Speaker 1I think there is an opportunity to create a $100 billion company with a lot of specialized models in the inference. There's going to be a pullback. I'm okay with that because I think the long-term opportunity is so fucking big. There's just so much demand for it. It's like so large. I don't want to sell this company because I would just go and start Model 2. We've lost new grads going to, you know, some of the labs for like, like talking, you know, 20 plus million. I mean, these are exceptional people, exceptional.
Speaker 2This is 20 VC with me, Harry Stebbings. And do you know what I wish I knew 12 months ago that I now know? The demand for inference, it's infinite. And we're seeing some of the biggest companies in the world be built in this space. Today, I'm thrilled to welcome one of those founders to the hot seat in the form of Eric, co-founder and CEO of Model. Now, Model just hit a billion in ARR. They just raised $750 million led by Excel. And they're priced at $15.75 billion today. I actually think it's a steal, by the way. But today, we dig into the inference market. What to come, what no one sees that everyone should see, and so much more. This was a phenomenal discussion with Eric. But before we dive into the show today, Blitzy, the autonomous software development platform that's built for enterprise scale, is proud to sponsor today's show. Enterprise codebases have grown beyond human comprehension. Tens of millions of lines, decades of tech debt, complexity existing tools cannot fathom. Blitzy starts by understanding. Thousands of agents reverse engineer your codebase into a proprietary knowledge graph that maps architecture, dependencies, and business logic. That graph surfaces every CVE and zero-day hiding in your code before anyone hands you a ticket. Grounded in that context, Blitzy then executes entire epics autonomously. Modernize off legacy systems, ship new features 5x faster, or batch-remediate CVEs at scale, surfaced by the platform. Blitzy not only generates code, it validates and end-to-end tests every single line. And that's why Fortune 4 is so important. In addition to 500 enterprises, a shipping enterprise-grade code 5x faster. Go to Blitzy.com/20VC to learn more. While Blitzy helps you build faster, Flex helps you run your finances smarter. Business owners run their whole financial life on Flex. One platform from business revenue to their personal spend. Float every purchase for 60 days, tap capital that grows with your revenue, and pay vendors in 170 days. You can do it in 30 countries, across 32 currencies. Plus the whole back office: Bills, expenses, accounting, all in one place. So you spend less time reconciling, and more time growing. That's why thousands of owners use Flex, named one of Fast Company's most innovative companies of 2026. Visit flex.one, that's F-L-E-X dot O-N-E, and use the code 20VC. While Flex runs your finances. It keeps your compliance covered. When you're building a company, you learn that trust is what closes deals. You may have the best product, but no buyers will sign these days without proof of your security. Here's what happens if you're not prepared. A prospect asks for proof of compliance. The deal stools when you scramble. Your engineer gets pulled off the roadmap to audit prep. Every enterprise conversation turns into this horrible fire drill. That's where Vanta comes in. Vanta is the leading agentic trust platform that not only gets you compliant fast with frameworks like SOC 2, ISO 27001, HIPAA, and GDPR, but keeps you compliant by continuously monitoring your controls. So your deals keep moving and your engineers really keep building. Now access the Vanta agent everywhere you work, even if your team lives in Claude or Cursor. And that's why Vanta is trusted by more than 16,000 companies like Ramp, Harvey, and Writer. So prove you're ready for business and get $1,000 off Vanta when you go to vanta.com/20VC. That's Vanta.com/20VC. You have now arrived at your destination. Eric, listen, dude, it is so great to have you on the show. You know, I was sitting downstairs with you chatting before and I was just like, you can feel when it's going to be a good show because people are themselves. And that was an amazing conversation and we're going to have a lot of fun. So thank you for joining me.
Speaker 1It's so exciting to be here. I mean, I've listened to so many episodes, it's kind of surreal to be inside of it right
Speaker 2now. That's really awesome. As I said to you. You could have texted me when you started the company and maybe I would have been a little bit richer. Yeah, but you probably didn't know about me then. No, I didn't, but I would have welcomed investing in another suite. I wanted to start with, I was talking to Zach Frankel last night and he said about your childhood and like early indicators of success and like exceptional ability. How did you fall in love with technology? And for me as an investor, what would have made me excited in seeing your childhood?
Speaker 1I started coding 1992 or something like that. I was eight. I just loved it. You know, it's like one of those things, like I realized you can control computers. I had a Mac plus, you know, one megabyte of RAM or whatever. And yeah, I started making games and like, I mean, they were terrible, but you know, it's just, I think back at like, in a way I'm like so grateful. I look back at like my childhood, my friends, they were like collecting hockey cards or whatever. That was like their hobby. My hobby was programming and now I make a lot of money from it. Isn't that kind of crazy? Like, you know what you think about it? Like the fact that I can make a lot of money, like doing what was my hobby since I was eight. It's kind of nuts. So I'm just so grateful about, you know, the fact that I fell in love with technology and coding. And then, you know, I started doing programming competitions when I was 16, 17 and so much fun. Modal was your
Speaker 2first company or is your first company. Why did it take so long?
Speaker 1I think the consistent pattern in my life is like, I never had quite enough confidence in myself and a lot of it that was missing was just the confidence, a little bit the money too. But so it wasn't until I was like 36, 37, I started Modal. I don't think, I don't think I've ever wanted to necessarily start a company. It was like one of those things where like, I just saw this big opportunity in the market. I almost thought to myself, like, why is no one else doing this? Such an obvious idea. And that led me to think like, maybe I should start a company. And then, you know, five years later, here we are, it's gone pretty well. I think in hindsight, maybe I should have started much earlier. Do you wish you had? I look at some of these like Stanford kids and they're like 21 and like, I kind of feel like they maybe have too much confidence. You know what I mean? Like, they're like, you know, think they're going to take over the world and they're 21. I think the ideal age is maybe like, I don't know, 32 or something like that. You have a little bit more experience. You know how to navigate people, you know how to hire people, you know how to fire people, like all these things. I don't know if I would have been a good founder at like in my 20s.
Speaker 2Before we get into the market and everything beneath that, we were chatting downstairs. We often hear that founding companies is like eating glass, it's hard, it's painful. How do you feel about those?
Speaker 1Dude, I hate that stuff. It's like, I don't get it. Like you're eating glass like every day, like why are you doing it then? Like, I don't know. There's some like magic reward, like some big cookie in the end. Like I never understood that mindset. I think it's like this mindset that a lot of people have. I'm going to suffer pain and then there's going to be this big reward in the end. I don't know. I don't think about it that way. I enjoy every day I go to work. Of course, there's like hard times, like there's, you know, you have to have hard conversations, maybe fire people, maybe like do hard things, but like in the grand scheme of things, like, I mean, such is life and I enjoy life. I enjoy building a company and I don't think of it as like one day we're going to flip this company and be, I hope to work on this for 20, 30, 40, 50 years. I'm having so much fun. I don't want to sell this company because I would just go and start model two. You really wouldn't. No, I don't. I don't think money necessarily makes you happy. I mean, a little bit, like it's good to have some money, but you know, beyond a point, like what, you know, I like building stuff. I love technology. I love coding. So much fun. You're such a Swede. Is that Swedish mentality? Maybe it is. Yeah. Dude, never has an American come on before and said it's not about money. I mean, I do care about money a little bit, but maybe you're right. Because I do think in Swedish mentality, it's like, it's like self-actualization, like doing what you enjoy. It's like kind of the highest form of prestige in a certain way. And it could be music. It could be like poetry, whatever. Like it almost doesn't matter. What do you do that you don't enjoy? Yeah. I kind of hate fundraising, honestly. I mean, it's fine. I don't know. Like I enjoy it when it's like good people, but it's like kind of challenging. You have to say no to a lot of people. I guess that's a good problem to have, but you know, in general, it's, it's.
Speaker 2Oh, you have to say no to a lot of people.
Speaker 1Oh, Eric. I know. I know. That's a horrible thing. Oh, Harry, you can only have 10 million, not 50 million. Yeah. Oh. You've never made a pitch deck, have you? I've never made a pitch deck. Yeah. Every round we've done so far, it's, I like to get to know people well in advance of raising money. So, so every round we've done, I mean, most recently we announced the D round yesterday with Excel. I've been speaking to Rich and Gonzalo at Excel for, I think at least a year. And at some point, you know, we're like, actually, you know what? We could take some money. Like what are the terms? Like let's discuss terms. And then it happened very quickly because we already had that relationship. How do you think about price optimization?
Speaker 2Is it a game? Is it a game of getting the highest price?
Speaker 1No, I don't think so. I think maybe in later stages, it becomes a little bit more of a commodity, like the money itself. But I think in the early days, like, I mean, for several reasons, like one is that there's a misaligned incentive. Like VCs always want to put in way more money than a company needs. And that, you know, you just take on a lot of dilution. It's not good as a shareholder. But the other thing is like, you actually want to hold back, I think, and it's often better to raise a lot less money at a lower valuation and then keep the VCs hungry for the next round.
Speaker 2Is this not the generation though, where actually capital is the most valuable? And you need so much more money than you think. You said that VCs want to give you more money than the company needs. I thought AI was finally the output exhaust for venture dollars that was needed.
Speaker 1I think there are times in the market where you are capital constrained. We have not been capital constrained for most of the time of the company. We've been extremely capital efficient. We burn only a few million a month. I mean, only like, you know, it's a lot of money. I respect for the money.
Speaker 2You burn a few million a month?
Speaker 1No, I mean, up until like last year. Now we have to spend a lot of money on capacity.
Speaker 2How much do you burn a month now?
Speaker 1I mean, we actually are profitable on a P&L basis, but like cash flow is quite negative because we have to spend a lot of money on getting GPUs. use in building out capacity. And so that is a different period in the company and how we think about capital. Capital is, I don't want to call it a moat because I feel like people throw around that word to mean things that. Moat to me has a very strict definition, but it certainly helps. Capital helps at this point.
Speaker 2What's the moat definition for you?
Speaker 1I mean, moat to me is like, you go read Seven Powers or one of those things. I love Seven Powers. It's a good book. But I think then people read that and they get delusional. They're like, yeah, we're starting this company. It has all these moats. You never have moats in the early days. Google could do anything they wanted to. Google could do. Yeah, but it's also like, what are the seven. I don't even remember. It's like cornering resources, market power, whatever. It's like none of those things happen until you're 10 years in. There's an old blog post. I always come back to it by Ben Thompson. It talks about Snap and the gingerbread man strategy. Do you know this thing? And it's like, the whole point is Snap doesn't have a moat. They just run faster. They're the gingerbread man. And I actually always come back to it. I think that's so true for almost every startup these days. It's like, you just have to move faster than everyone else.
Speaker 2I actually think the three things that matter to say when I invest is one, team. Are they top 1% team? Two, speed of product decision-making, very specifically. You look at instinct today. The speed of feature shipping is really very impressive. And then three is brand. And I think those are the three determinants to me of defensibility moat, but it's a kind of package.
Speaker 1Yeah. I mean, again, I don't know if I would. But I kind of agree with your point. I think brand, I don't want to call it a moat, but it's actually, I think the value of brand has gone up a lot. Can I be a dick? Yeah, please. Do you think Modal have done well with brand? I think pretty well. I think we have. It's coupled with the developer experience and the product and the website and the visual identity and all these things. We just did an event in San Francisco and I thought a lot about that, sort of how we package it, the event and the visual look and feel and the vibes.
Speaker 2But do you think you have a bigger brand than Base 10 and Fireworks?
Speaker 1I think we have that. I think we have really, really, really good vibes. I'm terminally online. People tweet way more about Modal. I think there's a little bit more of a community. I think there's much better vibes around us. But I am paranoid about those companies. I am paranoid about competition. Of course, I track it. Of course, I see everything that gets tweeted and about our competitors. And this is important. Everyone wants to be a part of the community. Everyone. You know what I think is. People misunderstand. I love TypeSafe with Jev, because everyone wants to clone Jev. No one wants to get up on stage and wear a pink fur coat and just talk about the Jevolution in an unhinged way. That is what brand, to me, is. That guy, he's playing the game on a whole different level. And I think it's just so true about companies today. You have to think about that.
Speaker 2So can you tell me about why? Because he's authentic and different?
Speaker 1I think so. I think you just have to create a personality. You kind of have to figure out what is the personality of your company and just be consistent and create almost a sense of movement, a sense of community. This is something cool. And people want to use you, because. Yeah. You're one of the cool products out there. They're like, "Have you tried Modal?
Speaker 3It's pretty cool." What has Modal done then that Base10 and Fireworks haven't to build that?
Speaker 1I think we started with the developer experience. And we had a very clear sort of PLG product market fit, product-led growth, in the sense that people came bottoms up. They just signed up on a product. I think the consumer background of coming from Spotify is very important in how I thought about building what's now a B2B company, selling tech effectively. It's a very boring category, but I think. Having personality, and. Yeah. Having personality, and having a little bit of an edge is a big part of how you create that sort of movement, and that sort of sense of excitement, and the feeling that you're different. And I wanted to start the moment you go to a website, and the moment you try the product, and you just feel that it's different, that magic moment.
Speaker 2Can I ask you, we've spoken about three different providers there. Inference as a market has many different speculators commenting on it. When you look at the next five years, how does the inference market play out?
Speaker 1First of all, I think inference is going to be an enormously large market. It's like the new cloud, basically. But I don't think inference in itself is maybe the only thing you need. One of the things that I feel very strongly about is it has to come with a lot of other products. It has to come with post-training, sandboxes, agentic stuff, all kinds of other types of inference, like low-latency inference, batch inference. So building that platform. By the way, we've been working on this for the last five years. I think that to me is not a moat, but it's a very hard product to build. Oh, wow. That infrastructure.
Speaker 3Why do you need to have the breadth of that product set? Otherwise, you're just in a compression layer.
Speaker 1Yeah, because otherwise, you're just selling commodity product. And if you're just selling tokens for dollars, there's no switching cost. People can just switch to a different provider. It's just a race to the bottom. But if you couple it with all these other things around it, sandboxes and post-training and reinforcement learning and all these things, you can actually create a whole set of tools. No one wants to buy a single trick pony. People want to buy a portfolio of different things. They want to have a whole suite of tools that basically lets them build amazing AI applications on top of it. Right? And the infrastructure underneath is a lot of differentiation, a lot of staying power in the markets. It's very hard to copy the infrastructure required to build all of these products.
Speaker 2What does no one see about the inference market, or you don't see commented on much, that is very obvious to you?
Speaker 1One thing that people don't see about the inference market is, I actually think in the long term, there is so much gross margin opportunity in this space because you can build all these things around it. All the sandboxes, all the. Yeah. All the agentic stuff, workflows, post-training reinforcement learning. If you do all those things, there's actually so much opportunity to make money.
Speaker 2Is it a market that has software margins? Because we're looking at margins now in the 25 to 40% range. That's kind of traditional players. I don't know if that's yours, but that's kind of what inference providers are generally. Do they go to 70 as you own more and more of the stuff?
Speaker 1I think basically almost no company today has software margins anymore. What you used to see in Silicon Valley 10 years ago with this 90% margins plus, I think that's basically gone. And the reason is actually almost topological. It's like you have this stack of vendors. Today you have applications, you have models, you have infrastructure, you have cloud, you have Nvidia. And so when a customer pays a dollar at the top of the stack, it has to flow through a lot of different layers. And at every layer, there's going to be 20%, 30% gross margins. So I think there's a lot of margin opportunity, and there's a lot of opportunity to make a lot of money. This space is so big, but you're also splitting it across a lot of layers in the stack. So I mean, the 90% software margin businesses are gone. I think Modal has an opportunity long-term to get to 40, 50, 60% margins, but we're never going to get to software margins.
Speaker 2What's a wild prediction you have on the future of the inference market that you think many people would disagree with?
Speaker 1I think one prediction about the inference market is going to split into a lot of different things, because the market got so big in the last. I don't think anyone expected, me included, I don't think anyone expected it to be so big. It's like 10X or 100X, it's bigger than I ever thought. And that to me means there's going to be so much room for specialization, low latency, high latency, various types of fine tuning and post-training. I think there's going to be some wacky new technologies that let you do it with way less resources, new hardware, all kinds of different things.
Speaker 2I'm really sorry. Does that not go against what you just said, though, about the need to own it all and have that breadth of product? If it's so much larger that you need real specialization.
Speaker 1Specialization by product. I think it's going to fragment into a lot of different products. Now, we can build a lot of those products and host it all under Modal. Yeah. And that to me is very exciting because a lot of different. There's going to be a lot of different products that companies are going to need. For one product, they're going to need low latency inference. For one product, they're going to need batch inference. For one product, they're going to need sound boxes, which we have. For one product, they're going to do pre-training their own models. Maybe it's certain specialized audio models or whatever. We do a lot of stuff besides LM inference. And so that to me, it creates a lot of opportunity to basically kind of rethink what a cloud looks like in the era of AI and kind of deconstruct the cloud and rebuild it for this new era of software.
Speaker 2Earlier, you said about building capacity with GPUs. Yeah. What's it like doing that today?
Speaker 1Is it as crazy as reports suggest? So it is pretty crazy. I spent some time in March at GTC, NVIDIA's big conference in San Jose. I would describe that as like an open-air drug market, but GPUs instead of drugs. You're running around talking to 500 different cloud vendors. You're like, "Do you have GPUs? Please get. " And in the end, you come out, you do a lot of deals. And that's kind of what I like about it. And that's something we've had to develop the muscle. I think part of the value prop of modal is our customers don't have to think about that. We do the dirty work of doing all the GPU deals under the hood. We work with 25 different Neo clouds. Right now, we control 0.1% or whatever. I don't even know the number of the world's compute. But my crazy how I think about the long-term goal is as we control more and more of the world's compute, you can aggregate that capacity and aggregate demand on the other side and manage that capacity and demand extremely efficiently. That, to me, is something we actually think we can do. We can fill a big gap. What does that allow you to do, being that aggregation layer? Well, so we built a lot of our infrastructure ourselves. We don't use Kubernetes and Docker and things like that. We built it on file system. We built it on container runtime. We built it on scheduler. We built it on basically how we manage the capacity. And that enables us to basically treat all of the world's compute as one big pool of capacity. And so if you think about the fact that we already have many thousand paying customers, and over time, it just grows and grows, each of those customers have very sort of very large amounts of capacity. And so if you think of variable requirements, or their workloads vary over the day, and they have maybe bursts, and each individually, they're very unpredictable. But if you pull all of those customers together, you end up with a very smooth overall demand. And that is something we can manage extremely efficiently. And so one of the valid props of Modal, besides we can scale up with you, is also you don't even have to do capacity planning. Just come to us. We'll give you 100 GPUs. I think it's so amazing going back to TypeSafe, for instance. We had talked to them a minute ago before they launched, but they launched overnight on Modal, and we weren't even aware of it. They just like scale up, and we gave them a ton of GPUs. it just happened automatically. So that magic, I think, is something that people really value with modal. It's like we can give them a lot of GPUs on very short notice. And we can handle that sort of variable. If you go to us, you don't have to lock up a rectangular block of GPUs over three years, and you have to know the exact number. It's pay as you go. You scale up and down. You pay per GPU second.
Speaker 2How do you do GPU buying in terms of forecasting then? Because you don't want to have an insane excess of supply just sitting on your books.
Speaker 1Totally. The GPU lead times right now is about three to four months. So we basically do a straight line or exponential line extrapolation. And we look at an aggregate across all our customers. We have a very diversified set of customers. You can actually look at the demand and aggregate pretty well and predict based on monthly growth rates, which have been about 30%, 40% month to month for the last 12 months. And you just project that three to four months into the future. You end up with like two to three X. You go out and buy that capacity and make sure to ramp up.
Speaker 2And sometimes- Can you just buy that capacity?
Speaker 1You can. Yeah. I mean, I think there's a little bit of like a misconception that we're out of capacity. I mean, NVIDIA is like shipping like an insane amount of GPUs like every month. I don't know how many hundred thousand Blackwells are getting installed. Prices are up for sure. You have to pay more to get the same amount of GPUs. And I don't want to say it's like an efficient market, but if you're willing to pay more than other people, you can get a lot of GPUs. Is there a price at which it doesn't make sense for you?
Speaker 2Of course there is, but at what point?
Speaker 1I don't think so. I think in fact, like we're actually more long than we're short GPUs because we can charge a higher price to our customers. There's effectively a market price and it's like different dynamics because we lock up. Longer term reservations and rent them out on slightly shorter duration. But if the underlying price goes up, people are also willing to pay more.
Speaker 2What happens to the price over time? H100s were meant to depreciate and go down. I have no idea.
Speaker 1Yeah. I mean, actually it's funny. Well, dude, if you have no idea, what am I meant to know? Did you see Jensen yesterday? He was in London actually. Someone asked him this question. He's like, well, obviously supply is going to catch up with demand eventually. And then we'll see what happens with pricing. It might happen in a year. It might happen in five years. He said exactly the thing I always tell people. Hardware prices, it's like gravity. Hardware prices always go down in the very long run. Right now we're in a very, there's a supply shortage and that means that prices have gone up. They may continue to go up for another year, two years, five years. I don't know. But eventually they will come down.
Speaker 2How early are we? Because I run Instinct for everything. I have over a hundred automations on Instinct doing things that are just absolutely wild from buying concert tickets to planning shows to helping with medication for my mother. The amount of compute I am using is Jevons Paradox times a hundred. If that is equivalent for actually many people in society, because it's accessible within WhatsApp and what not, how early are we there?
Speaker 1I think we're very early. I think you're saying the same thing. I mean, yeah, Jevons Paradox, whatever. I think there's just so much latent demand for these things. And I think eventually we're going to start to run up against these capital constraints, right? There's interest rates that are going up. We just can't build data centers fast enough. Maybe that's good. I don't know. I think in a. In a way, it's a guarantee against overbuild. But I also don't know if we can overbuild these things. I don't
Speaker 2think you can overbuild in the next
Speaker 1five years. Probably not. No. I think what could happen is people realize Instinct is a good example. Allegedly they don't charge or they don't make revenue yet. So I think it's a good example, but clearly Instinct can make a ton of money in the future. That's the dynamic in the market. There's a lot of companies that are not yet making money yet, and they're using a lot of GPUs. They just have to. They just have to eventually start to make money. But that lack could take a while, right? And we might run up against that.
Speaker 2You mentioned the specialization that comes within the inference market. And we spoke about NVIDIA there. We're seeing more and more specialization within the chip market. Is NVIDIA's business today stronger than ever or actually weaker than ever as you see the fragmentation of the market into very specialized players from Cerebrus to Etch to Olyx to Fractal that weren't there before?
Speaker 1I think you're trying to phrase it as like one versus the other. My view is like, I think Eric Vissio is actually, it's funny, like the same thing. All of these things are going to grow. I think NVIDIA is going to keep growing. They're going to keep shipping amazing chips. I think there's also going to be like Cerebrus and all these Etched and all these other ones, like shipping specialized chips, and they're also going to grow. So I think there's going to be markets for all of it. So why is Cerebrus down 30%? I don't know. I don't follow really the stock market. You don't follow the stock market? No. I'm in like a very passive, buy the index, mostly because of my own time. I don't feel like I have the energy to follow the stock market. To what extent is CUDA the moat that everyone says it is for NVIDIA? I think it's a little overstated. I don't think CUDA itself is the moat. CUDA is actually kind of annoying to work with. It's like a pretty annoying programming model. But I do think all the NVIDIA, the ecosystem is. I think all the kernel optimizations and all the stuff that. Torch always supports NVIDIA much better than anything else. And the open source engines like VLM and SGLang, because of that, they always support it much better. So everything is NVIDIA first today. And that's probably going to continue. It's funny, for the last 15 years I've been saying, next year is the year of non-NVIDIA accelerators. It never keeps happening. I'm always wrong. NVIDIA has just shown to be incredibly good at having that staying power and just dominating the market. We'll see. I mean, there's Tranium, there's TPUs, there's AMD. There's a lot of other stuff that does look quite good and they're catching up, but I still think they're
Speaker 2quite behind. To what extent are the NEO clouds a friend? Because they're partnered with you in a lot of ways, I think 25, you said. Or a foe. I had the founder of Nabeus on and he was like, "We're coming for the inference market. We need to own full stack." And I don't mean that against him, I totally understand it. But are they friends or are they foes?
Speaker 1I feel like everyone in startup land is a friend and a foe at the same time. They have this weird relationship with everyone. I think some of the top NEO clouds, Nabeus included, they are trying to move up the stack. I'm personally somewhat skeptical. I think it's very hard to be good at your core business and also trying to move it. I think it's very hard to be good at your core business and also trying to move into different market at the same time.
Speaker 3Why would it be hard for them? What's the bear case on them being able to move into the inference layer efficiently?
Speaker 1I mean, I'm biased, but we spent the last five years building an incredibly complex infrastructure stack. I think it's very, very hard to replicate what we built. What's the hardest thing? All the code and everything around the file system, the container runtime, the scheduler. Building the system that can place millions of sandboxes every minute, scale up and down many, many thousands of GPUs, power hundreds of thousands of tasks running concurrently. These are very hard engineering problems. I mean, I'm an infra geek. I love that shit. It's so much fun to work on it, but these are hard problems. Is Nabeus a better business than Coolweave? I actually don't know. I don't know. I think they're both. They've executed extremely well, both of them. I have a tremendous respect for what we've done. I think at the end of the day, the thing that I have the most respect for is actually more on the financial engineering side. They're very good at that kind of stuff.
Speaker 2Why do you say that?
Speaker 1Because I think a lot of the constraint for building a NEO cloud very quickly is on the financial side. You need to be very good at, you go lock in a long-term customer contract, then you go raise a bunch of money against it, then you go buy the chips, then you deliver the chips, and then you pay off the debt over the next few years. Doing that sort of dance with the financials is kind of an interesting game. And Coolweave has done it exceptionally well. It's a capital game. It's a financial game.
Speaker 2When you move a layer beneath them, we spoke about Jev, who obviously worked with Modal. We've seen incredible movement from the open source community, which has been exciting. Yeah. But then you also see Haiku and Anthropics' newest models being incredibly performant and incredibly cheap. What's the benefit of open models if frontier models are almost as cheap and as efficient?
Speaker 1I think open source, there's always going to be great models coming out. Let's also remember the open source models, they're all moving into licensing models. So they're getting better at capturing the value of those models. And I think that means they're going to have more funding to train better models. And I really hope there's going to be European and Chinese models. Because I don't think it's a good situation that we have so many Chinese models. I mean, obviously I have tremendous respect for all the Chinese labs. They're extremely good at what they're doing. But I think it's good for the world if there's more power spread out. But anyway, going back to the question, I do think that open source, there's always going to be people who want to fine tune, want to have more control over their models and sort of tweak the parameters, like go low latency, high throughput, like whatever it is. And open source is going to play a big role in that. It's like Windows versus Linux. I am sorry, with the greatest of respects, do you think Europe will produce more of those?
Speaker 2Yeah. Do you think Europe will produce a competitive open source model?
Speaker 1Yeah. I mean, I love Europe. I grew up in Europe. I grew up in Sweden. But I do think that Europe does struggle with accumulating large amounts of capital. I think both of those things are things that Europe struggles with. And that is something that China has done really well. They've accumulated massive amounts of human capital. And US obviously has very good at raising enormous amounts of money and training very large models. I don't know if Europe has either of those.
Speaker 2Does it matter if NVIDIA owns the open source model layer in the US with first poolside and now reflection?
Speaker 1I think NVIDIA is very smart to focus on the ecosystem, building up an ecosystem. And I think that includes propping up a lot of open source models. And I think they probably saw an opportunity to pick up open source models. These companies are very resource intensive, very CapEx intensive. And I think it's smart of NVIDIA to buy some of those and sponsor them effectively. In order to create American-built models. By the way, I want to take back one thing I said. Europe does have an insane amount of human capital. I do think what Europe is very bad at is aggregating them and putting all of them at the same company. That is something I think US is very good at.
Speaker 2And I think inspiring a mindset of ambition and huge, huge scale. I think to the point that you said earlier, which is like, "Oh, I didn't have the belief in myself." I respectfully think if you're in the US, you might have been surrounded by influences that told you to start a company earlier.
Speaker 1Yeah, yeah, I mean, it's almost like too much on the other side to that point, but-- Oh, when you were four, you were encouraged start, leave school, drop out.
Speaker 2Kindergarten wasn't worth it. Exactly. I totally agree with you. I do want to talk about, we said about models and Jeb in particular. Are we seeing the unbundling of models on a per function basis where Jeb is obviously very good for certain tasks?
Speaker 1I think so. And I think that's a function of what I talked about earlier, where the market for inference is so large. And when a market is like a billion dollars, you're not going to have too many sub-markets. But when a market is starting to get 10 billion, 100 billion, whatever, you're going to have a lot of different sub-markets. And Jeb does classification and things like that, regression type things, very, very well. And there's a large enough market for that.
Speaker 2Okay. Welcome. It's your first day as a partner at 20VC Fund, and we're in the investment committee. And very kindly, we have the opportunity to invest. Jeb, we're all in on Jeb's new round. All in. What's the bull case for Jeb being worth 100 billion?
Speaker 1I told you before, but I think the brand and the community and the movement around, just the sort of everything they're doing is so incredible. And that, to me. To me, it's like a massive, massive asset in this new age. That's a $100 billion company. You would have done it, though, at $8.5 billion. I would have done it at $8.5 billion. I think there's a pretty big outcome here. Could be an exit, could be a public company, who knows? But I do think there's a lot of upside here.
Speaker 3Dude, I'd love to see him ringing the bell on the New York Stock Exchange. Do you know what I mean?
Speaker 1Yeah. Fantastic guy. Love that guy. Is he great? I mean, you know, his. His charisma, his, like, you have to create, like, a personality, like, around your brand. You have to create this, like, visual identity. It's obviously not the visual identity I would have picked. I hear you, but I think then you should be more loud, if I'm just honest, Eric.
Speaker 2Moodle? No. Well, yeah, but you.
Speaker 3Yeah. I mean, that's possible, right?
Speaker 2Like, I don't think there's, like, a personality-led cult from Moodle, from your perspective.
Speaker 1I think that's a fair point of criticism. I think I could be out there even more. I try to tweet. I try to. Blogging, I've been kind of bad at that recently. No one gives a shit about blogging. Yeah, maybe, right? Maybe I just need to, like, go more unhinged on Twitter. It's, like, founder-led marketing, basically. Like, in the early days of Moodle, like, that was a big part of our growth, right? Like, me just, like, tweeting all the time about what we're building. And I think that's really important. I mean, every company has to find its own sort of, you know, go-to-market strategy. But in the early days, like, we spent a lot of time just talking to people on Twitter, just, like, hitting people up, trying to product, what do you think? Half of them ended up angel investing. You know, they're now supporters of Moodle. And, you know, you have to lean into what you're good at. And in the early days, I did a lot of shit posting. Do you know, for every
Speaker 2follower that followed me before 50,000, I would send them a personal, like, thank you message. That's amazing. You typed it out yourself? Every night from 1 till 2.30 a.m. I used to send a personal. And I'd say, like, I see that you're from Sweden. I love the Grand Hotel. Let me know when you're in London. And I would send, like, 50 to 100 a night. That's dedication. That's amazing. No, but it was the customer acquisition flywheel. Yeah. Like, they would go into the office the next day and be like, wow, you know, this guy, he'd send me a message. And, like, you should listen to his show.
Speaker 1It's pretty good. It's smart. I love what you've been doing in terms of building up this, like, media brand.
Speaker 2It's fantastic. There's a great thing, which is, like, how can I 10x the customer experience? And actually, like, let's do it. Fuck it. Let's play a game. How do I 10x the current Modal customer experience?
Speaker 1I think the biggest thing we need is we need way more products in Modal. Like, we want to create a busy, comprehensive experience of anything you need to do in order to build an AI application should be a product inside Modal. The other thing we can do is hire way more FDs and way more sales reps. I will say we've been behind on that, but we should hire, like, way more people that reach out to you personally and say, hey, I saw you get started in Modal. I saw you're working in the bio space. How can we help you? Here's some bio examples, you know, protein folding, whatever, right? Like, we work with a bunch of other similar companies. You seem skeptical.
Speaker 2But no, I don't at all. I'm actually just thinking about, like, I like the product ambition, but I go, as a European, it goes, wow.
Speaker 1We're in the B2B space, so I think it's like, you know.
Speaker 2But do you worry about being stretched too thin? Yeah. To your point on, like, you know, why can't Nebius do what you do so efficiently? Because you're so good and you've spent so much time. As you, expand into all the specialities, there are players in each of them who've been doing that for years, where I'm like, oof, Eric.
Speaker 1I think that's true. I don't think they're very good, a lot of them. But I mean, I sort of agree with your higher level premise. Like, if there's anything that's, like, super critical and never goes away, it's prioritization and resource allocation, right? Like, you can't boil the water and try to do everything at the same time. You need to be extremely ruthless. And having a multi-product strategy, I think, is a very hard balance. Like, when do you branch out into second product or third product, right? Like, it took us four years to see success with a second product. Which was Sandboxes. Four years to see success with your second product. But you're five years to a billion in revenue. Yeah. So, we launched Sandboxes in 2023. Didn't see a lot of success in the early days. I mean, there was, like, some success, you know, it wasn't horrible. But, you know, it was a relatively insignificant part of our revenue up until summer of 2025, when models, I think, like, Sonic 4.5, one of those, like, they just got really good at generating code and building whole apps. And then it just exploded. I mean, Sandboxes today is a 300 million run rate business. It's about a third of our revenue. And so, that's been, kind of, a crazy journey. And now we're thinking about, you know, what's the third and the fourth? We just launched a training product. I'm super excited about that. We're working on batch products. We're working on reinforcement learning. We're working on all kinds of other stuff. And so, that evolution into becoming a platform, it's actually there. Like, if you go back to the very early decks of modal, you know, I put together some just for my own thinking. It was all there. It's like, we had this, like, market map and all these, you know, it ended up being very different products than we thought. But it's so important for me to create, like, you know, to focus on a category and focus on a set of solutions, not just, like, be a single widget vendor.
Speaker 2What do you know about the Sandboxes market today that you wish you had known when you started and released the first product?
Speaker 1It actually is, like, one of those products. I think one of the benefits of being in infrastructure, but also one of the drawbacks in a weird way is, like, you kind of just have to trust your gut on, like, what are, like, important primitives and just build, like, amazing building blocks. I think we're in the space of, like, providing building blocks. We try to look at, like, what do engineers need and not, like, listen too carefully because, like, they'll, like, build a lot of stuff. But, like, you know, like, you're asked for, like, all kinds of random stuff. We kind of distill it down into, like, what are some of the building blocks? And we put out a serverless function product in 2021. It saw success in 2023 with Gen AI inference. We put out a Sandbox product in 2023 because we saw a lot of people using LLMs to generate code. And then it saw success in 2025, like I said. So a lot of, like, how we think about building products has always been kind of, like, let's just put out, like, good building blocks. And then it exploded. But, like, to answer your question, it was reinforcement learning, it was vibe coding, background agents, and maybe some other stuff too in the future. Those were things we actually didn't anticipate. So I think infrastructure is often like that. Like, we didn't anticipate stable diffusion when we started building our serverless function product. It just happened to be the killer app.
Speaker 2What's your takeaway from that? The only thing that matters is you've got to stay in the game long enough for your timing to be right?
Speaker 1Sometimes I think that's right. I mean, we've definitely been extremely lucky in certain ways with timing. Like, we put out products, like, right before we started the company in 2021. And then, you know, Stable Diffusion and ChatGPD came out in 2022. And then, you know, we started the company in 2021. We had really good timing with that, right? It just created so much demand and we're very well positioned. But some of it is also, like, with infrastructure, like, again, like, I think it's about, like, building products that, you know, you anticipate will be useful in the future. And from your own experience are things that you always wanted to see. I think you have to kind of trust your gut in
Speaker 2that. You said that about resource allocation and that being very, very important as you think about this breadth of products. Where did you allocate resources where with the benefit of hindsight, you wish you hadn't? And how do you reflect on that?
Speaker 1I think we should have allocated more resources towards that. Yeah. I think we should have allocated more resources towards LLM inference, like, earlier. I think we're a little bit on the sidelines for a while. We didn't realize how big open source inference would be. And we thought it would be kind of a race to the bottom, just a commodity space. And then Deepsea came out and we're like, fuck, like, we got to move very fast. And so we staffed up a team immediately, you know, went from, like, you know, one or two people working on this to, like, 30 people 12 months later. And now we're one of the top three providers. Jason Lamkin, a dear friend of mine who I
Speaker 2do a show with every Thursday, he always says that now we've reached peak open source LLM inference. Jason Lamkin, a dear friend of mine who I do a show with every Thursday, he always says that now we've reached peak open source LLM inference usage. We're going to see Frontier providers bluntly provide incredibly cheap products that they are now. US enterprises don't want to use Chinese models, that will continue. And so actually, with those two core prevailing wins, Frontier will actually research and we will see a reduction/ plateauing of the US LLM inference model.
Speaker 1I think that's a little bit of a fixed pie size mentality. I think, with all due respect, I do think both spaces will grow 5x, 10x, 20x over the next 12 months and beyond. And so it's hard to argue which one's going to be bigger than the other, but I think both will grow in orders of magnitude over the next few years.
Speaker 25x, 10x, 20x over the next 12 months. Yeah, for sure. You just hit a billion in revenue. Well done. Yeah. Thank you. Does that feel pretty cool?
Speaker 1It feels pretty cool. I don't know. I'm this Swedish guy that grew up in a random. For me, these numbers are absurd, but yeah, it feels great. It's so much money. I love that.
Speaker 3Eric, you're so. Do you know what? I wasn't expecting you to be so likable and cool. I don't know. It's like a lot of zeros. How many zeros are in a billion? It's a lot. Your wealth manager will tell you. When you look at that, where will
Speaker 2you be in 12 months?
Speaker 1I don't know. What I can say is we've been growing. We grew 15x in the last 12 months, so you can extrapolate that. I mean, I don't know. At some point, we start. Markets just get very big and they start to slow down a little bit. But with the growth rate right now, the growth rates are not slowing down. We're growing 30, 40% every month. I hope to be way beyond that.
Speaker 2With the greatest of respects, 15 billion then feels like quite a cheap price. I'm an investor, so this is now my ballpark. If you think you're going to be at 4 billion, say, dropping, I don't know, a billion two to the bottom line, then they actually only paid, what, 14x end of year, next year bottom line, 3x top line? Yeah, it's kind of cheap. That feels pretty cheap.
Speaker 1I think it's cheap. I wouldn't do this if I didn't think we could grow and become a generational company. I think there's so much upside. I think this is one of the largest opportunities that's ever existed. I think that the demand for computing the demand for AI applications, it's going to be enormous. And they're all going to need a place to run. And I hope a lot of them are going to run a modal.
Speaker 2What revenue line do you not have today, or is tiny, that you think will be very significant in three years' time, five years' time?
Speaker 1That is a good question. I think training is something we just launched. I think that could be very large. I hope that could get to 100 million run rate in the next six to 12 months. Post-training, similarly. I'm working on batch jobs. I'm very excited also about a lot of traditional data warehousing stuff, databases, workflow orchestration, feature stores, vector database. There's just so many parts of this. And they all fit together very nicely. And that, to me, is what I'm so excited about. Once you start building in this space, there's so many natural adjacencies. This is a problem with me. I get excited about. I have no lack of ideas. At Modo, we have so many ideas of things we want to go. I would love to get into continuous integration. That's a very interesting space. Agent harnesses. Maybe we should train models. Maybe. Yeah. There's just 5,000 directions. And that's my ADD brain. It wants to run in all of those directions. You have to be, obviously, ruthless with prioritization and only take on one or two things at the right time. But there's so many different directions we can go in. It's amazing that your wife calls you for decisions.
Speaker 2She must get a very incoherent answer. I know. I know. What is this? What is this?
Speaker 1I just wanted some curtains, Don. Yeah. With the curtains, I just like to pick one of them. I've seen Simpsons and Reign of Wolf Castle becomes the president. And they're like, which one do you want to pick? One to seven. He's like, I was born to lead, not to read. Number four. Yeah.
Speaker 2Can I ask? We've seen this real shift, I think, in emphasis, but also in resource allocation from pre-training to post-training. How do you feel about that? And do you think that is the new normal?
Speaker 1I don't know. I think post-training works because there is a lot of tasks that people do in real life that have fairly finite feedback mechanisms. So coding is a great example. You can do coding, which is reinforcement learning, pretty well because you can run code in sandboxes. And a lot of labs are running modal sandboxes for that exact purpose. You start to look at cybersecurity as another area. Math, obviously, with Lean. I mean, we've seen some fantastic projects. I think it's kind of incredible, all these open AI proofs the other day. So law, maybe to some extent, you can do eval. So I think post-training right now, there's such an incredible focus on it because you have all these tasks that traditionally only humans can do, but actually well within reach for agents. And for LLMs. But I don't know if it extends beyond that. I don't know. There's a lot of tasks out there that have way longer feedback loops that I don't know if post-training quite yet is within reach. A lot of people ask me, what's going to happen with software engineers in the long term? And I think we're probably going to automate everything that an agent can do within 24 hours or a week or something like that. But I kind of feel like engineers, they work on a longer time scale and they have feedback loops that are longer. So I have a feeling like reinforcement learning, we're going to start to run into some limitations around that. But I have been wrong for so long, so maybe I'll be wrong. It just keeps going. And eventually we can do reinforcement learning on things that take six months for a human. I don't know. What does the day look like in three years for an engineer? I mean, I'm extremely bullish on software engineer. I've been coding for 30 years and I've seen so many different changes with programming languages and the cloud and so many different frameworks. And every single time we make programmers more efficient, it just increases demand for software. There's so many parts of the world that desperately need software. And so I'm very, very bullish on software and the need for software engineering. And I think one of the biggest misconceptions from the outside is that developer is about translating from English to code. That has never been in the job of software engineers. It's like to understand the requirements in the first place and be kind of an interface between the humans. You're building for humans at the end of the day and trying to figure out what does that even mean in terms of processes and workflows. And that continuously moves up in the stack. Back in the '60s, we used to write assembly and punch cards, and then you moved to COBOL and then to C++ and then to Python. And now we're using agents. And that's just one. It's definitely the biggest thing that I've seen in the last 30 years. But I do think we have many, many analogs and many parallels throughout the history of software and computers. To me, that makes me feel like software engineers are going to be fine for the next 10, 20 years.
Speaker 2How much money do you spend on coding agents a month at Modal?
Speaker 1I think a half a million or something like that. How many engineers do you have?
Speaker 3150. That's so worrying. You want to. Should be token maxing more? There should be 10x that. Yeah, maybe. I don't know.
Speaker 2Brandon McCaw, that they spend 3x their human salaries on inference.
Speaker 1Yeah, we're not quite there yet. But I will say, our software engineers are extremely agent pills, including myself. I mean, I write code with agents all the time.
Speaker 2But I think the big question, if you're looking at an anthropic IPO, and don't worry, I'm not doing a Bloomberg and pulling you in to be like, "What do you think of the anthropic IPO?" I hate those ones. Great. I think it's great. Yeah, it's great. No one in AI is like, "Oh, not a fan." Yeah, yeah, yeah. I'm against it. Everyone is like, "Go." But I do think a big. The mother load, so to speak, is Cloud Code. It's $60 billion of revenue. It largely dictates the $2 trillion price. And you look at Mark Benioff, who says that they spend 5% of engineers' salaries, 300 million, on Cloud Code today. Now, if that comes way down, as efficiencies improve, you name it, that's a challenge. Or will that go up as it just eats more and more of engineers' workflows?
Speaker 1Yeah, it's just going to eat more. I mean, again, we've seen this so many times. You make something more efficient. You make something more powerful. It just unlocks five new use cases. And then demand just goes up in the end. That's just the history of the last 50 years of computers and software. But I wouldn't be here if I wasn't such an optimist. That's why I started a company. I'm just the biggest optimist that there is in the world about software and computers. I just love this shit. And I think it's just going to
Speaker 3keep growing. The data market is an interesting one. We have so many players that have scaled so fast.
Speaker 1Yeah. It's incredible. If I was 20 and I just wanted to make a lot of money, I would go out and start an environment company and just sell data to Frontier Labs. There is so much money to be made in that space. It's kind of incredible. Let's go a layer deeper. What would you do next? Do you specialize on a specific market? I mean, the coding space is where you see the most demand right now. If you can sell a code environment to the labs, you can make a lot of money. There's a big space in that. Still? Still. Yeah. Yeah. I meet startups all the time. I angel invested in a bunch of that. I mean, they're incredible businesses. What I will say, my one piece of advice is you make a lot of money today, but is this a venture fund? Do you want to get on the venture treadmill? I'm not sure. I think these are companies that are going to make a lot of money today. I don't know if they're going to make a lot of money in the very long run, but who cares? Take the money. It's great money. It's a fantastic business today, and that's all that matters.
Speaker 2I think there's data marketplaces, and then there's data and services. And when you look at the leading providers, they started as the data acquirers, and then just sell you the data, and that's kind of it. And I think they've really gone full lifecycle now to deployment, implementation, evaluation, monitoring with a real enterprise handholding ability, similar to the FDs, where they will really be that partner to ensure full implementation. That's how you expand margin and have some form of business in the long term. For sure. Yeah.
Speaker 1And I think it's going to be a tough business in the very long run, but today you can have extremely good cash flow selling these
Speaker 2data sets. Can I ask, when we think about that kind of implementation working with partners so closely, you've said, and Lynn has said too from Fireworks, about the importance of owning your own intelligence. Can you unpack how this looks? Because specialized models for companies in this way, the kind of jury's still out. We have Harvey who says that it's fantastic and it's been amazing. And then you have Legora who say, "Ridiculous, the underlying models move so fast. It's the wrong strategy."
Speaker 1Yeah. For some stuff, it's good to own your own intelligence. For other stuff, I don't think it matters. We have a lot of customers who own proprietary models on model too, and they're customers, they use a proprietary model. I don't think it makes sense for everyone, to own their own intelligence. Where does it make sense for you to versus not? I think for certain use cases, you can take an open source model and you can fine tune it, and you can get really good performance. And the nice thing is, you know what the fine tune model is based on. It's not going to change. They're not going to take away the model from you. It's nice to have that consistency and the ability to continue to tweak it without the underlying model changing, which has happened in some cases with the Frontier Labs. That's nice. I support that, but I think there's many other things that matter to people. Owning your intelligence is just one thing out of many. I think latency matters. I think cost matter, performance. I think hackability. I think having a platform of a lot of adjacent services. These things matter too. Owning your own intelligence, yeah, I'm supportive, but there's many other things. Isn't that going against your business? I love the
Speaker 2honesty, but dude, it would be in your favor to say, "Yes, every company is going to need it, and we're the partner to help you." I teed you up
Speaker 1there. Well, not really. If you think about TypeSafe with Jav, it's a highly proprietary model. I want people to use Jav. It runs on modal. We have many customers that themselves are proprietary model that are hosting on modal that are serving their customers. I'm very in favor of that too. I think the beauty of modal is always that there's a general purpose platform, and we support so many different things. That to me is the beauty. If you want to use open source models, that's great. We'll help you, and there's many customers that are building. There are a lot of companies that are building incredible AI applications on top of open source models, fine-tuning them, reinforcement learning, have a bunch of adjacent sandbox use cases or training or whatever, but there's a lot of companies that have built highly successful businesses, Suno, Runway, TypeSafe, and many others running their own proprietary model on modal, and their customers don't care. That, I think, puts us in a really good spot.
Speaker 2I want to ask two more questions before I do a quick fire. Okay. Model routing, as the dear Americans say, is that a defensible business?
Speaker 1Did you say in the UK, rooting?
Speaker 2Rooting. I love that. Well, I mean, we are actually the original language. America just took ours and bastardized it. But yes, my Andrew, is that a defensible segment of this value chain, or is that a commoditized product that everyone just builds themselves?
Speaker 1I think it's possible to build a successful business in model routing. I think you can do that today as a standalone product. I wonder if long-term it has to be part of a more comprehensive platform. At the end of the day, I think what these companies want, they want evals, they want ways to fine-tune, they want more control over performance, like latency, throughput, like all these things. And I think it's one of those things that kind of makes sense today. These are very good businesses. I invested in OpenRouter personally, and they had a great outcome. I actually think they could have done even better on a standalone basis. But I think in the very long term, it feels like one of those categories, but that's my view on everything in AI. It should really be a part of a larger, more comprehensive set of tools.
Speaker 2Congratulations. You're on the OpenRouter board, and the acquisition offer comes in. Please make the case for why they would have done that. Yeah. Please make the case for why.
Speaker 1I think OpenRouter, the growth rate and the revenue, and the brand, frankly, they have a really strong brand. I mean, there's a lot of like, there's 500 other competitors, but the space is growing so fast, and there's so much revenue, I would have told them to keep going. Final one. I think
Speaker 2a really telling question is, what do you believe that does not go in the favor of your business? And so, for example, I think that seed investing today is kind of dead and bullshit. This idea of like two on 10, or slowly build for 18 months.
Speaker 1It's kind of bad, like it's kind of bad if you want to make money, right? But it's great if you want to develop a relationship with engineers or, I mean, with founders.
Speaker 2Sure, but that goes against what I do.
Speaker 1I'm interested. You're against the relationship with founders?
Speaker 2No, I'm saying that I think the death of seed investing, the two on 10 and build for 18 months is dead. Yeah, I understand what you're saying. So what would yours be?
Speaker 1I don't know. I mean, I think one thought I have is like, obviously, at some point, this AI stuff is growing so fast, and we're here, and we're going to go to here, and then there's going to be a pullback. You know, 20%, 30% in the stock market, everyone's going to freak out, and like, people are going to say it's the end of the world. I'm almost certain it's going to happen, but like, I'm so bullish, because then we're going to keep going. And like, you know, like, so that would be like one thing that I'm like, you know. I'm okay with that, because I think the long-term opportunity is so fucking big. It's like so large.
Speaker 2Do you take secondaries? Very little. Why?
Speaker 1I mean, I personally don't need a lot of money. I'm pretty happy with life, and I have enough. So personally, I don't know. I actually support it for my employees, though. It's quite important, actually, as a founder, that you make the equity worth as much as possible. And one of the ways to do that and to tell future candidates that, no, this isn't just paper money. This is real money, is to have a precedent of doing employee secondaries. And you can point to last round, we let employees sell this much or whatever, and, you know, up to 10%. Like, I think you have to have guardrails. Like, you don't want employees to sell too much. You want them to still be very, you know, have a lot of skin in the game and be very incentivized. To make the company successful. But I think it's a way to, like, you know, like, create a very liquid, hard currency out of the equity.
Speaker 2I totally agree. And as you think about a talent marketplace that's ever more competitive, it's free. It's extremely competitive out there. Is it nuts?
Speaker 1It's nuts. It's nuts. I mean, you know, like, the offers we see for, we've lost new grads going to, you know, some of the labs for, like, talking, you know, 20 plus million. I mean, these are exceptional people.
Speaker 2Exceptional. What makes an exceptional, exceptional new?
Speaker 1You know, like, just fucking sharp people. You know, these are like, I know it when I see it. Like, they're just, you know, they're just extremely hungry, hardworking. And where do they go? They know everything about technology.
Speaker 3I mean, they go to some of the big labs. Is that it? They go to open air announcements? Yeah, the big labs. Yeah, they pay a lot. But is that where the talent comes from? There's, like, a few other ones. But, I mean, mainly those people are known for paying a lot of money.
Speaker 1And you won't pay that much. We pay a lot. We can go very aggressive. We want to be out there paying $95. That's a fifth percentile in the market. And there's, like, a few companies that I know, like, they're probably going to pay a little bit more. I think we have way more upside in the equity. And going back to what I said about secondaries, I think it's important that, for that reason, also, like, to create, like, a real tangible value in the currency, in the equity that makes us, you know, our offers seem very, very strong. Plus the fact that we have a lot of upside. I mean, you said it, right? Like, our stock seems cheap. Like, I kind of agree. There's a lot of upside in our stock. So, that enables us to not necessarily have to match with the labs, but I think, you know, create a compelling sales pitch. For why our comp package is, like, basically better than the labs. And we're willing to go very far to get the best talent.
Speaker 2Okay. We're going to do a quick fire round because otherwise I could talk to you all day. You have a great angel portfolio. Open router. You mentioned lovable before. You can put all of your money into only one.
Speaker 1I love Turbo Puffer. I know Simon really well. He's a Danish guy. He has a Scandinavian connection. Just a fantastic entrepreneur. I just love the business. How big can that business be? That's a good question. I mean, it is, like, somewhat specialized. They're doing vector databases. I think it could be a very large category. If I ran that company, I would have 500 different ideas of how to expand into many adjacent things around databases, data warehouses, other types of querying system. Like, there could be a large category.
Speaker 2What have you changed your mind on most in the last 12 months?
Speaker 1Probably the magnitude of the sandbox space. I think it's kind of insane. Like, I look at the revenue and it just keeps going up. And I'm like, well, clearly it has to stop right now. And I think I'm, like, doing some sort of reverse psychology on the revenue because it just keeps going up. I'm like, what is going on? It just keeps growing and growing. Like, I've been wrong so many times. I was like, you know, in a good way. Like, you know, and so it's incredible how large that space has gotten.
Speaker 2What do you think is the most overheated category
Speaker 1within AI today? Oh, boy. I don't know. I don't think anything is overheated. I will say, like, one of the things that I'm a little nervous about is companies that are dependent on continual infusion of equity capital. I would not invest in them. And there's a lot of them that I would be nervous about anything where, like, if there's a venture, capital pullback, they will struggle to raise more money. I think it's important that you raise every round as if it's your last round. And there's, like, a real path towards commercialization and revenue.
Speaker 2You've got two kids. Two kids. Biggest lesson on being a good dad and being a good CEO?
Speaker 1I mean, sometimes I feel like it's kind of similar. Like, you mean, like, in terms of, like, how to balance it? Or do you mean in terms of just, like, being a good dad?
Speaker 2What does it take to be a good dad?
Speaker 1Setting them up for success in life. And I always, like, think about, like, the right amount to push the kids. I'm not a tiger dad. But, like, I think. I think a little bit you need to push the kids a little bit to, like, get them exposure. I don't want to create, you know, in the future a situation where my kids come back to me and say, hey, dad, I wish you'd made me do X. Or I wish you'd made me do Y. But I also don't want to create a situation where my kids come back to me when they're 20 and they say, hey, dad, why did you make us do X? Or why did you make us do Y? So a little bit of pushing. I make them play piano. I don't know why. But, like, I think it's, like, a good way to, like, learn something that I hope, you know, when they're 20, 30, they will actually appreciate. They don't like it that much. But I think stuff like that. You got to push them a little bit, but not too much. My parents are very lax. And, you know, I discovered math and programming. And they were like, Eric, why are you doing so much math? Like, you should go out and play like the other kids. So in a way, I actually wish they would have pushed me even harder. But I pushed myself so hard anyway. So it doesn't matter. So that, like, right amount of pushing.
Speaker 2Why did you push yourself so hard? Sorry, you're actually from a nation where you are told that it's, like, bad to stand out. I'm forgetting that word, but I had a Swedish girlfriend.
Speaker 3The Jante Law. Yeah, where it's, like, it's bad to stand out. Yeah, don't think you're special.
Speaker 1Yeah, why? But I don't know if that's true. Like, I think there's a lot of misconceptions to Scandinavian culture. I do think there's a lot of, like, respect for people who follow their dream and, like, do things. And, like, whether, like I said, you know, it's music or writing books or, you know, making furniture or art. Like, or being an entrepreneur. Like, I actually think there's a lot of respect for, I think there's, like, you know, self-actualization and following your dreams. It's, like, kind of highly regarded. And I think, you know, like, that's the flip side of having a very lax culture is, like, you kind of pursue people to follow their dreams. And I don't know. My dream was, like, doing math and programming. I love that shit. So I did that.
Speaker 2Unless you're from Norway, where a tax structure exists that basically makes it impossible to start a company.
Speaker 1Yeah, I mean, Norway has some issues.
Speaker 2They also barely work. Yeah, this is very true. But we do have a sovereign wealth fund.
Speaker 1Yeah, they are rich.
Speaker 2Thank God for that.
Speaker 1Maybe it's, like, the first post-economic society. I don't know. Literally almost. In certain, in multiple meanings of the word.
Speaker 2Final one for you. What are you most excited for? It can be anything. Like, I look at. Drug discovery for kind of chronic conditions. My mom's got that.
Speaker 1Actually, drug discovery is, like, one of the applications I think is so incredibly cool. Like, I can't wait to take AI-generated drugs. You know, I'm, like, old and my skin is getting bad. And, like, I don't know. Like, you know, every time I drink a beer, I feel, like, horrible the day after. Like, I don't know. Give me all the AI-generated drugs I can take. Like, I want to live long. I want to live a healthy life. You know, and I think. Do you think that is actually a realistic future? I think so. But I think with the FDA and other things, it's going to take a while. But I think there's just so much incredible progress today. Like, in AI-aided drug design. I think there's so many cool stuff. So many things.
Speaker 2Dude, I've so enjoyed this show in a way that I honestly wasn't expecting you to be this fun, open, charismatic. You've been amazing. So, thank you so much.
Speaker 1Thank you so much for having me. This is a lot of fun.
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