The Exchanges

Q Final one for you. What are you most excited by?

A We talked about Jeff Dean. I'm, Jeff Dean? Yeah. Uh, I'm most excited by applications of AI. To pharmacology and biology. So, uh, I have a family member who has very severe autoimmune neural inflammation. He's had it for six years. I took a blood sample from him every week for 15 weeks, sent it to a lab, sequenced his genome, uh, did proteomics on it to figure out how the proteins are expressing in his body, and run an RNA analysis in each one of those weeks, and then I compared that to self-reporting data on what the quality of life is and what his mood effect was, Uh, every day I have, like, six years worth of data on him, and ran it on a GPU cluster, and, like, I found so many things that no doctor could ever tell me, and he has a very rare disease. It was called an, like, an orphan disease, because there's not that many people. There's a Facebook group for this disease. I'm buying now, uh, basically, like, you know, it's like a 5000 dollar device you can fit in your pocket, but if you put a piece of hair or saliva or blood into it, it can sequence your entire genome, And so I'm organizing meetups with all the people who have this disease to sequence all of their genomes and then compare them all on a gigantic GPU cluster to figure out what epigenetic common thread there is between them. And I am, I know I'm going to solve this disease. I would have never had an edge to do t…

AI assessment note: “I'm most excited by applications of AI. To pharmacology and biology.”

Q What was your biggest lesson from working with him? If there was one takeaway?

A That you can overestimate what you can achieve in one year and underestimate what you can achieve in five. When I started, it was a three-year-old startup, and it was basically a glorified contact manager, you know, Salesforce automation. Um, we essentially said what you're traditionally using ACT or Goldmine or using a spreadsheet, you can use Salesforce for. But Mark had this bigger vision and he said, we're going after Siebel, SAP, Oracle, Microsoft. We didn't have the product set to go after those competitors, but he was such an incredible marketer that he created this perception in the industry that some of the largest companies in the world could adopt this technology and that we would deliver on a roadmap that would satisfy the requirements over time. And he did.

AI assessment note: “That you can overestimate what you can achieve in one year and underestimate”

Q Why football? Why English Premier League? You can sponsor F-One, you can sponsor, you know, LaGuardia Golf as well. Why football?

A Well, I love the sport, but let's put that to the side for a minute. I think the value of these sponsorships obviously come in two forms. The first is awareness. And as we saw last night against Chelsea, this is a game that's being televised globally. So you've got millions of viewers that are looking at your brand and you're getting impressions, obviously. The second, which is obviously easier to quantify, is hospitality. And we're sitting here at Craven Cottage along the Thames. I think this is arguably the best sports experience in the world, and I've been to many. We had 20 executives last night attend an intimate Michelin grade dinner. We had, you know, the C-level executive come from Paris, one of the largest banks in Europe, just to experience that. And those types of relationships are extremely important, especially as we move up market.

AI assessment note: “I think the value of these sponsorships obviously come in two forms.”

Q they're about tenders. We see them more and more for employees, and I think talent acquisition is one of the hardest things today. I actually got in a lot of trouble the other day for this. I said, if you are trying to hire A-star talent today, you can't. OpenAI and Anthropics simply pay, and they go to the front-end model providers. Is that true, or was I being glib?

A I think it's true depending on the category that you're in. I think if you're a Digital native AI startup in San Francisco. It's a very difficult employment environment because you're competing against OpenAI and Anthropic and others, um, that are extremely well capitalized and are putting offers that are extraordinarily aggressive into the market. I think if you're a infrastructure provider like ClickHouse, uh, we look for a slightly different, um, profile. You know, we're looking for database engineers, people that have experience with distributed systems. Um, slightly different than what the frontier labs are hiring for. We employ people in 27 different countries, um, which gives us a competitive advantage, so I can hire engineers in Portugal and Germany and Singapore. Um, you know, we've got single digit attrition, so we've got extraordinarily high retention. Um, we have done some structured secondaries, um, and we'll continue to do so over time, but not with the frequency that I think some of the younger companies are doing.

AI assessment note: “I think it's true depending on the category that you're in.”

Q If you could have your way, would you not have everyone be in office in some way?

A I, I wouldn't, and I'll explain why. Uh, we're a very international company, uh, by almost every measure. Over half of our revenue comes from outside of the US. 40% here in EMEA, 10% in Asia. Over half of our customers are outside of North America. And so we need to support our customers in a variety of different languages, in a variety of different time zones. I mentioned we're live in 36 different regions around the world across all three hyperscalers. There's no way that you can centrally manage that. From one location. You need to have people in every single time zone. You need to have relationships with the hyperscalers in region. We go to market with AWS. We go to market with Google Cloud. We go to market with Azure. I flew to China to launch a partnership with Alibaba. Like you're going to have, you're going to need local language speakers to maintain those partnerships. And you can't do it from one or two or three centralized hubs.

AI assessment note: “I, I wouldn't, and I'll explain why.”

Q What am I missing? Is that, what should I know that I'm not getting? How should I think about that?

A No, I mean, I think that is fundamentally the risk for an investor is that you're making a two trillion dollar bet on one of the most competitive application markets in one of the most finicky segments of the market, which is, you know, dev tools. And so I do think that part of what needs to happen in order to make these Uh, companies like Anthropica and OpenAI realize the value is they either A, which they're pursuing, have to go through regulatory capture, in which case they go and they tell, they sort of scare politicians into thinking that they must own the means of intelligence, and thus they become the only providers of the most frontier capabilities, or B, they have to figure out a way to build applications and outcomes That match the true Pareto frontier of cost and quality. And I think that means opening up to more models. So it's actually like sort of at odds with the two strategies. You either, you know, capture it and keep the model or open up to everybody. This is a very hard decision and one that I think you can start to see OpenAI actually grappling with as they've let more models into their harness. They're not making it official, but they're Clearly supporting an open model ecosystem in a more direct way.

AI assessment note: “I think that is fundamentally the risk for an investor”

Q lot of guests on the show before have made Kind of bold statements that, like, 70, 80% of the neolabs that we have today will die in a given time period, three to five years, whatever you want to choose. Um, do you think that's true, and how would you advise me and other investors on the model or the neolabs that will thrive versus die in this next wave?

A I think it's plausible it's even more. I think it could be 80 to 90% of Neolabs die in the next 18 months. And die is going to be a funny word to use because it'll probably be for a lot of them incredible outcomes. So I don't know if it's necessarily doom and gloom as much as it's these businesses may not make sense as independent businesses. And so a lot of what I think matters for a Neolab is you should ask questions like one, Is this business attached to a durable workflow? Two, is that workflow going to change if new frontier models get better? And three, if this workflow were to be introduced to a new business, then would that new business figure out something even better? And so basically, is it durable to, like, effectively an entirely new way of thinking or a new way of working? If all three of those are true, legal is a great place where I think, one, New models won't necessarily get better without access to the data. Two, it's obviously a very proprietary workflow. And three, we're still gonna have legal system in five, 10, 20 years. So, probably all the neolabs focused on legal are gonna have great outcomes. Versus, I would argue that there's some places like, um, a lot of knowledge work that's related to intermediate tasks, like people operating in Excel and JIRA. That's just not gonna be differentiated. The workflows are very common. And I think that we may not use…

AI assessment note: “I think it could be 80 to 90% of Neolabs die in the next 18 months.”

Q If I'm the founder of a company, an early-stage company, do I just accept that I'm gonna have B-tier or C-tier AI talent? And I don't mean that denigratively or rudely or horribly, but they're an anthropic and open AI. I mean, Google can't freaking keep.

A I think it's the wrong, um, framing, because if you think about it, I mean, look, when I was, you know, when you were building a software company in the age of the PC, You had four-tier chip talent because you weren't building a chip, right? The point is, if you're an AI company and you feel the need to build a frontier model, then yes, you've put yourself in direct competition with someone, and if you don't have the good people, you're toast. So, what you got to do is make the model a compliment and have A-tier talent at UI, A-tier talent at, you know, AI implementation, A-tier talent at the things that you have your competitive advantage in.

AI assessment note: “I think it's the wrong framing... make the model a compliment”

Q Well, that was incredibly succinct. Thank you. Normally people take about four hours after I ask for a succinct description. When you look at the models that you have on ARENA, the sheer number of them, Bluntly, I just am faced with the one question. Holy shit. Is this like the true commoditization of models? Are they just a complete utility layer at this point?

A Well, I think that there's, ah, the, the big question around this has started to rise because of open source models. So I think if you were to only look at the closed source models, you would say there's acceleration, but it hasn't quite commoditized yet because that layer is still owned by a pretty small group of companies. It would be an oligopoly if we only had the closed source models. But what seems to be happening is that the open source models, especially from China, Have really rapidly improved. And for the first time ever, we saw a couple of weeks ago that Kimi K three actually beat the best closed source American models, uh, on a, you know, pretty important subset of tasks, for example, front end, front end coding, like web development, which a huge fraction of developers are web developers.

AI assessment note: “if you were to only look at the closed source models... hasn't quite commoditized yet”

Q When you look at that dispersion, what do you think is inaccurate? You said you don't really believe the ninety-ten. What, what do you believe a more accurate representation is?

A Well, in our, uh, apex, uh, benchmarks, we're getting closer to around, uh, 50%, um, of long horizon workflows. Um, top models are scoring around, around, around that much, but I think that, um, the percentage for, there's a, there's a class of workflows that are just sufficiency based where you do it and it's done and you're, you're good. This is something like updating a CRM. Um, you couldn't really get much better at it. And then there's a class of workflows that we shouldn't even be thinking about in terms of, you know, binary, like, can the models do it or not? Um, and these can be things like legal arguments or, Uh, to an extent, medical advice where you could always get better. Um, and in those cases, I think that the percentage framing is, is just totally off, and we need to be thinking more about continuous uncapped rewards.

AI assessment note: “we're getting closer to around, uh, 50%, um, of long horizon workflows.”

Q Would you be where you are today though, if that round had come together?

A No, no. Cause I don't think we would have pivoted as hard into COVID when COVID hit. Um, I mean, it would have probably been easier cause we had, at that point we actually had a lab license. So You know, in the US you need this thing called a clear license to run these kind of tests. And we had got one of these licenses over like a painstaking two year process. And then in December of 2019, as part of the wind down, I sold that license to a company in San Diego for 150,000 dollars to pay some of the creditors. And then five months later, acquired a company in Southern California to get the same license for twenty seven million. So timing is everything.

AI assessment note: “No, no. Cause I don't think we would have pivoted as hard into COVID”

Q I didn't, I used to run a lot when I was young, and my needs were wonderful, and I used to love how I built this, because they would ask the questions like this, which is like, how do you go from like cows and musculature on cows and milk yield optimization to at-home STD testing? Like, it doesn't feel that natural a jump.

A Yeah, on the back end, it's more natural, right? All of these things have DNA in them, and so if you're, if you're looking to do better DNA testing, you're just looking for markets where people care more about that, and anything human, people obviously care a lot more about, are more willing to pay for, and are much larger markets, and so we sort of did like a market-first approach of, you know, where, where could there be interesting things, and we narrowed in on Uh, antibiotic resistance in STDs as being like a particularly interesting area where they're getting harder and harder to treat because you get more and more antibiotic resistance, and if you're doing the DNA testing, you can predict what the best drug is going to be early, treat with that drug, and then you're not using the most aggressive antibiotics.

AI assessment note: “All of these things have DNA in them, and so if you're looking to do better DNA testing”

Q Would you be where you are today though, if that round had come together?

A No, no. Cause I don't think we would have pivoted as hard into COVID when COVID hit. Um, I mean, it would have probably been easier cause we had, at that point we actually had a lab license. So You know, in the US you need this thing called a clear license to run these kind of tests. And we had got one of these licenses over like a painstaking two year process. And then in December of 2019, as part of the wind down, I sold that license to a company in San Diego for 150,000 dollars to pay some of the creditors. And then five months later, acquired a company in Southern California to get the same license for twenty seven million. So timing is everything.

AI assessment note: “No, no. Cause I don't think we would have pivoted as hard into COVID”

Q It was. I didn't, I used to run a lot when I was young, and my needs were wonderful, and I used to love how I built this, because they would ask the questions like this, which is like, how do you go from, like, cows and musculature on cows and milk yield optimization to at-home STD testing? Like, it doesn't feel that natural a jump.

A Yeah, on the back end, it's more natural. Right. All of these things have DNA in them, and so if you're, if you're looking to do better DNA testing, you're just looking for markets where people care more about that, and anything human, people obviously care a lot more about, are more willing to pay for, and are much larger markets, and so we sort of did like a market-first approach of, you know, where could there be interesting things, and we narrowed in on antibiotic resistance in STDs as being like a particularly interesting area where They're getting harder and harder to treat because you get more and more antibiotic resistance, and if you're doing the DNA testing, you can predict what the best drug is going to be early, treat with that drug, and then you're not using the most aggressive antibiotics.

AI assessment note: “All of these things have DNA in them, and so if you're looking to do better DNA testing”

Q How do you think about investing in energy, given the capex intense nature of it? You know, we're investing in a company called Fuse Energy. Which I think is incredible. I'm so happy to be, but it, it, it's a CapEx intensive business in terms of like the energy space.

A It is, but I think there's also a lot of innovation happening, and where there's innovation, you can, you can find early teams that are doing science experiments before anyone else is thinking about them. USV a few years ago, uh, invested in a great company called Radiant, which is building small nuclear reactors that literally come off of a factory line. You know, and it's going to be one of the first companies in the world to test, um, in the dome in the United States, um, for, for nuclear energy. And so I think there are a lot of interesting models and ideas happening on the edge of, of innovation. And I think those are the best places to bet as a venture capitalist, because in the earliest days, they're actually not that, um, capital intensive.

AI assessment note: “in the earliest days, they're actually not that, um, capital intensive.”

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “began by building what we call our MCP gateway. This is a single MCP server”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it.”

Q to the way that you run the company. It was so important in so many of my conversations before this. If we start with like the board meetings, I, I suppose as I said, many of the ambassadors, Every six weeks, not every quarter. Can you talk to me about your biggest lessons on how to really get the most out of your board and run the best board meetings?

A We do a couple of things. You mentioned the, uh, six week cadence. We have kind of a tick tock, a three hour meeting and a one, a one and a half hour meeting. We've done this since the beginning of the company because we could just see if you're on the AI time clock, it moves a lot faster. Things are changing, and most recently we came back from winter break, and suddenly coding agents were amazing. You had, right, Claude, four, five, Codex, five, two. There is a fundamental step change in the capabilities of these models. It changed our approach to software development. It changed our approach to the core product, and so having a cadence where you can take in information, even from the last six weeks, kind of update your priors, and then change course, I think is quite important. As for running the board meetings themselves, we don't have board decks, we have board memos. So Brett and I write a usually six to 10 page memo. There's a saying, writing is just thinking on paper, and I think it's very hard to hide from writing. And so getting our thoughts clearly out onto paper, sending that in advance, giving each of our board members some kind of soak time to, to think through the issues and come prepared. Rather than be like presented to and managed, I think is a big part of it. And then the contents of the board letters themselves, I think is notable. We've done quite well in o…

AI assessment note: “we don't have board decks, we have board memos”

Q Did you consider training owned models and what was the thought process around not?

A That's a great question. We did briefly And discarded it. Uh, if you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models. Character, inflection, adapt, great people at these companies, but the capital expense, uh, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work. Just doesn't work for any, but a small number of companies. And so our calculus was for areas that are deeply capital intensive. How do we slipstream behind the investments that the labs, that, uh, the hyperscalers are making and take as much as we can off the shelf while still being willing to, uh, engineer more deeply. So today we have a set of our own proprietary fine tune models. But these are fine tunes on top of open weights models. So we're not going, you know, all the way down to the, uh, you know, mega cluster training runs. Um, and I, I think it's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

AI assessment note: “We did briefly And discarded it. Uh, if you recall at the time”

Q Can you talk to me about what that is, how it was built, what it does? I'm just intrigued to see how companies change and how they operate.

A Yeah. It's one of the more significant developments in how we run the company of the last six or nine months. And we, we began by building what we call our MCP gateway. This is a single MCP server. That aggregates all of the main systems and services that we use to run the company. And so you can add this single gateway to your Claude instance, to your Codex instance, uh, and, uh, indeed to, to Pinecone, and basically via any one of those agents have full access with the permissions, of course, that you, as, as an individual at the company, you can't read someone else's documents, but you can read your own. You can read your own Slack messages. And it's kind of like having superpowers, right? You can, you can interrogate in essence, the entirety of the company, all information that is published, whether it's, um, slack messages or presentations or operating reviews, uh, and, and so on, and use access to all of that information to better reason, make decisions, get things done. Pinecone, uh, of course incorporates that MCP gateway, but then is a Purpose built harness for all of Sierra. So Pinecone knows how to build Pinecone. So it, there's a whole harness around the engineering of Pinecone and our engineers there are phenomenally productive. We have a whole harness around the core of our platform, our agent architecture, agent studio, where you build and deploy agents, speeding…

AI assessment note: “we began by building what we call our MCP gateway. This is a single MCP server”

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 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 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 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 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 Have you been surprised by how price sensitive people are around security and code reviews?

A Not in our segment. I would say in the engineering segment, um, And we, we have some engineers use Replit, but the 75% are non-engineers. Engineers are more price sensitive because they have a lot of options. They can use a lot of different products on the, on the market. Now, when, when you're an operations manager using Replit, And you just saved 10,000 dollars on a SaaS software. You've gained, you've saved another, you know, 200,000 dollars on, on headcount. And you're spending an additional thousand dollars to just make sure that the software is more secure. That's like a no brainer. The ROI has been a hundred fold for, for, for companies we work with. On the consumer side, there's more price sensitivity, especially if I'm an entrepreneur just dipping my toes, which is why we reduce the price on our core plan. So I think there's going to be, and you, you, you hinted at that earlier, there's going to be this different models for different use cases or different parts of your journey. If you're just starting out, you don't want to be hit with a thousand dollar bill. You want to be able to play around with 20, 30 dollars before you commit.

AI assessment note: “Not in our segment. I would say in the engineering segment”