How AppLovin Built a $100B Ad Machine

Adam Foroughi, Co-Founder & CEO of AppLovin, and Giovanni Ge, CTO of AppLovin, join Sourcery from AppLovin’s Palo Alto headquarters for a deep dive into the technology and strategy behind its advertising business. Gio joined AppLovin in late 2022, when the company was worth roughly $5.5 billion, and helped architect Axon 2, the recommendation engine that became central to AppLovin’s transformation. The new architecture improved predictions and advertiser returns, helping drive the company’s expansion beyond mobile gaming into e-commerce ads and its recovery from a 92% post-IPO decline to a $100B+ company. Adam and Gio break down how Axon works, how AppLovin scales with a remarkably lean engineering team, how AI is changing the way they build, why knowing what not to build matters, and what it would take for AppLovin to eventually become a trillion-dollar company. We also cover AppLovin’s expansion into e-commerce, the future of advertising across LLMs and connected TV, engineering culture, and how they think about building for the next five to ten years. Adam Foroughi: https://www.linkedin.com/in/adamforoughi/ Giovanni Ge: https://www.linkedin.com/in/xiaochuan-giovanni-ge/ Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊 YouTube: https://youtu.be/IZHwulZQixQ 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex https://brex.com/sourcery—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • Turing https://turing.com/sourcery—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance—and enables enterprises to turn those models into powerful, production-ready systems. https://turing.com/sourcery • VCX https://fundrise.com/vcx—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at http://GetVCX.com http://getvcx.com • Deel https://www.deel.com/sourcery—Deel is the global people platform that helps startups hire, manage, pay, and equip anyone, anywhere. Trusted by more than 35,000 fast-growing companies, Deel is the people platform that just works, so teams can scale without the chaos. Visit: https://www.deel.com/sourcery • Public http://public.com/sourcery–Investing platform Public just launched Generated Assets https://public.com/generated-asset, which lets you turn any idea into an investable index with AI. With Generated Assets, you can build, backtest, refine, and invest in any thesis with AI. Gone are the days of one-size-fits-all ETFs. https://public.com/sourcery Follow Sourcery for the latest updates! https://www.sourcery.vc https://www.sourcery.vc/ Disclosure Paid Endorsement. Brokerage services by Open to the Public Investing Inc, member FINRA & SIPC. Advisory services by Public Advisors LLC, SEC-registered adviser. Crypto trading provided by Zero Hash LLC, licensed by the NYSDFS. Generated Assets is an interactive analysis tool by Public Advisors. Output is for informational purposes only and is not an investment recommendation or advice. See disclosures at public.com/disclosures/ga. Matched funds must remain in your account for at least 5 years. Match rate and other terms are subject to change at any time. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Adam Foroughi, Co-Founder & CEO and Giovanni Ge, CTO at AppLovin (00:52) Gio's first impression of AppLovin (03:38) Joining AppLovin after a 30% stock drop (09:59) How Axon 2 actually got built (20:23) Why AppLovin never needed a sales team (21:59) From gaming ads to e-commerce (24:47) From a napkin drawing to a real product (30:54) The onboarding mistake most companies make (34:37) What "AI-Native" actually means (36:37) Should new engineers still learn to code? (41:41) UGC vs. traditional ads: what's winning? (47:56) Adapting to AI without rebuilding from scratch (54:50) Gio's daily tech stack (1:02:17) AppLovin's path to a trillion-dollar valuation (1:06:19) Recovering from a 90% stock drop (1:09:47) What's next for AppLovin? (1:13:44) What happens when AI takes over? (1:15:48) Adam and Gio's hottest takes (1:19:50) How to actually stay locked in

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  When we first started, it was I want to become a billion dollar company. When we got to a billion, it was okay, let's become a 10 billion dollar company. When we went public, we wanted to become a 100 billion dollar company. We dropped 92% in the first 18 months of being public. That dream seemed very, very far away, but post Axon 2, we recovered and now we're a 100 billion dollar company.

• 0:18Speaker 01

  Once we're able to make prediction more accurate, advertisers see better returns and our business grow. I don't want our engineer to sit next to AI. I want our engineers to sit on top of AI. Our team was elevated by AI. So we were able to manage the same size of a team while we're solving more challenging problems. I would attribute the success of Axon largely to what we decide now to do, not actually what we did.

• 0:40Molly O'Shea?

  How we could become a trillion dollar company?

• 0:52Molly O'Shea?

  Adam and Gio, welcome to Sorcery.

• 0:54Speaker 00

  Thanks for having us.

• 0:55Speaker 01

  Thank you for having us.

• 0:56Molly O'Shea?

  Well, thank you for having me at AppLovin's HQ. We're out here in Palo Alto. This is Palo Alto, right?

• 1:02Speaker 00

  Yeah.

• 1:02Molly O'Shea?

  Okay, cool. So Gio, you're the CTO. Adam, you're the CEO.

• 1:10Molly O'Shea?

  But I wanted to do something a little bit different. I'd listened to a bunch of the podcasts you had before and it's very apparent you guys have an amazing underdog story. There was one that I caught and it was towards the end of the interview. When you started to reveal the guts and the machine, the engine of what started to propel AppLovin to become one of the most efficient, well-performing companies out there. You guys have great margins, you're very profitable, all this awesome stuff. But it started when someone started to ask you hard questions. And Gio, you were that person. So you joined the company in November 2022. What was it like when you first joined AppLovin? Like, what were your first impressions?

• 1:54Speaker 01

  So my first feeling when I joined AppLovin was definitely a mixture of excitement and the sense of responsibility. So in the beginning, first of all, it was like a breeze of fresh air. So I came from big tech companies. The process there was much heavier. So I joined AppLovin and I saw the team here is very lean. Both Adam and CTO Basil at that time, they were deeply involved into the day-to-day decisions and executions. And it was easy to work directly with them. They're super approachable. So that feeling was refreshing. But on the other side, soon and I realized, you know, in the company, the company was small.

• 2:36Speaker 01

  A lot of things, the models, the infrastructure, people's mindset, the way they handle iterations had a lot of room to grow. And more importantly, I feel the biggest problem at that time I observed was the junior people in this company was not elevated. They were not given an opportunity to understand the business. So they were given a lot of low-level tasks without the ability to have their own ideas and raise questions. But the good thing is that time I didn't, you know, take this thing with negativity. I wasn't frustrated, I wasn't disappointed. I saw it as opportunities and that's why I joined AppLovin.

• 3:19Speaker 01

  And it was also, I was glad to see that the whole team at that time has very open, open mind. And when I brought a lot of feedback to the team and new ideas and people were very willing to work together. So we took that as an opportunity and started to improve, like make changes and improve.

• 3:38Speaker 00

  So going back to Gio's hiring and the interview process, because the old CTO and myself both interviewed him. We were, at that point, somewhere around a five and a half billion dollar company when we started talking to Gio. And we had a model that powered our advertising business, Axon One model. And it was written on outdated machine learning techniques. And so we were looking for a researcher to come in and help us architect the Axon Two model, which we released, I don't know, probably five, six months after Gio joined and he was the architect behind it. But, so we liked Gio a lot in the interview and both of us gave him the hard pitch on joining. And then, so he was excited to join and I go do my, this was November 22 earnings call.

• 4:22Speaker 00

  And he's supposed to join, I think it was like a couple of days later. Stock went down 30% that day. And I'm like, damn, we might not get Gio in. I don't know why he joined us, but he's one of the weird people who say, "Oh, it went down 30%, so I'm going to get more equity. Now I have more upside than this thing, I should even join quicker." And so, I don't know, we were lucky that he ended up joining us at that moment.

• 4:44Molly O'Shea?

  It's smarts, arbitrage, you know you're going to make it go up.

• 4:48Speaker 01

  I wasn't thinking that way, but you know, I was in big tech. If I just wanted to stay in the, on the winning team, I would have not chosen to leave. I wanted to find a place where, you know, I see opportunities and I can actually make a difference.

• 5:01Molly O'Shea?

  So what were some of the big questions that you started to ask?

• 5:05Speaker 01

  I mean, I guess I didn't start asking questions. I know, well, what my position is when I joined, right? I saw those gaps and there were not a great model at the time. But before training the model, you need to have the right infrastructure for it. And so my first month here, I actually just started writing code. It was very easy. I was working directly with Basil. We did not have a lot of meetings, which by the way, in the beginning, I was not very used to it. I asked Basil to set up weekly one-on-one meeting with me and he didn't understand what that was because it's not a thing here in AppLovin. But soon we canceled that meeting because we communicated with each other, mostly it was code. And that was much more efficient.

• 5:49Speaker 00

  Yeah, from, I guess, taking it back to like when you start a business, teams are always small and you try to build something that has product-market fit and scale up a company. And so when we started, I mean, the company was probably 10 people at the beginning and then maybe for the first three, four years, max 50 people. So we always ran really lean. And in an advertising company, if you can't make money, then you probably shouldn't be in business. It doesn't make any sense because you're trying to deliver value to advertisers. You get paid for the value you deliver. And in theory, that should generate you something more than what your costs are. And so we were profitable really, really early. We had difficulty raising too, so we had to be profitable really early.

• 6:32Speaker 00

  But the leanness of the culture came from the beginning. Now, the types of people that we hired back then, Basil, the CTO, was the first lead engineer and then promoted the CTO four or five years later. But his mindset was, I'm just going to do. And he would just write code to solve every single problem. And up until when Gio joined, Basil probably wrote, I don't know, 60% of the code of the company like himself. And the mentality was always individual contributors can help us succeed in this very, very competitive field. And so we never built this framework of like one-on-ones and meetings and process. And so I think that was what appealed to Gio when he came in.

• 7:14Speaker 00

  One of the challenges with that, that style though, is that when you don't have like a team constructed to actually like go through challenges and solve problems as a group. And he can talk about like as we've gone over the last three years in his tenure, he's really developed that muscle. But my own view, just being an individual contributor myself, was always hire a bunch of great people, let them solve problems. If they need help, they can come to us and we'll give them help. But otherwise, it's sort of like just off to the races and that was what Gio stepped into.

• 7:47Molly O'Shea?

  How big was the engineering team when you joined?

• 7:50Speaker 01

  It's probably same size as now.

• 7:52Speaker 00

  100 people?

• 7:53Speaker 01

  100 people, right?

• 7:54Speaker 00

  Yeah.

• 7:54Molly O'Shea?

  How big was the organization?

• 7:57Speaker 00

  Company, so back then, we had gaming businesses too that we've since sold off. So not including those and not including Adjust, which is a software as a service company we bought, never integrated. Let's say the company's been around a 400 people for a while. And about two to three years ago, we probably cut it down from 600 to 400. And we haven't really changed all that much since. And people come and go, but that's what you got. The other, I think, interesting thing for Gio when he joined was he came in and we created the Slack room and it was Basil, Gio, myself. And we talked through the problems and it was like just real-time feedback.

• 8:37Speaker 00

  And then, I don't know if him and Basil even saw a sun for those five, six months because they were just coding and racing against each other. But the dynamic was like, okay, well, this is a new path. So we're effectively rebuilding the engine of the company. We don't really care about what exists. Everything else that has been built, the data platform, the software tools for developers called mediation service, all these things that we had as assets were separate. But this core engine, this algorithm, they were completely rebuilding. So they basically went in to, I don't know where they worked out of. I know where Basil worked out of, I don't know where Gio worked out of. But they just went in and just started coding against each other and racing to like

• 9:20Speaker 00

  get to a direction that they thought was, was the right place to go. And it was cool for me to see, I think it was different for Gio to see because he was used to much bigger processes around what engineers couldn't, couldn't do at companies. And one of the first things I told Basil when we first started developing this company was technology moves really fast and you got to be humble about what you have and know that if technology goes faster than what you've developed, you got to throw away what you have and re-architect it, rebuild it. And it's probably going to happen every couple of years or even faster in the future. And that was always one of the core things that we believed on engineering is who cares about what we've built? Let's always make sure we're on something that's current or innovative or cutting edge.

• 9:59Molly O'Shea?

  So you have a lean team, it's about 100 people on the engineering side. Everybody's quote-unquote an individual contributor. What was the process like for architecting Axon Two?

• 10:12Speaker 01

  So when Axon Two was being architected, the team at that time was very different to today, right? So I'm going to explain what happened that time. When I talk interviews, a lot of time, people are very curious about how I, what I did to change AppLovin'. But very rarely, people ask how AppLovin changed me. Actually, when I joined AppLovin, that was a change for me as well, because the moment I saw how Adam and Basil worked, you know, it just like gave me a new definition of what does it mean to be hands-on? I used to be hands-on. I loved writing code, but joining AppLovin just saw a totally different level.

• 10:52Speaker 01

  So initially, when I joined AppLovin, I actually planned to join two months later than my original date. And Basil and Adam were trying to convince me to join first, and I said, I already have a planned a trip to Europe for a month for the Christmas. I won't have much time to work during that month anyways. How about I join after that? But Basil insisted, he said, you can just join for a week, do a week of work, and you go on your vacation. And I said, okay, fine. But that week, like fundamentally changed me, because after that week, I decided not to cancel my vacation, but to work through my vacation. And I still remember what happened that time. It was the moment that ChatGPT 3.0 just came out,

• 11:33Speaker 01

  but it was definitely not enough to write code for you at that time. So we still have to, you know, write a code in the traditional way. I was on my flight to Italy, and it's like 12 hours of flight, and I was, I had to continue to code. At that time, you know, people still know how to code without, like agent. I don't think you can, you can still do that without internet today. So I was on the flight, there was no internet. I was coding for the, coding the X on 2.08. At that time, I was building the, the training infrastructure. And at a certain point, you know, I, I need to check a document. I need to Google search some, some, some information about the tool I was using. But I was on the flight, I have no access to internet.

• 12:16Speaker 01

  And I had forced myself, luckily, you know, on my SDE, I had access to the source code of all the libraries I was using. So I had to go deep into the, into the, into the source code, try to understand how things work. That was a pretty interesting experience. At that time, I was like asking myself, like a man is given infinite amount of time, because I was literally trapped on the, on the flight, you know? The time was infinite, but I have no access to external help. But I just keep coding, and when I was in Italy during that month of vacation, I kept coding. You know, my, my in-law, she's a lovely Italian lady. She couldn't understand like why anyone has to work that hard during the vacation.

• 12:58Speaker 01

  She kept asking, asking me, hey Giovanni, are you okay? Giovanni, are you okay? Are you losing your job? I said, no, no, no, I'm just very, very passionate about what I'm doing. So surprisingly, you know, initially, I thought I would actually start working in AppLovin in 2023. But by the time I came back from that vacation, we were already ready for the, for the infrastructure part of X on 2.0. The moment I came, came back, we started training the first generation of model.

• 13:27Molly O'Shea?

  Oh, wow. So that was a big reset for you.

• 13:31Speaker 01

  Yeah, it was, it was a reset. I feel like, you know, that kind of philosophy is already there in, in me, and I definitely love that, that at the most feel. But just prior to joining AppLovin, I didn't have a chance to experience that kind of working mode. And that month just made me feel, oh, this is the place. I love to work here.

• 13:52Molly O'Shea?

  So big tech is very different.

• 13:55Speaker 01

  It was very different, yeah.

• 13:57Molly O'Shea?

  Damn. And then in terms of re-architecting the actual algorithm and building out this algorithm, because it was a huge, a major inflection point for AppLovin.

• 14:06Speaker 01

  Right.

• 14:07Molly O'Shea?

  What was that like for you?

• 14:09Speaker 01

  So, so let me, let me go a little bit deep about what X on 2.0 actually is.

• 14:15Speaker 01

  You know, the problem that a recommendation system is trying to solve is actually, you know, you have many, many, many users, and you have many, many items, and the combination of each user and each item is enormous. And the recommendation system has to study the relationship of each combination. Before, the, you know, the last generation model tried to model this by clustering those combinations into different clusters and build this tree-based model that's basically is a huge set, like hundreds of thousands of if-else branches. And then each cluster of user-item like combination seats on the end of this if-else branch.

• 14:58Speaker 01

  But obviously, as you can see, the real world is so complicated, and the relationship changes dynamically as time change, as, as the weather change, as there are like promotion events, holidays, all the things are changing. And these tree-based models are not able to accurately model this dynamic and relationship between user and items.

• 15:23Speaker 01

  The X on 2 user approach that, you know, use this concept nowadays, people are very familiar with this, thanks to the language model, is called the semantic embedding. So basically, we use the learnable embedding tables to encode those ID, like item ID, user ID, that otherwise would make no, no meaning. We encode these, these IDs into semantically meaningful embeddings, and it carries the statistical informations. And then we pass those information through deep neural network that allows us to, to, to study this complicated interactions between user and items.

• 16:04Speaker 01

  The result of this kind of model is it is much more powerful in recognizing the patterns, and it's also powerful in extrapolating this patterns to unseen data and unseen user-item pairs. And, and, and once we build this, you know, our model is able to make more accurate predictions. It was actually also cheaper, like in terms of infrastructure. So once we're able to make prediction more accurate, advertisers see better returns, and our business grow.

• 16:33Molly O'Shea?

  Why was it cheaper?

• 16:35Speaker 01

  This just has to do with, you know, some details of, of how the model is, is being influenced. The neural network, the benefit of neural network is it's, it's made up a lot of standard GEMM. Basically, it's a general matrix multiplication, which, you know, is the famous, the GPU. GPU is highly optimized for this kind of operation. But the selection tree, you know, the old generation slack trees has like, has the, the structure that is very hard to optimize on a GPU machine. So the new generation model is very, very powerful and efficient with a, with a modern GPU architecture.

• 17:12Molly O'Shea?

  So you're not token maxing?

• 17:14Speaker 01

  We're not. Oh, the, we, so token maxing is a concept in, in large language model. It's a, it's a different, different term.

• 17:23Speaker 00

  I token max every time I hear Gio talk, I have to put it in the L. I'm translate what Gio said for me.

• 17:29Molly O'Shea?

  So Adam, can you translate what you just said?

• 17:31Speaker 00

  I'm not smart enough, unfortunately.

• 17:33Molly O'Shea?

  But I'm really curious. Okay, so as you're talking about deeper infrastructure and the deep learning that occurs within the model and the tree and that kind of thing, okay, you pull out, what are the main categories of data that you're trying to build patterns off of?

• 17:47Speaker 01

  Basically, you know, the type of data, the most important data is the user interaction with the ads. Because every ad we shoot with user is an important feedback data point for us. User either react to it or not react to it. That information, if you collect those information, that builds a very powerful prediction, you know, machine for the user next section.

• 18:09Speaker 00

  It's also important to understand our ads in, in this context. We're not like just a static native or banner ad. The ads inside games are much more similar to what television is, but specifically for games, you have somewhere around 60 seconds that people sit on an ad, and they're not just sitting there doing nothing. They're playing mini-games that we serve them. So they're engaging in something that is representative of what they can go download, and there's tons of interaction data that comes from that. So if you think about average time spent on most websites that are really successful, user spends, call it 40 minutes a day on a really successful social property. If a user sees 10 ads on our platform a day, that's 10 minutes of engagement with the website effectively.

• 18:53Speaker 00

  That's like a mini-game serving website. Tons of data is able to be extracted out of those engagements.

• 18:58Molly O'Shea?

  What is more data that you have behind those ads? Because you've served to effectively like a billion people, I see this on your website, and they sit there for 60 seconds and they get served this ad. So what is the typical click through? Like, how do you determine that?

• 19:12Speaker 00

  Well, you have the engagement. So you have how they're playing, what they're engaging with. Then you, you follow them through conversion funnel, right? So we serve the ad, user engages with the ad, user goes to install or doesn't install. Sometimes people will just say, I don't want to engage with this ad at all. I'm not going to, I'm not going to click anything on my device other than skip the ad. And so, you know, that person just really doesn't like that ad. And on the other side of the extreme, someone might follow the ad all the way through the App Store, go download an app. So now you know, okay, this is pretty warm. They're interested in it. But just a download for us means nothing. We're trying to deliver to the advertiser engagement and revenue. So to get really hot, the user then has to engage very regularly and has to go generate revenue, whether ad revenue or, you know, purchasing revenue for the customer.

• 19:55Speaker 00

  The benefit of, of doing what we do is that we, we can serve the beginning of the conversion funnel and then close the loop all the way at the end, and the advertiser gives us data to help close that loop, and then GeoSystem can then take all of this data, which across our businesses a ton, and put it all into this model to then determine, okay, now I've got one thing that this person's really hot on. What other users are like this person? What should I start predicting for that person?

• 20:22Molly O'Shea?

  What I thought was interesting, learning more about AppLovin, is the business model is predicated on performance, but it's advertising to make people more money. So it's like, can you explain? Like, was it always like that? Were you always making advertising? So someone could just build their business and like, create this reinforcement system to make, you know, put more money in it, get more money out, build their business, that kind of thing?

• 20:47Speaker 00

  Yeah, totally. It's, we want an advertiser to be able to spend money on our platform, measure it, and know that they end up getting more revenue and profit from the money that they spend. If you can give them a user and they make profit on it, then they'll scale pretty large numbers. You don't have to sell that through. And the reason why originally that was our goal as a company is that I realized I don't have an ability to sell all that well, don't have the patience to go really build up a traditionally large sales force. And this is what most advertising companies do, is they just scale out the go-to-market team when they have anything that works. And what we wanted was a system where the company on the other side just knows it works. So therefore, they want to put money in. And we're delivering so much value that they're really desperate to work with us,

• 21:32Speaker 00

  not the other way around. We're not really selling anything through, other than they're getting the user, they're getting profit, they're happy. What makes it cool to do that is that the platform ends up becoming much more catered to businesses that you almost have never heard of before. We got companies that scale on our platform that are small businesses to most, but have very large P and Ls. And they're able to do that because of a solution like ours. And that always made us feel really good. It also fit the DNA of the company well.

• 21:59Molly O'Shea?

  When did you feel comfortable enough to switch not just on gaming ads, but also extend into e-commerce?

• 22:06Speaker 01

  It was about a year after we scaled Exxon 2.0. I think, you know, at that time, we realized the model architecture we had, like, would work beyond just gaming, right? Of course, there are new things we have to build, the new business, the new pipeline, the new product flows. But we felt that the core, the core technology should be general enough to support different verticals.

• 22:34Speaker 00

  Yeah, I mean, I think when Geo built and architected Exxon 2, he knew that this was going to function for any vertical. But we always got the pushback from anyone who's covering the business, whether analysts, investors, someone scrutinizing the business of, well, you only have gaming data, so how could it work? Well, one thing that I lose sight of is that it's a billion people on the other side. Not a billion, just people who are only playing games and doing nothing else. So if you understand the person, then you can actually deliver value to them outside of just a game-to-game scenario. And you can only do that with a very sophisticated model. The other piece of that is that you're getting advertisers sharing you data. This is what powers a lot of what Facebook was able to build over the years. Is originally, and when they went public,

• 23:20Speaker 00

  there was this notion that you couldn't monetize the social network. Like, how could you monetize this blank canvas? Well, they have a lot of people, and you start understanding the behaviors of those people through what people are doing on advertiser properties. You pixel websites, you get data. And so we took the same approach, started pixeling websites, started getting data, understood the user more than just their game behavior. The user does a lot more. And the people who were playing mobile casual games turned out to be a lot of heads of households. It skews slightly female, but it is this sort of 30 to 50-year-old age group that's the heavy mobile casual gamer. And so we had this really good user. They're engaged with a really immersive advertisement. And we started collecting the data that the model could interpret to make these recommendations, and it just started working.

• 24:03Speaker 00

  But I think Geo had a sense that was very doable because he understood that the architecture isn't just looking at one vertical. It's not a verticalized model.

• 24:12Speaker 01

  Yeah, and I think another important thing of having a great engineering team, the value of having a great engineering team is a lot of times you don't have to guess these kind of things. At the time, you know, we did analysis and discussions. There are reasons to support why this is going to work. There are reasons that tell us why this might not work. So our solution was we just quickly built the prototype. And it took us like a few months and to launch the first model with some testing advertisers. The result was overwhelmingly good. And that gave us this confidence that this is going to work. And we started building.

• 24:47Molly O'Shea?

  What's your process for testing?

• 24:50Speaker 01

  For testing the e-commerce?

• 24:53Molly O'Shea?

  Yeah.

• 24:53Speaker 01

  So if, in the beginning, it was building, not really testing, right? The building story was also fascinating. I think at that time, the CTO, Bezo, and then there were some other senior leaders in the company were attending a Google conference in Vegas. And on that morning, we're having breakfast and we're discussing how do we build the first data flow engine for this new product. And we're waiting for our manual to come. And there was no paper, so we just found a napkin. And we started discussing and enjoying design on the napkin. You probably did, do you know this story?

• 25:28Speaker 00

  No, I don't know this story.

• 25:29Speaker 01

  Did I ever tell you this story? Okay, you should, you should talk more, hang out more with Bezo. And then we were having this discussion. And by the time the photo arrived, we already have a pretty good design. And then we all went back to our hotel room. Before we arrived in San Francisco, we actually had the first prototype for that data engine. And then it took us about a few months, a couple months, and to build up the data, the model, the infrastructure. And we recruited a few advertisers. I think it was the business team who found the advertisers for us. And we put them online. Initially, the model has very limited data. So the model was very premature. But even with very premature model, we're able to see people were making a purchase.

• 26:14Speaker 01

  The ROI, the ROAS, was decent. And we're like, okay, this is going to work.

• 26:19Speaker 00

  What's cool about this story, which I'd never heard before, is that one of the cultural philosophies we had is almost no product management and have engineers that are competent enough about business problems to just write their own architecture, write their own product to solve business problems that maybe not even a business team knew existed. And so here, you're not hearing about a product org that's talking to clients, that's writing a spec, that's handing engineers a spec, that's having them write to code to spec. None of that happened. You have engineers that didn't even talk to a business person sitting down together at breakfast on a napkin, writing out a solution to a problem that we'd never even discussed, and then something that could become a big business. And so I think the team still is constructed that way, where I don't know how many product managers are on Geo's org,

• 27:02Speaker 00

  but it's probably sub-handful. And the rest of it is engineers who are competent enough to know what they're going to go develop.

• 27:10Molly O'Shea?

  This episode is brought to you by Brex, my favorite. You become what you spend on. And I refuse to spend my time on work that shouldn't exist. Expense reports, receipt chasing, and manual closes. The companies building what's next, from Vercel, OpenAI, Anthropic, Granola, and Deepgram, all made the same call. They all run on Brex. Brex is the intelligent finance platform that combines cards, expenses, and banking into a single stack with agentic finance built in. AI agents that handle expenses automatically, enforce policy before spend happens, and close your books in minutes. That's why Sorcery runs on Brex, so I can spend time on building and not busy work.

• 27:55Molly O'Shea?

  It's time to get Brex AF. Learn more at brex.com/sorcery. That's B-R-E-X dot com slash S-O-U-R-C-E-R-Y. Bye. Turing is training the next generation of AI with tasks that require real expertise and real-world judgment. That's why companies like Nvidia, Anthropic, Salesforce, and Gemini partner with Turing. Turing builds realistic reinforcement learning environments and data systems based on real operational traces, the kind of infrastructure Frontier Labs need to train superintelligence. Visit turing.com/sorcery. So are you then working directly with the advertisers

• 28:40Molly O'Shea?

  on tweaking it or making sure that it's working? Not going to another team to ask how they're handling their relationship with that person, to know whether it's going well or not?

• 28:48Speaker 01

  So the communication with advertisers is still through the business team. But all the data and decisions, like, our engineers understand what a business needs is, right? Our engineers, you know, when we have to talk to advertisers, right? Sometimes you have to schedule a call, a meeting. I think that's not a great way to scale our engineering team. We leverage the business team. But engineers understand what is important for the product.

• 29:12Speaker 00

  It's also like, if you're delivering something like an audience to advertisers, it's sort of hand-wavy on the result. There's no measurement of what actually occurred. And with engineers to like, give them a path, you got to give them pretty clear metrics on, is it working or is it not working? If you're living in a revenue-based world, a return-on-ad spend-based world, there's not much of a debate with the advertisers. Either they're going to put more money in because the return on ad spend is good, or the return on ad spend is not good. If someone's selling lipstick and the lipstick costs 10 bucks, and it costs 5 bucks for them and they're going to have a $5 spread, well, they can't spend more than $5 on selling the lipstick, otherwise they're in the red. It's really easy to then put that into a formula

• 29:57Speaker 00

  and say, engineering, here's where we're at. We're either good or we're bad, and we need you to be here for this to be scaled.

• 30:03Molly O'Shea?

  Did you always have that intuition? Or was that also built up?

• 30:07Speaker 01

  I think that's definitely built up. It's definitely built up. I, before joining AppLovin, you know, in my previous job, I already carried both the engineering part and also the product part of the work. As I, as I shared in the beginning of the podcast, when I joined AppLovin, I saw that the engineers were kind of disconnected from business. So one part of my job was try to help our engineers to understand the full context. Instead of assigning tasks to them and tell them to do exactly what they have to do, I show them, you know, how their work is impacting the business. And because our, you know, engineers are very talented, once they are provided with that kind of context,

• 30:51Speaker 01

  they have a much better sense of what to build.

• 30:53Molly O'Shea?

  What do you think the biggest mistakes are that people do when they hire on new talent and try to onboard them into these teams?

• 30:59Speaker 01

  The biggest mistake?

• 31:01Molly O'Shea?

  Yeah.

• 31:02Speaker 01

  I wouldn't be able to comment in general, but I think on our like team, I think few mistakes could be, you know, first of all, not setting them the right context. I feel people usually will assume, you know, the junior people needs handholding. And therefore, they only provide very granular tasks for them. But I think this is not the great way of having them to grow. So I think that's one type of mistake. The other mistake, this is something that I recently learned, is actually exactly the opposite. I feel sometimes we, when a junior like team member does a good job,

• 31:45Speaker 01

  we tend to give them too much encouragement without providing continuous challenging to them. And then sometimes their ego will grow and then they will lose their humility and they stop questioning themselves. They'll also become a problem for their early growth.

• 32:01Molly O'Shea?

  How do you continuously challenge each other internally?

• 32:04Speaker 00

  Let me cover that, the prior question too, and then we'll get to that one in a second. But I think the big mistake I've seen is people really handhold new talent and bring them along slowly. So one of the things that's a little unique is like, whether Gio is an intern or a full-time employee, just started, maybe even right out of school, they can push code within the first week of being here. And so, one of the, end of the day, we're an advertising company. So if we went to a really high IQ graduate out of one of the top universities and said, "You're gonna build ads." It's probably not easy to go get them excited about this opportunity versus all the other opportunities they have in front of them, especially in the field of AI. And like here, they come in and intersection of math and engineering

• 32:49Speaker 00

  and they're building models or data and plumbing to support the infrastructure that we need to run the models that we have. They come in and they just get to deploy code. So they get hands-on experience. There's no process in front of them. There's a lot of controls in place to make sure that their code doesn't blow up the whole system. But there's no process in front of them where it's, "Go through all of this training and go like review this, this, and this, and talk to these 15 people who will bring you along in over six months." And then now you get to be a little bit hands-on and a year and a half later, okay, now you can push your first test. Here, within a week, they can push a test. They get, start getting results. That moment is a pretty substantial dopamine hit.

• 33:31Speaker 00

  They get something that they developed or thought to develop quickly into production, reaching a ton of users, and they get real-world feedback loop around it. And this isn't just engineering. It's on the business side too. We don't have the traditional training processes to bring people along. There's no training manual. There's no come in and like someone's gonna like tell you what to do. It's, we want people to come in and be very curious and learn for themselves. And at least my own experience in learning is like when I was told what to learn, I never retained anything. But when you're hands-on, you can retain. And now in the world of AI, it's even more powerful. If someone can come in and say, "Look, there's no training manual, but I'm gonna sort of build my own training manual and prompt AI and start asking the questions they need." And if they ever hit a roadblock in learning, they go to someone on the team.

• 34:16Speaker 00

  Not only are they able to use their own curiosity to educate themselves and bring themselves along faster than others, they're able to say, "Look, I'm willing to raise my hand and speak up in an organization I'm new in." And we found by enabling that, we're able to bring people along much faster than we otherwise could.

• 34:35Speaker 00

  Sorry for interrupting there.

• 34:36Molly O'Shea?

  No, I mean, that was a great answer. You've been really hardcore on making sure that your team is AI-active or AI-enabled and AI-first, whatever you wanna call it. How do you evaluate people on that? How do you make sure that they're continuously upgrading themselves and upgrading their skills?

• 34:54Speaker 01

  So for people, of course, I think, you know, the AI works differently in the business team comparing to the engineering team. I can speak about engineering team. So AI has been evolving very rapidly in the last few years. But there's still a boundary of what AI is good at and what AI is not yet good at. For example, even today, AI is extremely good at answering questions, writing code. Probably also is good at doing research and like help you to shape your opinions. But still, AI is not good at doing long horizon plannings. A lot of work that we have to do today requires long horizon planning. So then we still need human to be involved.

• 35:38Speaker 01

  Maybe one day, AI would be better at that. So the way I see the relationship between AI and our employee, our engineer, is I don't want our engineer to sit next to AI. I want our engineer to sit on top of AI. So as AI is improving, this boundary between AI and a human is also moving, right? Previously, AI was only useful for correcting the syntax errors when they are coding. And then AI became better. AI can like complete the whole phrase, the whole paragraph for your coding. And nowadays, AI, you know, you can just give him an idea. AI can write an entire PR. As AI is improving, human just sits on top of it and focus on what AI is not good at.

• 36:21Speaker 01

  So because of this, I think our, almost every engineer's output and productivity has been dramatically like multiplied in the last few years. And as AI is, you know, advancing, I believe the output efficiency of our engineers will just keep growing.

• 36:38Molly O'Shea?

  I asked Max Levchin this question because he's a hands-on CTO, CEO type. And he's super technical. I asked him, "Do you still think people need to learn how to code?"

• 36:51Speaker 01

  I think at this moment, yes. At this moment, yes. So, but I'm not sure that my answer will keep the same in the future, right? At this moment, I think AI still may, very often, still make bad decisions or coding decisions. If our engineers are not capable for coding themselves, they will not be able to judge and correct that mistake.

• 37:14Molly O'Shea?

  Then you'd blow up.

• 37:16Speaker 01

  Yeah. That's probably a lot of companies see it, right? We don't have that problem at this moment. Our code is still pretty clean. Internally, we also have this concept of, you know, what is the system you want to prototype? And what is the core system that you have to keep absolutely clean? Right? When people just want to set up a dashboard, want to create a temporary project for a presentation, they do whatever they want. You know, it doesn't have to be robust. It doesn't have to be maintainable. But for all the core infrastructure, the core system, this is what I told the team, right? You can use AI however you want. But in the end of the day, you as a human is held accountable

• 37:59Speaker 01

  for every decision that your AI made for you. So it is absolutely not acceptable when I come to an engineer and say, "Hey, why you implemented this system this way if the engineers tell me, oh, I don't know, AI did this." I told them that this is not acceptable.

• 38:15Speaker 00

  I think the other thing, I mean, AI allows everyone to write code, obviously, and it commoditizes a lot of that. And so as a company, you need to create alpha out of somewhere. And your smart engineers can't just go to AI and just say, like, "Give it to me out of the box." If that was the case, then everyone would have technology that'll look identical to each other. We say this in ad creative too. Like if the AI starts writing all the ad creative, then all of it's gonna eventually look the same and the user won't respond to the ad. So how do you create alpha? Well, you have to have really smart people that understand systems, understand what they're asking from the AI to output for them. And they need to create variance on that. And not just the simple prompt to get an output that anyone could deliver,

• 38:58Speaker 00

  but something that understands the product, runs their own product management, has to direct the AI a specific way to create that alpha. I think that is gonna be a skill that's required if companies wanna stay ahead of each other. Otherwise, everyone's gonna land in the same place on a lot of these functions.

• 39:13Molly O'Shea?

  Can I ask like a glaringly obvious question? I don't know if you, maybe you have gotten asked this many times. Maybe you guys do this internally. But I was listening to a podcast about this. It was an advertising agency trying to educate people on how to make the best app love in ads. And so what makes the best ad? They were talking about making 20 to 50 videos, 100 videos. I had no idea you had to make this many videos.

• 39:41Speaker 00

  So because our ads have a lot of time with the user. And half the ads user sees between a level. And so think of it as like a commercial break. They can skip quicker. But half the ads, they opt into watching. They wanna watch to get some sort of reward in the game. So let's say you played a puzzle and you lost your life. By watching the ad, you can get another life. Or you could just pay a dollar. But here, it gives you something of monetary value, so you're gonna watch the full 60 seconds. You've got a lot of time with the user. Then the second part of that is that usually the first ad isn't gonna convert the user. They're gonna see multiple ads in a conversion funnel to get to the point of transaction. So call it, if it's 10 ads to convert, you had 10 minutes with the user. Well, if you're giving them 10 minutes of content, it better be interesting. Otherwise, you're actually gonna turn them off.

• 40:25Speaker 00

  And so there's no really good formula to this. I've tested tons of ads in my life, even before I've been in advertising for 21 years now. I don't know that there's a formula to know how to create a great ad because we can't all, none of us can really predict what a consumer's gonna respond to all that well. So it requires one, a lot of shots on goal. People have to test a lot of things. If you create 30 ads a week, probably one of those might be interesting. And you don't wanna just change very small things around. You wanna create concepts that are differentiated to find, what is a user actually gonna respond well to when it comes to that specific brand? What works for that specific brand won't translate to 10 other brands also. So like each brand has to figure out, what is a user gonna respond to in our framework where they have all of this time?

• 41:10Speaker 00

  And then they gotta start creating multiples of that because they get so many shots with the user to engage them before the final conversion happens. And so there's not a great answer to this. People wanna come up with like best practices. Here's a list of 10 things to go do, simplify it. But end of the day, like this is one of those variables that can create alpha in an advertising system. The advertisers that invest in creative and are good at it, whether they're using AI tools to develop them or they're using people to develop them, can create lifts in an advertising system where everything else is automated.

• 41:41Molly O'Shea?

  Has there been any sort of, I guess...

• 41:48Molly O'Shea?

  Has there been any sort of resolve in whether UGC is taking over traditional ads? I know with like the proliferation of clipping, like it's really cool, like seeing on social media. I put out content every day and I put out content before where like there was this like prebiotic, probiotic like bar, I bought it, I ate it, I was like, oh my gosh, this is like Ozempic. And I tweeted that, they made that tweet an ad and it became like a Forex ROI ad for them. And they just like made so much money on it, I got no money. But like, but like there are like different kinds of things that have made more ads more effective than others. And this proliferation of UGC and what looks like clipping and what looks like just normal social feed has kind of risen. Have you seen anything with that?

• 42:33Speaker 00

  I mean, some advertisers can make that work on us because there's a wide range of things that work. But I'd say social is really high ADD, so everyone wants to get to the point really quickly. Typically like a social ad will say, you got three seconds to capture attention, that's it. And otherwise you lost the user. In our world where the user is sitting and engaging with the ad for such a longer extended timeframe. And the user is just frankly different, the person who's playing a Solitaire or Mahjong is not the power user scrolling on Instagram and TikTok all day long. And so you've got a different person, probably the power user is older. Their mind is not working in the same way as someone who's grown up in today's culture.

• 43:12Molly O'Shea?

  So you have smarter people.

• 43:14Speaker 00

  I wouldn't call them smarter, different, differently constructed minds.

• 43:18Molly O'Shea?

  There is alpha in this.

• 43:20Speaker 00

  They're just wired differently, but it takes, this is part of the education process. We're in gaming, we've been in the space 12 years. The game customers really brought to market playables that are the mini game preview in the ad. I want to say like 10 years ago and like the ad stayed pretty consistent since then. Cause it's just a really good ad, you get the mini game and then you get the download behind. We got into this, this consumer vertical and more specifically e-commerce 18 months ago roughly. And so you're so early in a market, you have to educate the market on what works. And that where alpha comes to play here is some of the customers who go, I'm going to invest in this platform early on.

• 44:00Speaker 00

  And really learn how the users behave, who the users are and what they're willing to buy and what kind of ads to build. Create alpha, they get, they get to create much larger campaigns at successful metrics on us. Then those who come in and just go, look, I'm going to run the same UGC ad I do on social over here. Well, we don't have three seconds, they had a lot longer to actually go engage the user. And have that user remember their brand, but they chose not to take that. And so it'll take time for us to, to make sure agencies and advertisers understand these concepts. But it does create a world where early on in a platform advertisers who are paying attention get alpha.

• 44:38Molly O'Shea?

  How has AI changed the making of ads?

• 44:41Speaker 00

  It's early, it's not like, like you can get a good clip, a short ad out of some of the large language models. The challenge is to get them to put together a 30 to 60 second video advertisement for a brand. That doesn't mess anything up because you can't like anything wrong in that video, the, the brand is not going to approve it to run. And so can't mess anything up, have to be engaging and for a pretty long extended timeframe, not that easy yet. So it's not at a place where you're just going to get a massive amount of inflow of advertisements that are diverse and creatively inspired. And, and going to create lifts in campaigns, but their tools are available now. So the humans can create ads much faster and much lower cost.

• 45:26Speaker 00

  So we've seen much more ad content coming into the system. It's just not at a point where the average marketer can go type into a box and say, here's a great ad and off we go.

• 45:36Speaker 01

  Yeah, from our product, we saw a lot of AI assistant assisted content on the platform by advertisers. I think, you know, the, it's important to still have the human in the loop to gather the process. Because advertisement is about trust. If you create a content that, that, that breaks the, the brand image and it will break the trust. And it's harder to convince users to, to, to buy. But so there are two different types in terms of AI generated as content. There are two different approaches, you can, you can, you can try to approach it. One is you make this tool entirely automatic, right? This is what if App Loving is building such a product, this is what we aim.

• 46:21Speaker 01

  Because we're not serving one single advertiser, we're serving all the advertisers. We have to create a tool that can fully automate everything. The other choice is you just create a tool that allows advertisers to interact with, to allow them to collaborate with the AI tool to create their own content. So right now we see that the second type of tools has been like pretty widely adopted. But I think there's still more challenges for the first approach and we're still working on that.

• 46:51Molly O'Shea?

  Have you guys thought about buying any of these kinds of companies? There's a company called Higgsfield, there's like, I mean, how do you scope out R and D on this kind of like new market that's opening up?

• 47:05Speaker 00

  I mean, they're, they're, it is much easier to get content that is short. So they're optimized for social and UGC, which these things, if you want to create a 10 second clip and put it on TikTok or Instagram, you can do out of the box. And Higgsfield is a tool for influencers, that's not the advertiser on the other side of, of our product. Like if, if you get a brand as big as a Wayfair and you hand them something out of the box that looks not ideal for their brand. They lose trust in you as a platform and certainly you can't run that on their behalf. So they got to get something that is compelling enough for their brand to be as good as what a human builds. And maintain brand trust and have extended time in it. So there's nothing in the market that we've seen that solves that problem.

• 47:47Speaker 00

  Generally, when we see these problems, they're hard to solve and specific more to us than just the general market. Our team has to develop too.

• 47:56Molly O'Shea?

  Okay, so we covered a lot internally to kind of take it a little bit back into macro. So a lot of people are trying to rebuild right now for the AI era. I've listened to many of Adam, your interviews where you're talking about you build for the next, you know, three, four, or five years. A long ahead of you. So whatever you built right now, you know, you built back in 2022, 2023 or so. I'm curious, does that still hold? How do you rebuild in such an era where things move so fast?

• 48:28Speaker 01

  Yeah, I, I don't really want to rebuild. You know, in the last three years, we built a lot of things that made us feel so proud of. AI is, you know, powerful, but I don't think that's the reason for us to rebuild. What the AI really helps to do is, you know, to build on top of what we have today. With the empowerment of AI, we were able to achieve much more. You know, I have been thinking about this, this, this growth trajectory, this growth journey of, of our company. As, as we shared previously, you know, the size of the engineering team was basically the same, the same size as like today as three years ago.

• 49:07Speaker 01

  And typically when a company went through such a growth of their business, you know, your, your business, scale of your business is much larger. The type of problems you're solving is also much more sophisticated. And this kind of growth has to be paired with the growth of how content and team size. But luckily, you know, I feel our growth just was perfectly in sync with the, the, the advancement of, of AI. So as the problem we are solving becomes more complicated and our team was elevated by AI. So we were able to manage the same size of a team while we're solving more challenging problems.

• 49:48Speaker 01

  As we're speaking now, you know, we built this model, the generation, new generation model three years ago. Now with the, the assistance of AI, the team was able to tackle and work on a new generation of model that would be even more powerful. So back to your question, I don't think we need to rebuild anything, but we are definitely building on top of what we have today.

• 50:09Speaker 00

  On the business side, we keep the team really lean as well. And you wouldn't be able to scale out the products we want to scale out with the team sizes that we target in the past. But if we can get to a place where eventually everyone on the business team is doing something. And then ask the question, could this be automated and done across the whole advertiser base? And they actually understood how to use AI or they go to, we have central folks in, in a team that actually can create tools, dashboards and automation for them. Can, can go take that process and then automate it. We'll get to a place a couple of years from now where hopefully most every process is automated. And then the business interpersonal skills matter a lot. You're not going to be able to replace sitting down with the customer at a QBR and understanding their business.

• 50:54Speaker 00

  Because our AI as an enterprise can understand our business, it can't understand the businesses of advertisers. So we always say, our job is to be their growth consultant. It's to understand what their needs are and then deliver value that meets their needs on our platform. And so there's a lot of interpersonal relationship skills there that the AI can't replace. What we want to get to is a place where the team is scalable, most of the processes are automated. And, and then those people are really sitting down with the customer, making a difference and building relationships.

• 51:23Molly O'Shea?

  So I guess most people are trying to rebuild right now because they just, they're acting a little bit more reactively. Versus proactively is what I was trying to get at. And so in terms of others that are trying to rebuild right now, what do you think the biggest mistakes that they'll make are?

• 51:40Speaker 01

  This is a very interesting question, right? Everyone wouldn't, I think everyone here would agree. The AI is making, is helping us to improve the productivity of every engineer, every business person tremendously. But it's also kind of interesting to see that not every company, not every organization has been able to multiply their output. As a result of the AI empowerment. I think a big, big mistake that people are making is that because of AI, building things become easier. People are making a lot of bad decisions, bad taste, wasteful ideas that actually offset the empowerment of AI.

• 52:24Speaker 01

  Right? You, you build things faster, but you're, you're making a lot of bad tasted ideas. And in the end, you might not see the, the end, end result. And this is also why I don't think, you know, we just have to rebuild everything simply because of AI. We need to understand what's the problem we need to solve. AI usually doesn't, AI usually helps, helps us to solve the problem, but actually doesn't change the problem we have to solve.

• 52:49Speaker 00

  I think the fact is, probably 99% of people that use AI today just use it as a search engine. And so you're left with like, okay, as an organization or running an organization, that's really not what you want, because it could do so much more. But how do you get your organization in a position where your people actually know how to use these tools better? And you've got AI-native companies, they were started in this era, okay, that's easy, they're hiring for it, and they're structured as such. You, us, we were able to stay lean all the way through and maintain a high bar on talent. So most of our people should technically be AI-native and figure it out. If they're not, they're probably not going to survive here because they'll fall behind. And then you've got other companies that are not really using it appropriately today

• 53:33Speaker 00

  because their people aren't AI-native, and they're not built as an AI-native company. And so they've got people that, in some ways, they may even like push back at the usage because they want to justify their jobs. And so how do you turn those organizations into AI-native? It's really, really hard. You can't just cut people out and say, okay, the rest are going to figure it out, because how do you know who to cut and who not to cut? You can't just go, here's a budget, go token max and figure it out, because people start building a bunch of slop. And then you have business people building dashboards, and engineers building dashboards, everyone's paying Anthropic and OpenAI for the same stuff. Like, it's like, what are we doing here? And so that doesn't get you there. And I think this is going to be a challenge for a lot of companies.

• 54:15Speaker 00

  I don't think there's a great answer to it, because technically, you almost have to replace near everyone and rebuild the culture up to be able to deal with the world as it is today. But that's not really possible for most companies. And so there's no clear answer for how those companies can get there.

• 54:31Molly O'Shea?

  Bummer.

• 54:32Speaker 01

  Yeah. I think a lot of people think, you know, the biggest differentiation for a great company versus a not-so-great company is the ability of building. But I actually think it's different. It's the taste. It's the taste to know what to build and what not to build.

• 54:47Molly O'Shea?

  I'm curious, Gio, what are the tools that you use today, and how have they changed?

• 54:55Speaker 01

  I'm a big open-source fan.

• 54:57Molly O'Shea?

  Okay.

• 54:58Speaker 01

  So like, for the idea, I use VS Code, right? In terms of coding, I use Cloud for coding. But for like text tasks, because I like to use ChatGPT these days. I used to be a Gemini person, but recently I switched to ChatGPT. ChatGPT recently, hey, you know, often challenges me, often disagrees with me, but it provides a very, very good perspective. But these things change. Like, a year ago, exactly, you know, I felt exactly the same contrast, and I switched from GPT to Gemini. So who knows what's going to happen next a few months?

• 55:35Molly O'Shea?

  Today's episode is sponsored by V C X by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Some of you may not have heard this yet, but our sponsor, Public, just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cash flow, or defense tech companies growing revenue over 25% year over year. Public's AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis. What really stands out is how clearly it explains why each stock is included.

• 56:20Molly O'Shea?

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• 57:03Molly O'Shea?

  Yeah, I was on a big ChatGPT for text-based, because for all these episodes, I'll take the transcript, I'll be like, okay, give me a summary. Let's work with that, let's make a newsletter out of it. And then I switched to Claude, and then I spent a whole day yelling at Claude last week, and it was so exhausting, and I hope it doesn't retaliate.

• 57:25Speaker 01

  It's okay. Yeah, Claude is very logical, but I just feel it's not perfect for...

• 57:31Molly O'Shea?

  It started like using English, like UK, like verbiage and numbering systems. Like, it was writing dates in... It made those... It was really aggravating.

• 57:42Speaker 01

  It actually invented its own slogan, and like, code words.

• 57:45Molly O'Shea?

  Yeah. Okay, so I'm really curious, as it relates to the LLMs and the chatbots, that is new grounds. You guys, primarily, if not 100%, you advertise on gaming apps, right? So do you extend into LLMs, chatbots? Like, how do you see the ground shaping?

• 58:09Speaker 00

  Yeah, you mean in terms of advertising?

• 58:12Molly O'Shea?

  Yeah.

• 58:13Speaker 00

  Yeah, it's, I think it's still early. And if you think about my statement a couple minutes ago, if 99% of the use case is search, then the ads in chatbots are probably going to look and feel a lot like search. And what is a search ad? Well, there's two types of advertising. One is bottom-of-funnel, which typically is search. And a user goes to a search engine and says, I want this type of pair of shoes, and the result will give them those shoes and some other shoes. And sometimes users will click other things, but usually it'll just close the conversion funnel. The consumer knows what they want to buy, the search engine places them where they need to go, transaction complete. Tons of value in that, because you don't want that consumer to land on your competition, but the user already knows what they're going to get.

• 58:57Speaker 00

  Then you have top-of-funnel advertising, which is, the consumer doesn't actually know that they want to buy something, and they see something and they discover that they want to buy it. And this is best example, largest example is Instagram. And you go to Instagram today, and a lot of people will say, I do my window shopping and figure out what I want to buy on Instagram, while the ads are top-of-funnel. They're, that consumer didn't open up Instagram, know that they're going to buy that dress, but they saw a dress and they went and transacted. We're trying to go show consumers things that they don't know they want to go get, whether it's games that they have never seen before, or something in shopping that they didn't understand that they wanted to go buy. We want to start the funnel and then close the loop. And so our solution isn't the like, highly relevant bottom-of-funnel advertising.

• 59:42Speaker 00

  If that becomes a big space outside of the frontier labs and some of the main destinations in large language models, we can look, but there's a really good search advertising business out there that probably extends into it. For us, what is compelling as we think about, okay, well, what's next in our business, is what are the other spaces that create this form of discovery, where you don't actually know what the user wants? And so an example of that, that we've been working on and still doing some research and development on to see if we can extend our platform to is connected TV, television. User doesn't know what they want. If you can start them down the flow and convert them, that's super high value. The open web and video placements inside the open web.

• 1:00:27Speaker 00

  If you're on a recipe site and you see a video, maybe the obvious ad would be, okay, something related to the recipe, but the less obvious ad might be something completely unrelated and might create more value. And so that's another space that we look at over time, this, again, this like three, five, 10-year time horizon, where we can really extend our product offering. So we certainly will track what's going on in usage of these chatbots, because there's just explosive usage there, but it's probably not the most well-suited for our advertising model.

• 1:00:59Molly O'Shea?

  It was interesting, I was listening to this podcast you did with Patrick O'Shaughnessy back in 2022, and you were explaining how delicate, but dynamic and competitive the gaming ecosystem is, where you are partners with Apple. You have a lot of gaming apps on, you know, the Apple App Store, like, and then you're also partners with Google on ads. Are there any other environments where you think that could be replicated?

• 1:01:24Speaker 00

  Unclear. I mean, because you're just in such a duopoly state in mobile, right? Like, anyone who has a business today to the consumer, and even if ads are B2B, it's B2B to C, you're going to sit probably on top of one of those two platforms, most likely both. There's, if you think about like connected TV as an example, it's completely fragmented, the operating systems, the highest use operating system is not dominated by one. And so you don't have that same framework, the open web by definition is open. And so probably where we end up venturing out to is going to be more open. And if we ever got into advertising with some of the chatbots or large language models,

• 1:02:07Speaker 00

  you'd say, would one of these frontier labs become an operating system? It doesn't look like they're going that way. So like, even there, you're going to have fragmentation and proliferation of a lot of different sites.

• 1:02:17Molly O'Shea?

  Okay, I have a hard question. How are you going to become a trillion dollar company?

• 1:02:23Speaker 00

  I mean, as an advertising business, again, I said this earlier in the pod, but you got to make money. Otherwise, you don't have a really good advertising platform. And we fortunately make a lot of cash. And we like to think, just in traditional finance terms, you're worth your cash flow after SBC and what you expect that to be many years out into the future and the terminal value of it. And if you think about where we are today, we probably on an EBITDA basis, I think we're over $7 billion run rate. And we generate somewhere around 75% cash off the EBITDA dollar. Now, the business itself, and then that's sort of after SBC. If you took us three years ago, that number was much, much smaller.

• 1:03:07Speaker 00

  It was probably 1/20th of where we've gotten to in three years. To take it from this scale up and be worth a trillion dollars, we've got to believe that we can get to 30 billion plus of cash flow a year. If we could get to 30 billion dollars plus cash flow a year, depends on how quickly you can do that in the future. Investors probably give you a pretty good multiple on my cash flow to get you to that. And so when we think about the things that we're working on, everything that we work on has to have a very large economic opportunity. So typically in investor terms, you think about total addressable market. Well, the games category, we've done really well. And we built the number one place for mobile game developers to go market themselves. But the mobile gaming category is not big enough in terms of user acquisition dollars

• 1:03:51Speaker 00

  to support the type of cash flow we need to be valued at a trillion dollars. So hence, you go into the consumer side and you really widen up the type of dollars that you can go get and the companies that you could service that on its own, probably in the sort of shorter term time horizons. And again, when I talk shorter term, this is five years plus, but can't get us there. So then you have to start thinking about, okay, what else beyond that? And one of the things that is very, very compelling is you look at the companies that have been B2B to C or B2C and are worth over a trillion dollars is they started with a product, they generated a ton of value off that product, and they started extending to adjacent categories and executed exceptionally well there to get there.

• 1:04:35Speaker 00

  And so I think it's geo my job as partners in the business to go, what are those other things that we're also going to do on top of just executing on our core for the next five, 10 years?

• 1:04:47Speaker 01

  On engineering side, we also have to be propelled for that kind of growth. So when we are building systems, for example, the data infrastructures, right? We just have to project that in order to support a company at a trillion dollar value, what is this scale we have to factor into the design? When we are designing, let's say, a product, we have to factor whether this design, when they is able to support the expansion of different verticals.

• 1:05:13Molly O'Shea?

  So you're not going to build a hyperscaler?

• 1:05:16Speaker 00

  Probably not. Yeah, we don't have much CapEx. One of the things I also learned early on is you got to be very confident about where you're going as an executive, and you also have to set goals. And like, as you keep adding zeros, it gets harder. But when we first started, it was, I want to become a billion dollar company. And I conveyed it to the team, we had a plan for it. When we got to a billion, it was, okay, let's become a $10 billion company. When we went public, we wanted to become a $100 billion company. We dropped 92% in the first 18 months of being public. That dream seemed very, very far away, but post Axon 2, we recovered and now we're a $100 billion company. Again, it gets harder when you add zeros, but you also, on the other side of it, as you scale a business, you matter more.

• 1:06:01Speaker 00

  Your technology is more compelling, the data that you have access to is much larger scale. You reach network effects. So a lot of times, a lot of things get easier, which is why, even though it feels very, very daunting, if you have a plan around it or people have confidence that you can direct it that way, you can eventually get there.

• 1:06:19Molly O'Shea?

  That's awesome. Well, okay, so to go back a little bit, you guys dropped 90, over 90% as a stock. There are companies that are doing that right now. The market's super volatile, SaaS is getting hit really badly. But you guys played offense, you kind of like dug your heels into the ground. I don't know if that's the right term. And then you just went after it. So do you think this is a different environment for that circumstance? Do you think those companies, what does it take to pull out of a 90%?

• 1:06:49Speaker 00

  I think it depends on the business model. So like, the challenge you have when stock price goes down is your stock-based compensation as a percentage of total goes way up. And so let's say like, you're a company that typically like companies will issue 2%, 3% of their equity every single year to the team. Well, there's dollar value with that. If the stock goes down 90%, are you going to issue 30% dilution every year? You'd never recover from that. So we always thought about that in fixed dollar terms. And we want to pay our people exceptionally well, but we don't want to overpay our people and we keep a lean team. So it was always manageable. And then on the other side of that, we generate a ton of cash.

• 1:07:29Speaker 00

  So when the stock tanked, we didn't have to go, look, we got to go convince investors to buy the stock. We actually didn't do any investor relations for well over a year at the bottom because what were we going to convey to investors? So we're rebuilding our technology, stock is dirt cheap, but nobody buys something that's dirt cheap. They want to see a vision, they want to see the long-term opportunity and growth prospects. Investors always, just like the private markets, will chase something that they expect is going to grow really quickly, but they very rarely tend to buy something where they think it's just cheap because things trade wherever they deserve to trade. So we were able to flip it and say, we're just going to buy our own shares. We're going to become our best investor. We're going to give our team nice equity compensation, but not overdo it because we can't afford excessive dilution.

• 1:08:14Speaker 00

  And on the other side of that, we're going to deploy every dollar we make and more and just start buying back our own shares and be our best investor. That was only possible because we had conviction in what we were building and conviction in our future growth opportunities. So for us to disconnect with the public market investor was that they felt like we had no growth opportunities. We on the inside fundamentally believed we can extract a ton of growth out of the business with the right technologies. And I think the challenge is not every company is constructed the same way. Some don't have the cash flow to do that. Some don't have the capacity to actually grow. They're trading where they trade because those growth prospects aren't there. And like, especially when you talk about enterprise SaaS, very robust platforms, but they're not algorithmic.

• 1:08:57Speaker 00

  So how do you actually accelerate revenue without go-to-market teams? And if you have to hire more go-to-market people and your stock is low and your burn goes up, it's just a downward spiral. And so it is company-specific, and some of these companies will have a tough time recovering.

• 1:09:11Molly O'Shea?

  What do you think is going to happen to them?

• 1:09:13Speaker 00

  A lot of times when companies reach that point where it's tough and revenue multiple and potential cash flow multiple, if you do big layoffs, ends up pretty low, they start getting accumulated by private equity or effectively plucked off the public markets, taken private, because the only way to recover it is a complete restructure. You fire a lot of people, you cash flow it, you lever the business up, and that fits the private equity model, it doesn't fit the public markets all that well anymore. Once you can't convey that you've got pricing pressure in a big market on the other side, you tend to lose all your multiple in the public markets and then you're not a good public company.

• 1:09:48Molly O'Shea?

  As we think about the next 12 months, I don't know if you guys think about in 12 months. Some people think in the next week, I don't know. It's crazy right now. What are you most excited about?

• 1:10:02Speaker 01

  What I'm most excited about is, I think, you know, what kept me excited in the last three years has been pretty constant. You know, the team is constantly making changes to the models. We are improving our infrastructure. Also, as I previously shared, right, like as the business is growing, the number of people stays the same, which actually means every team member is growing together with the company, which is, by the way, is not typical, no matter what company you're looking at, right? All the hottest companies we are seeing, they are growing, but their headcount grows at the same time, which means individuals in this company does not have the same growth speed as the company itself. But I think a belief is different. So every individual in the company just has the outsized growth.

• 1:10:47Speaker 01

  And I think seeing the company grow, seeing myself grow, seeing the people in the team are learning more and becoming more mature, that just keeps me excited.

• 1:10:58Speaker 00

  So I don't really know the feeling.

• 1:11:01Speaker 00

  I don't get excited.

• 1:11:03Molly O'Shea?

  What do you feel? Do you feel anything?

• 1:11:05Speaker 00

  Well, it's like, I'll give you an example. Like, when we went public, a lot of people celebrate, think, okay, we went public, this is a great milestone. For me, it was, people just bought our stock at $30 billion market cap. Holy shit, we got to make those people money. And so it's, I never think of the world in excitement terms. I also don't get, on the other side of the spectrum, I just don't get down. Everything is a grind. We're trying to build something that hopefully lasts years after we're both gone. And we're thinking about time horizons very far into the future. And so if you get excited, you over-index on something short-term that probably isn't something that's relevant long-term. And on the other hand, if you get down, it's also really hard. So I've just learned to manage both those emotions out and really try to think,

• 1:11:50Speaker 00

  okay, let's make sure that we have the right people here and the right path, and then everything else will fall into place.

• 1:11:57Molly O'Shea?

  Are there any certain parts about what's accelerating in AI or at least turbulence in the market or anything macro that is concerning you or keeping you up at night?

• 1:12:09Speaker 00

  I've had a 92 sleep score last night, so I sleep pretty well. So that's also like, look, like in a competitive field like this, we were told when we were went out first looking for seed funding at $4 million valuation, big companies are gonna put us out of business, we're not even worth a million over four. When you're competing in a field, always, you can't lose sleep over things around you. What you have to do is be very aware of everything around you, and you have to be able to have some aspect of being visionary and seeing around corners that others can't, and trying to build, again, for the long term, just the right way. And because we've always, in some ways, we've always been disadvantaged and had to become

• 1:12:50Speaker 00

  advantaged in the face of those disadvantages, we, I don't know that anyone at the company loses sleep on what's around us. It's more, the world of technology is moving really, really quickly. If you live in a world of tech debt and you're unwilling to move quickly with it, then you're potentially gonna lose, and that would get us nervous. But we're constructed for the way the world is today. We got a little lucky that some of the things that we believed ended up fitting perfectly today. We couldn't do, as Gio said a couple minutes ago, what we're doing today with the team that we have if we were not now in partnership with AI, enabled by AI to go faster. So a lot of the stuff that's happening in the world in ways is exciting for us because it fits exactly what it is that we're constructed to do.

• 1:13:33Speaker 00

  But the reason I don't get overly excited about it is because you still have to execute. End of the day, it's the burden of execution is still present. You gotta have the right people, you gotta have that hunger, you still gotta be able to push forward hard.

• 1:13:45Molly O'Shea?

  Okay, I have to ask you both this now, based off of that. What happens when we hit and if we hit singularity?

• 1:13:53Speaker 01

  What do you mean by singularity?

• 1:13:57Speaker 00

  Yeah, I don't know what that means. Some people say we already hit it.

• 1:14:00Molly O'Shea?

  Everything is autonomous, I don't know.

• 1:14:01Speaker 00

  Some people say we already hit it.

• 1:14:02Molly O'Shea?

  The machine takes over.

• 1:14:03Speaker 01

  That's a bigger problem than, I think that's a bigger question than uploving. So sometimes I have to believe that that singularity moment will never come. This is not like, you know, opinion from fact, it's opinion from my standing. As a human, I have to defend the value of humans, right? But overall, I think seeing how, you know, AI is advancing, I feel pretty optimistic as a company. I think thanks to our structure, we, our structure allows us to benefit from the improvement of AI much more than our competitors. So overall, if, you know, AI is just improving at the current speed, I feel it's a positive thing for us.

• 1:14:45Speaker 00

  You could probably find like 100 very smart people right now on Twitter saying we're at singularity now. So it's like, it's a weird one. I mean, I think a lot of us in tech live in the Twitterverse. The world is much bigger. We get questions like, well, isn't the chatbot gonna tell everyone what to buy and just complete the transaction and no one will ever do ecommerce off of an ad again? And what that loses sight of is that people actually like to shop, and people want to look at different products, and people want to click buttons on a shopping site, and people wanna hit buy and then track their purchase, and get the box, and open the box, and whatever. End of the day, we're all still human beings, and different people have different understanding of how these technologies can work, but probably most of the real world is not gonna understand

• 1:15:29Speaker 00

  how these technologies work at any level, like we all talk about in the Twitterverse. And so it's, the reality is, whether we get to that point or not, it's gonna be different impact for different people, and they're still, we're still gonna have to understand that humans need human interaction, humans need human solutions as well, end of the day.

• 1:15:49Molly O'Shea?

  Okay, I have two more questions left. One, maybe that was your answer, but what is your hottest take right now?

• 1:15:55Speaker 01

  I think just in this era, you know, AI allows everyone to build almost everything you want, taste is a very important thing, and I think has been underestimated.

• 1:16:04Molly O'Shea?

  Yeah, I was just listening to David's interview with Mickey Malka, and Mickey Malka was talking about how Silicon Valley lost the ability to make beautiful things.

• 1:16:15Speaker 01

  Yeah, no, once it tastes, not just aesthetics, right, it's also engineering taste. I, you know, just from personal experience, I would attribute the success of Axon, you know, largely to what we decide not to do, not actually what we did. Because I saw this, you know, these mistakes that all these small companies, when they're trying to challenge giants, what they're trying to do is to copy exactly what giants are doing. But the problem is, you're just such a smaller team, you have much less resources, you have no chance to win if you're trying to do exactly the same thing as your giant competitors. I think this is when the taste comes to play.

• 1:16:56Speaker 01

  We know our difference, we know where our strength is, so we deliberately decide what to focus on and what to not work on.

• 1:17:03Molly O'Shea?

  Well, where do you get your inspiration from? Do you get it from outside sources, from like abstract things?

• 1:17:09Speaker 01

  First principle, just first principle, I guess you just have to, you know, like, ask the questions of what, how, and why. I think a lot of times people overly focus on what. People say, oh, that's AI, let's do some AI in the company. That's a, like, you know, that's, let's see how AI can, can, can help our business. And then they will start hiring a big team working on AI without asking how. As AI, how do you apply AI in your product? And then why? Does your product actually need that? Right? This applies to AI, it also applies to previous generation of technology. So when we were building Axon 2.0, the team was really, really small.

• 1:17:50Speaker 01

  You know, I had a lot of experience in the industry, I knew how a recommendation system looks like in many, you know, big companies. I think it was impossible, it will be, it would have been a big, big mistake if we tried to hire a huge team and try to replicate every single piece of that. So it is important to filter through all the noises and figure out what is this 1% of most important thing, and you have everything you can in your team to focus on it.

• 1:18:18Speaker 00

  My hot take, I guess, going back to marketing, is the, there's a lot of animosity towards AI in the world right now. And people on, again, on the Twitterverse talk about AI as it's so amazing. Well, if 99% of people are just using it as a search engine, and then they're hearing a lot of smart people saying it's gonna take jobs away, and there's gonna be all this loss here and there, why would you be a proponent of AI? And to me, it comes down to marketing, and the way to flip people is always talk about the positive of something and let them really see it even when they don't understand it. And back to our business, we serve ads. So like, end of the day, like, ads could be seen as annoying for most people, most people don't notice the ad. If we just went out and we said we serve ads, that wouldn't get anyone excited.

• 1:19:02Speaker 00

  But you have to understand that an advertisement is creating a transaction, and it's creating a transaction that otherwise wouldn't have happened. So it's creating economics. It's helping expand the economy, it's helping grow businesses, it's helping those businesses hire people. And so when you have a really good ads framework, you actually are creating a positive catalyst on the economy. And once we learn to speak about it in those terms, and really cherish the moments where we know some developer, some shop is able to hire people because they're able to get growth that's profitable on our platform, that makes everyone here feel good. And to tie back to the AI narrative, until people start understanding what the average person can have as positive use cases that are incremental to search, they're not gonna

• 1:19:47Speaker 00

  be proponents of AI. And there's no reason to expect them to be.

• 1:19:50Molly O'Shea?

  Sorcery is sponsored by Brex, and they're the performance corporate credit card. So they like to spend smarter, move faster. But I like to ask this question on more of a personal performance standpoint. For you, Adam, Michael Barton said you were the most locked-in person that he's ever met, but I'm curious, from maybe both of your standpoints, how did that come to be? And maybe, is there anybody that you've been inspired by that keeps you motivated? How did you become that locked in?

• 1:20:22Speaker 00

  I don't know if I'm locked in. First of all, that was a great native ad. Your partnership, thank you. But it's, I always optimize my life to remove all distractions and focus on the one thing that I always get out, which was building a business in the category that I'm in. And for me, early on, it was find my passion. I don't know why advertising became the passion, but it was sort of an extension of something I used to do, which is derivatives trading, finance, math, algorithms, placement, prediction. All those things came together in advertising. And so I found my passion, and then I tried not to do things around it that would create distraction. I'm not out there super active on social media, I'm not out there making a whole bunch of

• 1:21:04Speaker 00

  other investments, I'm not out there speaking at a lot of different conferences. I'm basically 100% wired in to try to do the best job that I can with this, and if I can do that well, then I feel like I'll have succeeded. And so that's my answer, Adam.

• 1:21:20Speaker 01

  Yeah, yeah. No, I came to uploving as a pretty junior, middle-level team members. All those things about executive is pretty new to me, like attending conferences and give speeches. Honestly, I haven't, you know, really thought a lot about that. But I think an important part for my success is that, I don't know how, but in my early life, I just developed this kind of desire to not disappoint. I don't like to disappoint people. So whenever people around me, you know, my leaders, Adam, has some expectations on me, I just want to do anything I can to make it work.

• 1:21:59Molly O'Shea?

  Why do you think most people don't do that?

• 1:22:01Speaker 01

  I don't know. Maybe it's the type of vitamin I add since I was young. I don't know. It's just like personality thing.

• 1:22:07Molly O'Shea?

  Does that become an obstacle sometimes?

• 1:22:10Speaker 01

  It could be, yeah. Sometimes I feel like it becomes optical because I had a hard time to pause and enjoy the achievement we have. Like, I don't know if Adam feels the same thing. There was, you know, a lot of excitement in the past because of the success of AppLovin'. But I just find myself hard to take, like, press the pause button and just enjoy that moment.

• 1:22:38Molly O'Shea?

  Well, we do have a trillion-dollar milestone, have us.

• 1:22:42Speaker 00

  I think that you have the next one. This is actually a common trait I've found in really highly competent, successful people is like, there is no moment to really enjoy because there's no sort of goal that you get to once you get to one, you're on to the next one. And it's sort of like, I mean, when you're raised in households where like the A is not good enough, you gotta get the A+. A lot of times that builds this in people. The downside of it is it's hard to reach fulfillment or happiness or some of the emotions like a lot of others would have. But if you're always achieving for more, you're pushing yourself to keep going. And no moment is good enough.

• 1:23:19Molly O'Shea?

  Are there any other traits you look for in players?

• 1:23:21Speaker 01

  I think the type of people I really like in my team, they usually have two characters. So one is intelligence. The second is having low ego. I think the intelligence part is easy to understand. It has to be smart, learns fast, you know, has higher standard for themselves, get things done, right? This is, I think, the character a lot of companies are looking at. But I think the second part, having low ego, is often overlooked. When a person has low ego, he tends to listen. He tends to empower his team, tends to help others. And more importantly, they take feedbacks. And then they proactively question themselves. And this is a very important part for growth.

• 1:24:07Speaker 00

  And it usually helps with the third thing, which I look for and always ask about, is how they react to adversity. It's, nothing is easy in the world. And some people grew up with easier upbringings than other people. And usually, people who know how to respond well to adversity do really well.

• 1:24:22Speaker 01

  Yeah. This is kind of like a danger for high performers. Because when you're a high performer, a lot of people around you, they observe you as a high performer. They trust you. They don't question you, right? So it becomes extremely important for those high performers to be able to question themselves. And I've seen, you know, some people did extremely well in this, but some others might forget to be the one that challenges themselves.

• 1:24:48Molly O'Shea?

  I know I said I already asked a difficult question, and I almost left out without asking this one. But Adam, okay, you built out a gaming advertising company. You're in the gaming space. You know who else was in the gaming space? Okay. We had Torsten, who then went on to found Helsing. We had Palmer Lucky, who went on to found Anduril. What comes after AppLovin' for you?

• 1:25:16Speaker 00

  That's a tough question. I'm probably, hopefully, if I'm good enough and earn people's trust around me, gonna be the CEO for a long time. And so, I don't think about the after at this point as a public company CEO running a business this large. I think it changed for me along the way because I'll say when we were smaller, I did used to think about building again. And I do really love the early stage. But not a lot of people in the world have gotten the privilege to be able to work with people and together build something from zero to $100 billion plus. And so where we are, we've just got so much opportunity in front of us, I can't really sit there thinking about what's after this.

• 1:25:56Molly O'Shea?

  Wow, that's a great place to end. Well, Gio, Adam, this was such an interesting and different and thoughtful conversation. Thank you so much for giving a deep dive inside the engineering side of AppLovin' and the growth that you guys have achieved. I really appreciate it.

• 1:26:13Speaker 00

  Yeah, thanks for having us.

• 1:26:14Molly O'Shea?

  Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery.vc, where we deliver a once-a-week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.