It's a really big number in terms of percentage of their spend. So not only are we the best monetizing and most dense and offset competition every step of the way to get to that point. We give them the best growth tools as well. Long way of saying our tools are really sticky. We've not been in a market that didn't have competition. So then if you go, okay, well, what's going to happen as we go forward? There's inevitably going to be more competition. We live in an agentic world where it's really cheap to start products. It's really cheap to build tools. Let's not forget that we're also very good at using the same tools. We don't look at product innovation that comes from competition or within us as something that's static. We're always innovating our own products as well. So if there are breakthroughs in the space and we have the best platform on both sides that's already locked in, those breakthroughs will help us make our product even better for our customers. So we look at competition as inspiration, but we know that the strengths of our platform make it completely locked in because these companies that are on the other side of it depend on us for their growth.

Got it. Matt, maybe I'll go back to you and then we'll close, Adam, with sort of a big picture question. But before we do, Matt, there was an interview last month with your chief product officer talking about some experiments the company is doing in social media. My understanding is that this is less of a key strategic priority and perhaps more of like an other bet. Do I have that right? And how should investors think about some of the other smaller projects you're working on?

Yeah. I mean, there was a lot of talk about this question because it came out, I think, in an interview. And look, we're always testing new things like this. We're just running small projects to see where there might be opportunity for us in the future. For us, it's less of something that's core, so your characterization is correct. It's really an opportunity for us to bring in new talent that's differentiated, so something that's outside of our core kind of talent pool, bringing in new ideas. And for us, I mean, obviously we run very lean. So none of these types of other bets or bets that we're running from an R&D perspective really will materially change the cost profile of the business. We're going to keep them very, very small and tight. But we think about it more as an opportunity to bring in new talent that we can then cross pollinate ideas to the other components of the business. Got it. Great. So Adam, maybe to close.

so obviously a lot of ai talk today i guess what would you highlight as the most underappreciated opportunity available to app love in the conversations you're having with investors and then maybe uh a challenge that you think is worth pointing out as something that you're

going to execute through yeah look we're we're in we're building models in a recommendation system the structure varies similarly to the path of the large language models and i think everyone expects the technology to be static for whatever reason. We're not out there boasting marketing terms like AGI, but recommendation systems are going to evolve on the same trajectory as the large language models. Not only are we developing with similar architecture, we're developing paired with the large language models to accelerate rate of development. If we believe that AI technologies are going to be two times more efficacious in five years, just based off of that if we do our job right our system is going to be two times more predictive for its task the sequence of problems that it's predicting in five years that would double our business or more and so i think that's not particularly well understood people really latch on to large language models obviously because they can interact with them and less so latch on to the the strength of what these recommendation systems are going to be able to do as they evolve the challenge we have is also tied to the opportunity. Paired with the technology, as we go get more customers, as we open up our platform, we're going to really be able to expand this business as we talked about. Every new customer is more data and every new customer is more demand. Our data mode's already growing with the technology and the ads we serve, and the more customers we get and the quicker we do that, that data mode will even expand more. And so our job is to do that. The challenge is, we're clearly not very good at marketing ourselves. I named the company App Lovin'. That's a handicap. Nobody knows about the business. and we've got to put ourselves out there. We've got one of the best solutions for companies in the world to market themselves. They've got to find out about it. It's our job to make sure that happens. We do that right. Our customers grow to over 100,000 and I think it'll be in the many hundreds of thousands over the next five to 10 years or more and we improve our technology at the rate or ahead of the rate of improvement in where these AI technologies are going to go. This is going to be a much, much bigger business in the future.

Adam, Matt, thank you for being here.

Thanks for having us. Thank you.