The Exchanges, every show

Q Very cool. Help me understand how you've thought about pricing, then we'll get the backstory here. What's the average director of golf paying you for the software?

A So our list price, which we stick very close to at a private club today is about 4200 dollars per year for essentially unlimited use of the product for up to, ah, two 18 hole golf facilities. So if you're a pine hearse with seven different courses, that's all custom priced. But it's actually, as I like to say, every business is price times quantity. P times Q, you learn in economics one. We're relatively low P, A high Q, high quantity. We're in 11,000 courses, so we're pretty, plus we do lots of other things, but it's certainly the most inexpensive software that club will have because they also need software to, you know, do their point of sale, to manage their T-sheet, to do their website, to do member billing, and things like that.

AI assessment note: “about 4200 dollars per year for essentially unlimited use of the product”

Q Very cool. Um, okay, so let's talk a little bit about Tandem PV as a company. Can you say a little bit about sort of the origin story of the company and, uh, and how the company has evolved over the years and when you came on board?

A Yeah, sure. So Tandem PV was started by our co-founders Colin Bailey and Chris Iberschbacher. They're both PhDs in material science and applied physics. Between the two of them, they've over 50 years of experience. Uh, they met at Stanford when Colin was getting his doctorate. Um, and that is where Colin made the world's first perovskite silicon tandem cell. And so people ask us, like, you know, what's your secret? How did you, how did you figure this out? And Colin always says, I don't know what the secret is. I've just been working on it longer than anyone else. So, so when he left Stanford, um, there wasn't Many, no one was really working on perovskites in a commercial way, and so we start, he started Tandem PV. So this June will mark 10 years of the company's existence. Um, and we've been a purely R&D company for about that long. Um, and this is, you know, what the team is doing is really hard. It's incredibly cross-functional, right? So it's chemistry, applied physics, engineering, material science, and it just, deep tech takes a long time. Um, But about three years ago is when we started kind of transitioning into commercialization, so we brought on Scott Wharton, our CEO, um, who, this is his fifth scale-up and startup, um, and then I joined, uh, almost two years ago now to really lead the company into this kind of next chapter of commercialization and scale-up, which is…

AI assessment note: “then I joined, uh, almost two years ago now to really lead the company”

Q Can I ask you, when we think about that enterprise adoption, I think one of the biggest problems that we have is data structures and data cleanliness. Um, I interviewed a guest the other day and they said, we'll have like data cleaner as one of the most important jobs in the next five years. Um, is data structure and data cleanliness the biggest barrier to enterprise adoption?

A Well, I agree in part. I think that certainly the models need to have access to data to perform their jobs effectively, but the caveat is that they'll be able to clean the data themselves fairly effectively as reasoning capabilities go up. The thing that humans will need to contribute to is all of the tacit knowledge within the organization that isn't written down, because I've found that when I try to get agents to do all of these workflows Um, throughout Mercure, there's just an enormous amount of context that lives in people's heads that the agents need to have access to, to perform effectively. Um, and so much of that is going to be the new job of employees of how do we codify all of this knowledge? How do we train agents so that they're able to perform these tasks effectively across every function in the organization?

AI assessment note: “Well, I agree in part. I think that certainly the models need to have access”

Q When it comes to taking the ideas, having your partners go and work with the companies, how do you think about being on the ground in the company alongside the executive team that already exists?

A Part of the design upfront is making sure that everybody, both at the company and then our partners, Have a clear eyed view for how that relationship works. The amount of time that an ethos partner spending at the company is bespoken situation specific. If it's helping somebody redesign a pricing strategy, that's a different amount of time than if it's somebody helping a company figure out how are we going to completely change your debt structure from A SOFR plus six, 25 unit tranche deal that we're paying 10 and a half on into a securitized asset backed loan that we're going to pay a fixed 6.8% and save fifty million dollars a year. That's a complicated thing where you have to completely redesign the legal structure of the company and create bankruptcy remote subsidiaries, backup servicer agreements, go meet with the rating agencies. That's almost a full-time effort. That is something that we did for this company, Identity Digital, where we had three of our partners working a lot of their time just trying to execute that one thing, as opposed to another situation where they might just be meeting monthly to talk about how to change the channel strategy a little bit differently, each situation specific. There's an alchemy to the relationship that is unbelievably subtle and important. If you show up as an ethos partner to a company, are you ethos? Are you the company? Are you goi…

AI assessment note: “the relationship that the ethos partner has with their counterpart at the company is a sacrosanct cone of silence”

Q And now you had done a lot of the foundational work. You had gotten it on the legs of Pamela Anderson, and people were sort of becoming aware of it. But what did they do that turned this into a, an iconic global phenomenon? How did, how do you think they did that?

A After they did the acquisition, they immediately hired a guy Who had come out of a company called L.A. Gear, and he tried to turn the brand into a street cool product. He lasted less than a year, and then they brought in a young lady called Connie Rishwain, who had spent time in a lot of the New York high fashion footwear industry, and she's the one that started advertising in, you know, the, the really, really high-end women's fashion mags. That Teamed up with the fact that we'd sent boots to Oprah, and she ordered pairs for her staff and everything, and if we had announced that Oprah was, was wearing our shoes or boots, it would have killed the company when I owned the company, because I didn't have the capital to build the product to take on that demand. That, in conjunction with Connie Rishwain, who knew New York fashion, those two things coincided to make our, like, Break through the hundred million dollar barrier and the 200,000,500 million.

AI assessment note: “they brought in a young lady called Connie Rishwain, who had spent time”

Q I heard or read a very interesting story of how you met your wife. Would you like to start with that?

A I'm Bolivian. I was at Harvard getting my business school degree, and I decided I wanted to go work outside the US. The US felt too competitive. It's large, but very competitive. And I said, I want a country that's large, I want there not to be a lot of competition, and I want there to be beautiful people, because I'm, um, I wanted to get married. Brazil. Top of the list. So I get a job offer with the number one private equity group in Brazil, the three G guys. They own Anheuser-Busch, they own Burger King, they're an amazing group of people. But they made a condition for my hiring, which is I learned Portuguese. And I said, will you pay for it? And they said, of course. So I call my Brazilian friends and says, find me a beautiful, smart Brazilian PhD or master's students and tell her I will pay her to talk to me.

AI assessment note: “find me a beautiful, smart Brazilian PhD or master's students”

Q Um, and... Can I ask you, we, we, we, we saw Benioff say that he spends three hundred million a year on Anthropic, which equates to about 3.8% of developer salaries on Anthropic. To make it justify the valuations that we're seeing for these companies, it needs to be 20%. Do you have any concern in that movement from 3.8% to 20%?

A No. I mean, I, I think if you look at I've never done this in any detail, but if you look at what we pay hardware engineers, and you look at what the tools, which we, the EDA tools they use, I bet you're much closer to 15 or 20% than two or three percent. What's happened is historically software engineers use very low-cost tools, and hardware engineers used extremely expensive EDA tools. And so, that's interesting, isn't it? I mean, I, I, I think we, the cost of bugs And hardware is so high that we became accustomed to using many expensive tools. And in software, we threw people at the problem rather than tools. And as AI becomes more productive, I certainly don't see a problem where software engineers using 50 or a 100,000 a year each in tokens. There are forty-seven million software engineers in the world. I mean, that's five trillion dollars just in software engineering token use.

AI assessment note: “No. I mean, I, I think if you look at what we pay hardware engineers”

Q Again, going back to my idea of what the future looks like, what should one expect from that? Does it ease? Does it ease over time? What happens to the cost?

A Well, I, I think the, the challenge here is that, that these are extremely, uh, lumpy. Items, right? You can't just add a little bit of manufacturing capacity at a fab. You have to build a fab for forty billion dollars, and it takes five years to build. So if you see demand explode, you cannot respond quickly. All you can do is fill your factory. Once your factory is filled, you've got to build another factory, right? It's a step function in your ability to meet that demand. And the step is huge and takes years, and so if demand stays high, uh, they are, we're going to continue to see memory shortages for at least the next several years.

AI assessment note: “we're going to continue to see memory shortages for at least the next several years”

Q What goes into shipping a mainline model like this? Like, what's the behind the scenes?

A It's complex. The Gemma team is actually relatively small. We have, like, two or three PMs. We have one marketing person, and then there are, like, engineers and researchers working on shipping this. Of course, there's, like, default training part. How do we do the post-training, distillation, post-training techniques, and so on. What is quite exciting is that once we have the model, then we collaborate with a bunch of open source partners, right? So for example, we work with Lama CPP, Olama, MLX, Hogan Faces, BLM, NVIDIA, AMD. So we have almost 50 external partners for every, well, for the Gemma for launch, which has been the most complex launch. And also internally, we collaborate with a bunch of different teams. So think of Google Cloud, Vertex, Vertex Models as a Service, ADK, uh, and then Android as well, right? So we work, for example, with the Android team, and with the launch of Gemma IV, we released an integration with Android Studio. So in Android Studio, there is this agent mode where you can have a model helping you buy code and do things within Android Studio. And they should say integration with offline models using Lama CPP or BLM or any OpenAI compatible endpoint. So now you can use Gemma IV to also buy code Android applications in Android Studio.

AI assessment note: “The Gemma team is actually relatively small. We have, like, two or three PMs.”

Q Okay, so let's talk about the main reason why I wanted to bring you on this channel is that you built a boring or unsexy business in a boring and unsexy niche Taxation. Taxes. So let's talk about this boring business. Why do you think that this business specifically, this boring business worked and became a million dollar business?

A I think it works for, All competition for real demand. In unsexy markets, you often have fewer talented founders chasing very real problems. And the ratio of opportunity to competition is way better. Second, extremely high pay. Taxes, compliance, domiciles, people don't want to deal with them. They just want to hire somebody to pay somebody just to forget about this. Which is exactly why they're willing to pay. Third, the value is concrete and quantifiable. In a lot of startup categories, you're selling a brighter future or some vague promise. In Texas, you can say, here is a likely financial benefit, and here is what our service costs. It makes the customer decision rational and straightforward. So you need to sell less. Fourth, it matched our strengths as a team. So this wasn't just an unsexy market with opportunity. It was a market where our strengths were unusually complementary. That's why I think it worked.

AI assessment note: “In unsexy markets, you often have fewer talented founders chasing very real problems.”

Q in that this market would be really important, and you more than others, right, since you actually started a company in it. But then it took some time for the market to really expand to the point where, uh, to your point now, it's, it's this massive use case. People really care about speed of inference and other things. Um, what gave you the conviction back then to do this?

A Combination of, of vision, um, the right co-founders, and a little bit of arrogance, a little bit of luck. You know, we, we saw AI on the horizon as a new workload. And as computer architects, new workloads are opportunity, right? It's very, very hard to, to, to enter in the x-eighty-six world, right? Where there's not, nothing new is happening there, and nothing has happened for generations. But You know, when graphics emerged, you got the discrete GPU and you, you, you got, uh, Nvidia and, and when, uh, when the mobile, uh, compute hit, you, you got arm. And it was interesting that, that not Intel, not AMD, not all sorts of people who you would have thought have been really well positioned to win in that business. They all got no share. And so we knew that, that this new workload would eat a lot of compute. It would require Uh, a new architecture, dedicated architecture, and that ought to be very different. The architecture could not be a derivative of what's existing. Those were our big bets, and they were a hundred percent contrarian, and they turned out to be dead right.

AI assessment note: “Combination of, of vision, um, the right co-founders, and a little bit of arrogance”

Q understanding is that there are various different versions of that. They did it at the, at the reactor level, but not, uh, not a plug break-even, or whatever other term you want to use. So like, or orient me, what, what did that Actually show, and then what's different about that from what we're going to have to show when we want to turn fusion power plants into something real?

A So what they did, and you're gonna have to forgive me if I, if I get some of the details wrong, I'm not a physicist, I'm an operator in the field, so disclaimer. Um, what they did was they stored about 300 megajoules of energy in a capacitor bank. They used a laser to drive about two of those megajoules into a target, and then they got five megajoules of energy out of the target. So really, really big achievement. They've since improved on an achievement, gone closer to eight megajoules out of the target. And so if you draw your, your proverbial box around that fusion target, then you got more energy out of the target than you drove into the target. But to your point, if you draw your box around the entire fusion machine, including the capacitor bank, you only got, you know, percent and a half or so of the energy stored in the system out of the, out of the machine. And obviously that's not a practical basis for a power plant. You need to get about five X more out of the machine than was stored in the system to have a practical basis for a power plant. And so, to your question about milestones, that is the next big milestone for this field. It's demonstrating something called net facility gain, which is getting more energy out of the entire machine than everything required to run the machine. We like to define it as all of the energy stored in the system. Um, and a number of com…

AI assessment note: “demonstrating something called net facility gain, which is getting more energy out of the entire machine”

Q you mean? Obviously, if we're talking about fission world, there's like a, such a broad spectrum. If people talk about modular, they could be talking about a microreactor that's a megawatt, or they could be talking about, like, versions of SMRs they call modular, uh, and that's literally in the name SMR, but it's still 300 megawatts plus. So, like, what's a, what's a pixel size for modularity for you?

A I just mean big things that themselves consist of many small things. So tabletop scale fusion doesn't work, but our goal is to build fusion power plants in the couple hundred megawatt range, so in the two to 300 megawatt range, that themselves consist of modular mass-manufacturable building blocks. Um, and so in our case, the, most of the capital cost and footprint sits in the driver. This is true for a lot of different fusion approaches. For us, that driver consists of a 156 identical modules. Each of those modules produces more than a terawatt of peak power and sits in about the footprint of a shipping container and is made from oil, plastic, metal, and water. So we bring two things that make a big difference, right? The first is an established scientific foundation based on decades of work at the national laboratories and the breakthroughs that I mentioned at the beginning of this conversation, but also a path to a modular, maintainable, deployable system that can scale more readily as a power source.

AI assessment note: “156 identical modules. Each of those modules produces more than a terawatt”

Q Yeah, I totally get that. When we go back to the alleys and the micro ones for the fundraise, What do you advise founders when they get multiple term sheets and the heat is on?

A There's kind of two groups of firms. There's like the kingmaker firms that you should accept at any price. Um, one example would be Founders Fund or Sequoia, uh, or people like that. Um, and so if you get an offer from them at X and the offer from, um, someone else is at two X, you should take their offer because even if you're maximizing, um, amount raised and minimizing dilution, in the long run, Um, it, it, you're king made, and it will save you in the next round. Um, so you should definitely go for, but the, the, another mistake these entrepreneurs make is they have the list of the kingmaker firms as too big. Um, there, there's probably like some tier two firm that think very highly of themselves that aren't in that same level as, as Founders Fund or Sequoia.

AI assessment note: “There's like the kingmaker firms that you should accept at any price.”

Q What specifically were you criticizing Gladwell about? What, what, what was the substance of what you were saying he was wrong about?

A Yeah. In my first book, I was criticizing the research underlying the so-called 10,000 hours rule, and then his popularization of it. The idea that the only route to exceptional performance is through 10,000 hours, so-called deliberate practice, highly technical, effortful practice. Um, and we, that brought us together for a debate with specifically about sports development at this thing called the MIT Sloan Sports Analytics Conference. And there I said, you know, The data that actually track, yes, elite athletes spend more time in deliberate practice than lower level athletes, but the studies that track them over the course of their development show that early on they spent less time In deliberate practice in that activity than peers who plateau at lower levels. They have what scientists call a sampling period. Broad variety of activities where you learn general skills, now called physical literacy. You learn about your own interests and abilities, and delay specializing until later than peers who plateau at lower levels. And so, he acknowledged when we were coming off stage, he said, that does not fit. With, with what I thought, why don't we run together? We were in Boston when we're back in New York tomorrow, because we'd both been national level middle distance runners, and we'll talk about it on our own time. And so we started running together every weekend, and we came to…

AI assessment note: “I was criticizing the research underlying the so-called 10,000 hours rule”

Q Is this set up as a special economic zone in the Philippines or can you tell us more about the details beyond, um, sort of the, the legal side that you mentioned?

A Yeah, absolutely. So, um, it, so right now the, there are two phases to the plan. Um, the first phase is, uh, the State Department taking into custody the, the zone. Uh, we, we are, we are referring to it as an economic security zone because, uh, it is a very unique type of arrangement. The State Department has authorities to take in, um, Land and property into custody, sort of how foreign governments gift the State Department counselors and councilates and embassies. It's very unique to, uh, do a gift of 4000 acres, but fortunately there's no statutory limits on how big or small property can be. And so that's phase one. So right now it's actually diplomatic property, um, that is effectively, you know, uh, governed by the same laws as, as our embassies are. Um, phase two will be the long-term development and build out of the land, and so we are gonna spend, ah, we have two years, a two-year window to negotiate the details with our, ah, Filipino counterparts on the investor protections that will apply to the land, the tax, taxation regimes, and, ah, and all of the different, you know, legal safeguards that investors will be able to benefit from, from for the long term, and the goal is within that two-year window, To actually have a long-term framework that will be multi, um, multiple decades.

AI assessment note: “we are referring to it as an economic security zone because, uh, it is”

Q Is it, is it in that case because they have insufficient supply of gas to feed those, those projects, or is it that gas prices are too high and so they'd be losing money on producing more?

A It's gas prices too high. Uh, when you look at the economics of it, it just doesn't make sense with where global nitrogen values are. And I also think there's the political aspect of it, right? The European political engine has been really charging towards green energy and Frankly, this old school, old technology nitrogen production, from their viewpoint, is very dirty. It's outdated. We don't want to have anything to do with it. So you've already got bad economics for the plan, and then you've got a political outlook that basically sits there and says, don't put any money in these plans because you're probably not long for this world. We're not going to support it. We're doing everything we can to shut you down.

AI assessment note: “It's gas prices too high. Uh, when you look at the economics of it”

Q Really interesting. Okay. Let's do a couple of these examples here. HubSpot, VSP, just to be clear, are these real, these are real paying customers, real examples?

A They are real enterprise paying customers with logos that we are allowed to talk about. So these are not, uh, experimental budgets. Most of these customers are paying the cost of a human, um, plus and have multiple like suit, like HubSpot, for example, we are working on, I believe it is their fourth superhuman, um, to go live across their journey. Fiona, the one you're seeing here, was the first superhuman that we built for them. It's in the top left corner. Fiona is set to talk to their SMB business, so when somebody asks for a demo, they engage with Fiona, she gives the demo, she qualifies, she takes them through to close, and they were able to increase their revenue by 25% in their SMB small business segment.

AI assessment note: “They are real enterprise paying customers with logos that we are allowed to talk about.”

Q Thank you. It probably makes sense to start with a little bit of a background on each of your seats and how the stock side platform fits into that. Derek, why don't you kick it off?

A Here at the state of Wisconsin, we run about half of our assets internally and then half of our assets externally. I head up the team where we allocate capital to external managers in the public space. We're trying to find managers where we can't manage those assets with either the team or the strategy or the region of the world internally. A lot more of the hedge fund strategies, a lot more of the emerging markets. How Dockside comes into the whole fray is It's a platform for us to be able to be a lot more dynamic about our allocations, to be able to target our risk a lot better, access managers that we might not have been able to access through a traditional GP relationship.

AI assessment note: “How Dockside comes into the whole fray is It's a platform for us”

Q While you were in college, um, were you also working at the store?

A I was, yes, yes. I, I, I went to University of Minnesota, and I actually, um, I don't have a degree. I rejected taking a lot of the required courses. I took business courses, design, uh, Art-related courses. I knew I was heading into the business, and, um, it was a combination of I was being groomed, and I think my dad had worked really hard his entire life. He had, uh, purchased a home in Florida. He'd fallen in love with golf, and basically for six months of the year, he was gone, and so as I started working First as a salesperson, then picking up different management roles. Um, in a fairly short period of time, I was running the business.

AI assessment note: “I was, yes, yes. I, I, I went to University of Minnesota”

Q All right, so you decide to give up on these other businesses, the, the kids furniture, and the, I guess the design studio, and, and really focus on room and board, and And I guess one of the things you do is move your store in Edina into a much bigger space, right?

A Absolutely. And it, the decision seemed pretty easy. We moved room and board into this much larger building, and part of what we did is we, we put room and board in, and then we created another department, if you will, which focused on Better quality. American made. At a considerably higher price. And I think the shock that hit us is our room and board customers, once they saw that, They bought it. They bought it readily and easily, and it just opened our eyes to the fact that this customer we were trying to appeal to, you know, out of college, not a lot of income, uh, they don't mind having the IKEA-like product. Well, now it's 10 years later, and they're established in their job, and they want more, not temporary furniture, they want permanent furniture, and they saw what we had, which was really Quite beautiful, American-made, and it sold way more than we expected.

AI assessment note: “Absolutely. And it, the decision seemed pretty easy. We moved room and board”

Q Isn't consistency not important? And what I mean by consistency is that consistency of personality?

A Not at all, because you're marketing for different demographics. So for example, you find this interesting. We test almost a thousand data generated ads a day right now, on top of the roughly 8000 Organic creatives we make with humans every single month, and one of the things we do is we risk reskin. So my sister Geffen leads our ads team, and we have a bunch of great ads with Geffen, and then we'll turn her into a sixty-year-old lady, and we'll turn her into a forty-year-old man, and then we'll turn her into someone who's African-American or someone who's Asian, and we'll change the background from a coffee shop to a library to, uh, like, uh, I was gonna say farm, but farm wouldn't convert for our users. And so you just test all the variations. And so I know that for us, Highest converting demographics would be people in their thirties and forties in terms of paid, but we have like crazy usage in the 18 to twenty-eight-year-old market. Um, but people who are 60 and above need reading glasses, so they use Speechify a ton. And now we're launching voice agents at Symbavoices.ai, and so that's a different demographic. And so all the initial ads for Speechify were me. It was my face. Um, and I would sit in my house, especially Saturday or Sunday, with my phone, and I'd record. I'd be like, As a medical student, I use Speechify to read while I'm working out. As a lawyer, I love work…

AI assessment note: “Not at all, because you're marketing for different demographics.”

Q You know, you're the CTO of Palantir. You, you've made, you know, infinite money at this point, and, you know, you're a husband and a father, yet last year you joined the U.S. Army Reserves. Why'd you do that?

A In part, to honor dad's sacrifice. Like, this country has given us more than we deserve to have, and I think, uh, a sign of a, of a functioning society is that those who succeed in it are willing to invest their time, energy, money, resources, attention, and, and give back. Uh, I think that the micro reasons are like, this is the example I want to set for my children. Um, I want to make sure that they have the same gratitude as maybe a generation removed from the visceral violence that we escaped. Uh, they should have the same gratitude that, that I had, that dad imparted in us. Um, and I think, I'm not sure I had A lot of differentiated skills to give the Army at 24, but I think at 44, uh, I've learned a thing or two along the way that I think can hopefully help the Army go faster.

AI assessment note: “In part, to honor dad's sacrifice.”

Q 30. Everyone's looking to invest in the market, trade the market, start a business, Building something from the bottom up. So we are speaking to them. So most of today is around that. Can we start with a little bit about you and how your own journey began from Queens to Wall Street? Maybe like a, how do you describe your story in your words? Your story, your origin story.

A Well, um, you know, I was born in Queens, New York, as you know, one of the, one of what are called the outer boroughs, uh, middle-class upbringing. Uh, neither parent, uh, went to college. Um, but my father was a very intelligent man and, and, and an accountant. And I think he did His job well. And so we lived a, uh, comfortable middle-class, uh, existence. Um, I went to the public schools of New York at a time when you could get a good education in the public schools of New York. Uh, and I think I got one. Uh, and, uh, oddly enough, uh, uh, Ended up taking courses in business law and then accounting in high school and really connected with accounting with the orderliness and the symmetry. Uh, it, it just clicked for me. So I decided to go to a business school. I applied to Wharton as the best business school for undergraduates in America. Uh, I was told I wouldn't get in, but I did. Uh, went to Wharton as an accounting student, switched my major to finance. Uh, uh, then went on to get a, an MBA in accounting from the University of Chicago business school. Uh, and, uh, between years of business school, I had a job in the investment research department of Citibank. Liked it, went back. That's my, that's my background.

AI assessment note: “I was born in Queens, New York... went to Wharton... That's my background.”

Q sort of build this business plan. She goes off, and we know what happens with the Honest Company. Um, I mean, you're going to get into cosmetics, into beauty products, but, but, but before we hear the story, how did it, how did the idea even start to come to you? When did you start to think about, um, Beauty products and makeup and cosmetics and then you making it.

A I had become impassioned with the environmental health movement, and as I made changes to my life, I found that there were opportunities in the marketplace. You know, I, it was really easy to switch my household cleaning products or just start washing my floors with water and vinegar. It was easy over time to switch from plastic to glass. It was easy to take my shoes off at the door. There were solutions out there in certain aspects of my life. But when it came to skincare and color cosmetic products at the time, there were some really great sort of all natural types of brands, but most of them Didn't really work very well, or they didn't smell very good, or they weren't presented in a way that was aesthetically pleasing to me. Then that was how I ultimately came to decide to enter into the beauty space. I had been introduced to Christy Coleman, who was a leading makeup artist during my tenure with Jessica, as I was exploring a whole bunch of different things, and I, she and I really clicked. She was the first leading makeup artist to Clean up her kit and start working with what was then called green products. She and I just started talking about what her dreams were in terms of, you know, creating safer makeup and my dreams about really taking on this, this industry. And we, we started having a lot of conversations.

AI assessment note: “that was how I ultimately came to decide to enter into the beauty space.”

Q We are seeing a deluge of SBC stock-based compensation, uh, at a level that we almost haven't ever seen before, I don't think, in corporate history. Um, how do you feel and think about that?

A So, we, we've given roughly the same amount of stock, um, every year in terms of absolute amount. It's roughly three hundred million dollars. And so if you think about our market cap, I think our market cap's about a hundred and fifty billion dollars. Our burn on stock based comp is very, very low. And so you can judge us on cashflow minus SBC, which I generally think is the right way to, to judge companies. What's happened in tech though, is that there's been an expectation that stock based comp will be high at companies. And as stock prices have gone down, especially in software companies of late, you have a downward spiral that's formed where all of a sudden a company that was burning three percent Of, of their cap table every single year to pay out equity to the team falls 66%, and now you're at 10%, and you're at a level of dilution that's incredibly hard to come out from underneath, and so it makes it hard to bet on those companies when they're burning that much equity. What I found in, in what we did, implemented in, in 22 when we fell a lot, is that certain people have enough compensation to not take risk on the stock if the stock's gonna be volatile. And we used to believe that every single person should have equity granted by the company. Instead, we went to a place where we said the top 10 to 15% of the company will get equity, and the rest won't. They'll have the ri…

AI assessment note: “judge us on cashflow minus SBC, which I generally think is the right way”

Q You said multiple times about it's very easy to have massive spend on the LLMs and just on the AI slot being created. How did you think about the decision whether to invest in your own model as Harvey did, as Kursa did, TBD on how that goes? We'll see. Versus existing frontier models.

A Yeah, I mean, look, we're not an interface on top of large language models. There's usage of large language models in the company for productivity. There's some usage of large language models in our core business as well. But a recommendation system model is something that drives engagement. What you see on content on a social network is something that drives most advertising products in the world today. Facebook's ad system, TikTok's ad system, ours. And so this is a space of machine learning that really hit its stride about a decade ago, and I would say really accelerated with some of the research that we've seen come out of the large language model space lately, but it's a space where you can't just go defer to the large language model and say, hey, based on what you know about this user and the data I have available, what's the next ad to see? That wouldn't work as well as a custom model built for this purpose. In a world where you get to a place where you're in a category Where you're utilizing the large language model, or you're building an interface on top, you better build a moat really, really fast, given how exceptionally talented companies like Anthropic are about releasing product on top of their own models.

AI assessment note: “That wouldn't work as well as a custom model built for this purpose.”

Q We mentioned TikTok and we mentioned Meta there. For AppLovin, currently valued at circa a hundred and fifty billion market cap, whatever it is precisely, but give or take. For AppLovin to be a trillion dollar company, do you have to be a social network as well?

A Um, no, I think, look, if you think about, like, what, what creates a trillion dollar business, and I sort of said cash flow minus SBC before is a real, real important metric, right? Like, if we ever got to generating 30, thirty five billion dollars a cashier, it would probably be a trillion dollar business, right? So you think about what can get us to that point, and so there, there's a couple things that can get us there. One is continued execution in the domain that we're in. We think we can get much bigger Just to better monetizing the gaming audience. It's a billion plus daily active users who play these games. Adult audience, a lot of heads of household. The next thing you think about is how do you expand what you have? So in the past I've talked about connected TV is one of the holy grails of advertising. If you can port the performance ad we serve on mobile to the television and allow small and medium-sized businesses to serve there and make it all performance-based, That's a really big unlock. So it's something we still, we still take seriously. Then you think about what are other applications of the technology? We're really good at advertising model. We have yet to have a chance to have our, our team work on an engagement model. So a social network for us is not a requirement to get to a trillion dollars. It's an interesting play to recruit talent and continue to tune…

AI assessment note: “a social network for us is not a requirement to get to a trillion dollars”

Q How have you systematized and organized the 70 people underneath that leadership?

A It's no different than a lot of firms, where we have an investment team. Jordan helps oversee that. We have a number of deal quarterbacks on the investment team who report into us and have a VP. Those are principal and partner level, and they have a VP, a senior associate, and often an associate on deal teams. They then report into me and Matt, who are the investment committee. Will oversees our CFO and our back office activities. We have a business development five person team that's done an incredible job helping source opportunities and give me and Matt leverage where we used to have to do every first meeting. Now with a business development team, every first meeting with founders and our business development team is able to not only handle the first call, but actually handle the first meeting and then help decide whether Matt and I should Fly out and spend time in person with the founders, which is a big part of our program. So investment team, business development team, and then all the back office activities of the firm.

AI assessment note: “we have an investment team... Will oversees our CFO... We have a business development five person team”

Q I want to ask you about capital allocation. You're buying businesses, there's a financing component. What do you see as the most important levers of capital allocation in the success of one of these businesses?

A It's two things. One is, what is the pipeline and opportunity set for inorganic growth? We are actively avoiding categories where the bolt-ons are trading outside of our price range. There are categories today that people are having success rolling up, whether they be Resi HVAC or pest control, or in a prior cycle, perhaps VET, where the platforms trade at big prices, but the bolt-ons also trade at big prices. You have relatively small bolt-ons trading at maybe eight to 11 times cash flow. For us, that is fundamentally less interesting than similar end markets where the platforms are trading at 12 to 15 times, but the bolt-ons, because of micro market risk or just lack of private equity heat, are trading at, let's call it, five to eight times. To us, those are more interesting opportunities. A lot of the time we spend in diligence on a category and on the initial purchase within that category is spent on building out the pipeline so we can think about within how much confidence interval range do we have that we can get the next 3040, fifty million to work at an unlevered low to mid teens return, and then with a dollop of leverage once the business is ready for it, now without even getting into organic growth, you're up into the high teens or low twenties. The second capital allocation decision that is critical to us is where are the pockets of technology implementation and inve…

AI assessment note: “It's two things. One is, what is the pipeline and opportunity set for inorganic growth?”