Inside AppLovin’s $100B Ad Engine

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 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.

Once we're able to make predictions more accurate, advertisers see better returns and our business growth. I don't want our engineers 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 solving more challenging problems.

I would attribute the success of Axon largely to what we decided not to do, not actually what we did.

How are you going to become a trillion-dollar company?

Adam and Gio, welcome to Sourcery.

Thanks for having us.

Thank you for having us.

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

Yep.

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

What were your first impressions?

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 as very lean. Both Adam and CTO Basil at that time, they were deeply involved in 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 I realized, you know, in the company, the company was small. 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 were 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, at that time, I didn't 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. And it was also, I was glad to see that the whole team at that time has a very 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.

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 $5.5 billion company when we started talking to Gio. And we had a model that powered our advertising business, Axon 1 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 2 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, and he's supposed to join, I think it was like a couple 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 in 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.

Smart. It's arbitrage. You know you're going to make it go up.

I wasn't thinking that way, but, you know, I was in big tech. If I just wanted to stay 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.

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

I mean, I guess I didn't start asking questions. I know what my position is when I joined. I saw those gaps, and there were not a great model at the time. But before training the model, you need to have theright 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 a 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 with code.

And that was much more efficient.

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. 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 to 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 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.

One of the challenges with that style, though, is that when you don't have a team constructed to actually 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.

How big was the engineering team when you joined?

It's probably same size as now.

100 people?

100 people,right? Yeah.

Yeah.

How big was the organization?

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. I'd say the company's been around 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, and myself. And we talked through the problems, and it was like just real-time feedback. And then, I don't know if him and Basil even saw 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 into 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 get to a direction that they thought was theright 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 could and 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 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."

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 2?

So

when Axon 2 was being architected, the team at that time was very different from today. So I'm going to explain what happened at that time. When I talk interviews, a lot of times people are very curious about what I did to change AppLovin.

But very rarely people asked 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 work, 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. 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 trip to Europe for a month for Christmas. I won't have much time to work during that month anyways. How about I join after that?" But Basil insisted that 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 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 at that time.

It was the moment that ChatGPT 3.0 just came out, but it was definitely not enough to write code for you at that time. So we still have to write code in the traditional way. I was on my flight to Italy.

It was like 12 hours of flight. I had to continue to code. At that time, you know, people still know how to code without an agent. I don't think you can still do that without internet today. So I was on the flight.

There was no internet. I was coding the Axon 2.0. At that time, I was building the training infrastructure. And at a certain point, you know, I need to check a document. I need to Google search some information about the tool I was using.

But I was on the flight. I have no access to internet. And I had to force 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 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 an infinite amount of time?" Because I was literally trapped 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. My in-law, she's a lovely Italian lady. She couldn't understand why anyone has to work that hard during that vacation. She kept 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 infrastructure part of Axon 2.0.

The moment I came back, we started training the first generation of the model.

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

Yeah, it was a reset. I feel like, you know, that kind of philosophy is already there in me. And I definitely love 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, "This is a place. I love to work here."

So big tech is very different.

It was very different, yeah.

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

Right.

What was that like for you?

So let me go a little bit deep about what Axon 2.0 actually is. 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 a recommendation system has to study the relationship of each combination. Before, the last generation model tried to model this by clustering those combinations into different clusters and built this tree-based model that's basically just a huge set, like hundreds of thousands of if-else branches.

And then each cluster of user item combination sits on the end of this if-else branch. But obviously, as you can see, the real world is so complicated, and the relationship changes dynamically as time changes, as the weather changes, as there are promotion events, holidays, all the things are changing.

And these tree-based models are not able to accurately model this dynamic relationship between user and items. The Axon 2 user approach that uses this concept nowadays, people are very familiar with this thanks to the language model. It's 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 meaning. We encode these IDs into semantically meaningful embeddings, and it carries statistical information. And then we pass those information through a deep neural network that allows us to study the complicated interactions between user and items.

The result of this kind of model is it is much more powerful in recognizing the patterns, and it's also powerful in extrapolating these patterns to unseen data and unseen user and item pairs. And once we build this, our model is able to make more accurate predictions.

It was actually also cheaper in terms of infrastructure. So once we're able to make prediction more accurate, advertisers see better returns and our business grows.

Why was it cheaper?

This just has to do with some details of how the model is being inferenced. The neural network, the benefit of neural network is it's made up of a lot of standard GMM, basically general metrics multiplication, which is the famous, the GPU.

GPU is highly optimized for this kind of operation. But the selection tree, the old generation selector trees has 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 modern GPU architecture.

So you're not token maxing.

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

I token max every time I hear Gio talk. I have to put it in that alarm. Translate what Gio said for me.

So, Adam, can you translate what he just said?

I'm not smart enough, unfortunately.

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?

Basically, you know, the type of data, the most important data is the user interaction with the ads. Because every ad we shoot to a user is an important feedback data point for us. Users either react to it or not react to it.

That information, if you collect those information, that builds a very powerful prediction machine for the user's next action.

It's also important to understand our ads in this context. We're not 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. That's like a mini-game serving website. Tons of data is able to be extracted out of those engagements.

What is more data that you have behind those ads? Because you serve 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?

How do you determine that?

Well, you have the engagement. So you have how they're playing, what they're engaging with. Then you follow them through a 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 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 and 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 in-app purchasing revenue for the customer. The benefit of doing what we do is that 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 Gio's system can then take all of this data, which across our business is 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?"

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

Yeah, totally. 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 spent. 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, 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&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.

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

It was about a year after we scaled Axon 2.0. I think at that time we realized the model architecture we had would work beyond just gaming. Of course, there are new things we have to build, the new business, the new pipeline, the new product flows.

But we found that the core technology should be general enough to support different verticals.

Yeah, I mean, I think when Gio built and architected Axon 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'll 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, when they went public, there was this notion that you couldn't monetize the social network.

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 just start working. But I think Gio 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.

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 kinds of things. At the time, 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 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.

What's your process for testing?

For testing the e-commerce?

Yeah.

So in the beginning, it was building, not really testing,right? The building story was also fascinating. I think at that time, the CTO, Basil, and then there were some other senior leaders in the company who were attending a Google conference in Vegas.

And on that morning, we were having breakfast and we were discussing how do we build the first data flow engine for this new product. And we were waiting for our manual to come. And there was no paper. So we just found a napkin and started discussing and enjoying design and napkin.

You probably did. Do you know this story?

No, I don't know this story.

Can I never tell you the story? Okay, you should talk about hanging out more with Basil. And then we were having this discussion. And by the time the food arrived, we already had 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 of months, 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 had very limited data. So the model was very premature. But even with a very premature model, we were able to see people were making a purchase.

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

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

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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/sourcery. So are you then working directly with the advertisers 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?

So the communication with advertisers is still through the business team. But all the data and decisions, our engineers understand what a business needs is. Our engineers, when we have to talk to advertisers, 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.

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 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 and it costs $5 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 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 scalable."

Did you always have that intuition, or was that also built up?

I think that's definitely built up. It's definitely built up. Before joining AppLovin, in my previous job, I already carried both the engineering part and also the product part of the work. 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 to try to help our engineers to understand the full context. Instead of assigning tasks to them and telling them to do exactly what they have to do, I showed them how that work is impacting the business.

And because our engineers are very talented, once they are provided with that kind of context, they have a much better sense of what to build.

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?

The biggest mistake?

Yeah.

I wouldn't be able to comment in general, but I think on our team, I think a few mistakes could be, first of all, not setting them theright context. I feel people usually will assume the junior people need hand-holding.

And therefore, they only provide very granular tasks for them. But I think this is not the great way of helping 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 when a junior team member does a good job, 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 stop questioning themselves.

That will also become a problem for their early growth.

How do you continuously challenge each other internally?

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 hand-hold new talent and bring them along slowly. So one of the things that's a little unique is whether Gio is an intern or a full-time employee, just started maybe evenright 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 going to 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 here, they come in an intersection of math and engineering, 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 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, "OK, now you can push your first test." Here, within a week, they can push a test. They start getting results. That moment is a pretty substantial dopamine hit.

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 that bring people along.

There's no training manual. There's no come in and someone's going to 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 going to 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. 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.

You've been.

Sorry for interrupting.

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 want to call it. How do you evaluate people on that? How do you make sure that they're continuously upgrading themselves and upgrading their skills?

So for people, of course, I think the AI works differently in the business team comparing to the engineering team. I can speak about the 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 helping you to shape your opinions. But still, AI is not good at doing long-horizon planning. A lot of work that we have to do today requires long-horizon planning.

So then we still need humans to be involved. Maybe one day AI will 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 engineers to sit on top of AI. So as AI is improving, this boundary between AI and a human is also moving. Previously, AI was only used for correcting the syntax errors when they are coding. And then AI became better.

AI can complete the whole phrase, the whole paragraph for your coding. And nowadays, AI, you can just give him an idea. AI can write the entire PR. But as AI is improving, humans just sit on top of it and focus on what AI is not good at.

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

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

I think at this moment, yes. At this moment, yes.

Why?

But I'm not sure that my answer will keep the same in the future.

OK.

At this moment, I think AI still very often still makes bad decisions or coding decisions. If our engineers are not capable of coding themselves, they will not be able to judge and correct that mistake.

Then you'd blow up.

Yeah. That's probably a lot of companies say it,right? We don't have that problem at this moment. Our code is still pretty clean. Internally, we also have this concept of what is the system you want to prototype and what is the core system that you have to keep absolutely clean.

When people just want to set up a dashboard, want to create a temporary project for a presentation, they do whatever they want. 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.

You can use AI however you want. But in the end of the day, you as a human is held accountable 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 implement 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.

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, "Give it to me out of the box."

If that was the case, then everyone would have technology that looked identical to each other. We say this in ad creative, too. If the AI starts writing all the ad creative, then all of it's going to 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 variants on that. And not just the simple prompt to get an output that anyone could deliver, 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 going to be a skill that's required if companies want to stay ahead of each other. Otherwise, everyone's going to land in the same place on a lot of these functions.

Can I ask 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 AppLovin 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.

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 want to 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 going to 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 going to convert the user.

They're going to 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 have 10 minutes with the user. Well, if you're giving them 10 minutes of content, it better be interesting.

Otherwise, you're actually going to turn them off. 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 is going to 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 want to just change very small things around.

You want to create concepts that are differentiated to find what is the user actually going to 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 each brand has to figure out what is the user going to respond to in our framework where they have all of this time. And then they got to 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 want to come up with best practices. Here's a list of 10 things to go do. Simplify it. But end of the day, 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.

Has there been any sort of, I guess, has there been any sort of resolve in whether UGC is taking over traditional ads? I know with the proliferation of clipping, it's really cool seeing on social media. I put out content every day.

And I put out content before where there was this prebiotic, probiotic 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 4X ROI ad for them.

And they just made so much money on it. I got no money. But there are different kinds of things that have made more ads more effective than others in 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?

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, 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 time frame, 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.

So you have smarter people.

I wouldn't call them smarter. Differently constructed minds.

There is alpha in this.

They're just wired differently. But this is part of the education process. 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 the ad stayed pretty consistent since then because it's just a really good ad. You get the mini game, and then you get the download behind. We got into 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 where alpha comes to play here is some of the customers who go, "I'm going to invest in this platform early on 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 to create much larger campaigns at successful metrics on us than 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 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.

How has AI changed the making of ads?

It's early. It's not 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 anything wrong in that video.

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 time frame, 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 going to create lifts and campaigns.

But their tools are available now so that humans can create ads much faster and much lower cost. 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.

Yeah. From our product, we saw a lot of AI-assisted content on the platform by advertisers. I think it's important to still have the human in the loop to guardrail the process because advertisement is about trust. If you create a content that breaks the brand image, and it'll break the trust, and it's harder to convince users to buy.

So there are two different types in terms of AI-generated ads content. There are two different approaches you can try to approach it. One is you make this tool entirely automatic. This is if AppLovin is building such a product, this is what we aim 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.

Soright now, we see that the second type of tools has been pretty widely adopted. But I think there's still more challenges for the first approach, and we're still working on that.

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

I mean, 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 our product. 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. Generally, when we see these problems that are hard to solve and specific more to us than just the general market, our team has to develop to it.

OK, 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 rebuildright now for the AI era. I've listened to many of, Adam, your interviews where you're talking about you build for the next three, four, or five years long ahead of you.

So whatever you builtright now, 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?

Yeah, I don't really want to rebuild. In the last three years, we built a lot of things that made us feel so proud of. AI is powerful, but I don't think that's the reason for us to rebuild. What AI really helps us to do is to build on top of what we have today.

With the empowerment of AI, we were able to achieve much more. I have been thinking about this growth trajectory, this growth journey of our company. As we shared previously, the size of the engineering team was basically the same size as today as three years ago.

And typically, when a company went through such a growth of their business, 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 team size.

But luckily, I feel our growth just was perfectly in sync with

the advancement 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 the team while we're solving more challenging problems. As we're speaking now, we built this model, the new generation model three years ago.

Now, with the assistance of AI, the team was able to tackle and work on a new generation 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.

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 asks 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 a team that actually can create tools, dashboards, and automation for them, 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 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 the 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 then those people are really sitting down with the customer and making a difference in building relationships.

So I guess most people are trying to rebuildright now because 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 rebuildright now, what do you think the biggest mistakes that they'll make are?

This is a very interesting question.

I think everyone here would agree. The AI 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. You build things faster, but you're making a lot of bad-tasted ideas.

And in the end, you might not see the end result. And this is also why I don't think we just have to rebuild everything simply because of AI. We need to understand what's the problem we need to solve.

AI usually helps us to solve the problem, but it actually doesn't change the problem we have to solve.

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, OK, as an organization, a running 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. OK, that's easy. They're hiring for it, and they're structured as such.

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 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 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, OK, 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.

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. 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.

Bummer.

Yeah. I think a lot of people think 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.

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

I'm a big open-source fan.

OK.

For the idea, I use VS Code. In terms of coding, I use Claude for coding. But for text tasks, I like to use ChatGPT these days. I used to be a Gemini person, but recently, I switched to ChatGPT.

ChatGPT recently, hey, often challenges me, often disagrees with me, but it provides a very, very good perspective. But these things change. Like a year ago, I felt exactly the same contrast, and I switched from GPT to Gemini. So who knows what's going to happen next few months?

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Yeah, I was on a big ChatGPT for text base because for all these episodes, I'll take the transcript. I'll be like, OK, 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.

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

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

It actually invents its own slogan, like code words.

Yeah. OK, 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? How do you see the ground shaping?

Yeah, you mean in terms of advertising?

Yeah.

Yeah, I think it's still early. And if you think about my statement a couple of minutes ago, if 99% of the use case is search, then the ads and 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. 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.

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 highly relevant bottom of funnel advertising. 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, OK, 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. If you're on a recipe site and you see a video, maybe the obvious ad would be, OK, 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, 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.

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 the Apple App Store.

And then you're also partners with Google on ads. Are there any other environments where you think that could be replicated?

Unclear. I mean, because you're just in such a duopoly state in mobile,right? Anyone who has a business today to the consumer, and even if ads are B2B, it's B2B2C, you're going to sit probably on top of one of those two platforms, most likely both.

If you think about 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, you'd say, would one of these frontier labs become an operating system?

It doesn't look like they're going that way. So even there, you're going to have fragmentation and proliferation of a lot of different sites.

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

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 SVC 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 a $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 SVC.

If you took us three years ago, that number was much, much smaller. It was probably 1/20 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 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 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 in. 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 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 wind 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, OK, what else beyond that? And one of the things that is very, very compelling is you look at the companies that have been B2B2C 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. 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?

Yeah, on the engineering side, we also have to be prepared for that kind of growth. So when we are building systems, for example, the data infrastructures, 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 in whether this design one day is able to support the expansion of different verticals.

So you're not going to build a hyperscaler?

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 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, OK, 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. 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.

Well, OK, so to go back a little bit, you guys dropped over 90% as a stock. There are companies that are doing thatright now. The market's super volatile. SAS is getting hit really badly. But you guys played offense.

You kind of dug your heels into the ground. I don't know if that's theright 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% drop?

I think it depends on the business model. So 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 you're a company that typically 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's always manageable. And then on the other side of that, we generate a ton of cash. 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? 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.

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, the 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 theright 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 especially when you talk about enterprise SaaS, very robust platforms, but they're not algorithmic. 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.

What do you think is going to happen to them?

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, 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.

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 crazyright now. What are you most excited about?

What I'm most excited about is I think what kept me excited in the last three years has been pretty constant. The team is constantly making changes to the models. We are improving our infrastructure. Also, as I previously shared, 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.

All the hottest companies you are seeing, they are growing, but they had a kind of growth at the same time, which means individuals in this company do not have the same growth speed as the company itself. But I think AppLovin is different.

So every individual in the company just has the outsize of growth. 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.

So I don't really know the feeling. I don't get excited.

What do you feel? Do you feel anything?

Well, it's like I'll give you an example. When we went public, a lot of people celebrate, think, OK, 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

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, OK, let's make sure that we have theright people here and theright path. And then everything else will fall into place.

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?

I've had a 92 sleep score last night, so I sleep pretty well. So that's also look, in a competitive field like this, we were told when we went out first looking for seed funding at $4 million valuation, big companies are going to 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 theright way.

And because we've always, in some ways, we've always been disadvantaged and had to become advantaged in the face of those disadvantages,

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 going to 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 of 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. But the reason I don't get overly excited about it is because you still have to execute.

End of the day, the burden of execution is still present. You got to have theright people. You got to have that hunger. You still got to be able to push forward hard.

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

What do you mean by singularity?

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

I don't know.

Some people say we already hit it.

The machine takes over.

That's a bigger problem. I think that's a bigger question than AppLovin. So sometimes I have to believe that that singularity moment will never come. This is not an opinion from fact. It's an opinion from my standing. As a human, I have to defend the value of humans.

But overall, I think seeing how AI is advancing, I feel pretty optimistic as a company. I think thanks to our structure, our structure allows us to benefit from the improvement of AI much more than our competitors. So overall, if AI is just improving at the current speed, I feel it's a positive thing for us.

You can probably find like 100 very smart peopleright now on Twitter saying we're at singularity now. So it's like 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 going to tell everyone what to buy and just complete the transaction? And no one will ever do e-commerce off of an ad again. And what that leaves aside 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 want to 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 going to understand how these technologies work at any level like we all talk about in the Twitterverse. And so the reality is, whether we get to that point or not, it's going to be different impact for different people.

And we're still going to have to understand that humans need human interaction. Humans need human solutions as well end of the day.

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

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

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.

Yeah, no, why is it taste and not just aesthetics? It's also engineering taste. Just from personal experience, I would attribute the success of Axon largely to what we decide not to do, not actually what we did. Because I saw these mistakes that all these small companies, when they are trying to challenge giants, what they're trying to do is to copy exactly what giants are doing.

But the problem is you are 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.

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

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

First principle, just first principle. I guess you just have to

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 how AI can help our business. And then they will start hiring a big team working on AI without asking how. That's AI. How do you apply AI in your product? And then why? Does your product actually need that?

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. I had a lot of experience in the industry. I knew how a recommendation system looks like in many big companies.

I think it was impossible. It would have been a big, big mistake if we tried to hire a huge team and tried 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 have everything you can in your team to focus on it.

My hot take, I guess, going back to marketing, is

there's a lot of animosity towards AI in the worldright now. And people, 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 going to take jobs away and there's going to 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 end of the day, 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. 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 or 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 going to be proponents of AI. And there's no reason to expect them to be.

Sourcery is sponsored by Brex and they're the performance corporate credit card. So they like to spend smart and 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?

I don't know if I'm locked in. First of all, that was a great native ad. That's your partnership. Thank you. But

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 derivative 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 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.

Yeah, yeah. No, I come to AppLovin as a pretty junior middle-level team members. All those things about executive is pretty new to me, like attending conferences and giving speeches. Honestly, I haven't really thought a lot about that. But I think an important part of 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, my leaders, Adam, have some expectations on me, I just want to do anything I can to make it work.

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

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

Does that become an obstacle sometimes?

It could be, yeah. Sometimes I feel like it becomes an obstacle because I had a hard time to pause and enjoy the achievement we have. I don't know if Adam feels the same thing. There was a lot of excitement in the past because of the success of AppLovin.

But I find myself hard to press the pause button, just enjoy that moment.

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

I think that near the next one. This is actually a common trait I found in really highly competent, successful people. It's 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 the A is not good enough, you got to get the A-plus. 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 that 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.

Are there any other traits you look for in these players?

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, has a higher standard for themselves, gets things done.

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.

And it usually helps with the third thing, which I look for and always ask about, is how they react to adversity. 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 here.

Yeah, this is a kind of like a danger for high performers. Because when you are a high performer, a lot of people around you, they observe you as a high performer. They trust you. They don't question you. So it becomes extremely important for those high performers to be able to question themselves.

And I've seen some people do extremely well in this, but some others might forget to be the one that challenges themselves.

I know I said I already asked a difficult question. And I almost left out without asking this one. But Adam, OK, you built out a gaming advertising company. You're in the gaming space. You know who else was in the gaming space?

OK, we had Torsten, who then went on to found Helsing. We had Palmer Lucky, who went on to found Andoral. What comes after AppLovin for you?

That's a tough question. I'm probably, hopefully, if I'm good enough and earn people's trust around me, going to 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 didn't 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 $0 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.

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.

Yeah, thanks again for having us.

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