Why $1B Exits are Dead

And Thropic and Open AI are adding more revenue per month than meta, Google or Microsoft. And I wouldn't be surprised if the combination of those two companies is doing 200 billion of revenue run rate. Between 2020 and 2024, top 1% exit started at $10 billion. We updated those numbers in February this year, $20 billion. We just updated them yesterday. It's now at $32 billion. So we've 10xed. Yeah. Oh, that's amazing. Kind of 24 months. When the models get really good and the products that get built around them get really good, you see this takeoff and usage happening. Oh, we in an AI bubble. I feel pretty confident saying that we're not in a bubble right now. The one thing that could shift that would be. Over the last decade, venture capital adapted to companies becoming larger and staying private longer. But AI may be accelerating that trend dramatically. The frontier labs are already adding revenue at a pace comparable to the largest software companies in the world, despite being early in real enterprise adoption. At the same time, the infrastructure supporting this shift, compute, power, data centers, and talent is increasingly constrained. That combination is forcing investors to rethink some of their core assumptions around scale, defensibility, value capture, and even how venture capital itself works. A16Z is David George and VencapCIO David Clark, discuss AI, venture capital, and the next generation of massive technology companies. I can't think of a time in my career where I have changed my mind about things at a faster clip, which is good, but is also humbling, right? Two big areas are scale and value capture. So on the scale side, the world kind of changed in November as it relates to our business, and I think sort of productivity in the workforce. The way that we thought about much of the AI work that was happening before that was a sort of nebulous promise in the enterprise, but we probably were contextualizing it around things like the cloud, and software companies, and productivity enhancement. And then on the consumer side, you could think about AI companies like a consumer business, so many users they have, and what the price is, and how big that can get. And by the way, I think that's going to be much bigger than people expect to, which we could talk about. As of November, I think all of our priors shifted around what is actually going to happen in the enterprise, but just maybe to contextualize what's happened since then, basically, and through open AI are adding more revenue per month than meta, Google, or Microsoft. They are already at that scale of revenue getting added, and actual diffusion of this technology into the real economy is tiny. It's like less than 5%. Yeah. Now, within coding and in tech forward companies, yes, it's much more advanced, but as it relates to every other function, and the enterprise full utilization of the capabilities, we're nowhere right now. So if you pair that up with the fact that they're already getting bigger, in terms of revenue added than the hyperscalers, and you're at less than 5% diffusion into the economy, I think the outcomes are going to be extraordinary. So the thing that we've started to try to look at to gauge what can possibly happen, like what's the upper bound is enterprises are going to have to pay for this somehow. Yeah. And so if you just look at the Fortune 500 or the S&P 500, they're actually pretty close. They generate like two trillion of profit per year, the collective, and I wouldn't be surprised if the combination of those two companies is doing 200 billion of revenue run rate by the end of this year, not to mention people using open source, other vendors, so you can add even more on top of that. So we're already talking about like a 10% profit into the Fortune 500. So I think the upper bound is going to be where the dollar is going to come from. And one of the implications to buy this stuff, one of the implications of this is we had all these theories, white open source, and local, we're going to be really important. And it turns out that cost is going to hit us in the face and make them really important sooner than we thought. So scale, we've updated our priors to get really piled on this outcome thing, on the size of the prize, and the scale. And you can see the early signs of it in the numbers. But basically almost no diffusion into the real economy. It's going to get great for all these other functions. By the way, what's happened in coding, you can kind of start to see it in some other white color jobs. So like it's starting to happen in legal. Legal space is much smaller, obviously, than coding. But when the models get really good and the products they get built around them get really good, you see this takeoff and usage happening. And I think it's going to happen in a bunch of different functions in organizations and verticals over the next 12 months. And how much of that do you think is going to be native AI applications? Because I kind of always go back to critics and point around the first three or four years, you see these skewer morphic applications. We've seen that. Most people are using AI to do their existing job in a way that's more efficient, faster, cheaper. But we're kind of starting to see some of the native applications come in with particularly around a gentigay eye. How do you think that alters the landscape? So I think the big thing that's going to change in enterprise is we're kind of nowhere on how companies are run differently today. And so the most cutting edge companies, I happen to think that what's happening with some of the way off things that we're seeing is kind of like trimming of previous fat. Like I don't think it's actually efficiency gains. And by the way, there's some really interesting thing that's happening inside these companies where most of the resource devotion, at least for really good companies, is actually on product and new things as opposed to like automating the way they're run. So like they only have so many resources. And the best ones know that the size of the prize of getting something right on the product side. And by the way, the best people at those companies, best engineers, want to work on that side of things. The size of that prize and the best people are going to work on that. And so that's kind of where most of the work is happening. The more mature companies would be the ones who probably would be better suited trying to automate the way their business is done internally. But they're the slower adopters. There's kind of this latent opportunity that we see in our portfolio companies to get more drive efficiency gains itself. But it's not the best people working on it. And it's not where the incremental dollar is going to go just yet. The most cutting edge folks inside those companies who are trying to do this that I've talked to are kind of in the documentation phase, which is just turn everything into markdown files, have as much context captures you can possibly get and then see where you can kind of still manage your business appropriately, not make sacrifices on customer experiences, but drive efficiency. So we're very, very early in that. I would say that the native AI companies run themselves totally differently. The founders are just built different. One of the things that we've observed about the previous generation of founders, like if you book it SaaS companies, for example, I've written about this, like we didn't realize how inefficiently they were running until until much later. It's like the chemical quickly they could grow or how much more quickly they could grow. And by the way, it turns out that the magnitude of their market we're already seeing is just is so small compared to what we've seen the models. The model companies are adding more than the entire public software universe in terms of revenue added combined. And so they're not particularly tightly run, but they had great business models. And so they could grow and they could do well and everyone had a mandate to buy more software and had count grew. And so everything kind of worked out. The new companies are very lean, very aggressive, and they work all the time. And so it's fun to see like the most cutting edge companies when you go in, all their researchers are sitting there and they're whispering in to the other. So they're not typing. They're not even typing. Like they're so efficient, they're like whispering in and they're running swarms of agents. And I think that's kind of going to be the future. It's just really early. I think the skeomorphic phase, I would say it's like everything that is reactive today. Like I think there's going to be a shift to proactive engagement, both in consumer and in enterprise. And we're starting to see it in some of the cutting edge early stage companies that we're doing, but it's really, really early. When I think of our priors sort of 12 months ago, there's a couple of things that I think have kind of changed. One's been reinforced, which was we always thought that the largest companies were going to continue to be an order of magnitude larger than we'd seen in prior cycles. Yes. And if anything, that's accelerating. So you've put out some data around the size of a top 1% exit doubling every five years or so. So between 2020 and 2024, top 1% exit started at $10 billion. We updated those numbers in February this year. And a top 1% exit for 25 in the first two months of 26 was then $20 billion. And if you look at just the exits that have closed, it's now at $32 billion. So whizz is the threshold for the top 1%. And then if you then think about open AI and anthropic coming in, potentially we could be north of $100 billion by September. It's incredible. So we've 10xed over this space. We've got 24 months. What a top 1% exit looks like. Yeah. I mean, just the combination of those large companies, I think is larger than the entire Russell 2000. So I'm not mistaken. And so the magnitude of these companies has just grown so great. And look, we've built our firm kind of in response to that. We believe the next subsequent generations of companies that get bigger as new trends happen are going to be bigger than their predecessors. We actually did a similar analysis where we looked at all of the VC backed IPOs that happened over the last six years. And if you sum all of them up, they're a little over a trillion dollars. That's probably going to be smaller than any of the three of the large IPOs that we expect to happen. So I'd say the observation is the outcomes keep it bigger, but it's happening much faster. Yeah. The pace of value creation is, I mean, let's draw a picture. Particularly something like whizz and curse that you'd kind of like four, five, six years, and to get from nothing to well, $30 billion and then potentially $60 billion. I would say similarly, there's a lot that we talk about all the time about deployment pace and how big our funds are and things like that. And if you extrapolate out and you say, hey, previous trends are kind of 10x smaller and the outcomes get much bigger. And by the way, there's a tremendous amount of concentration in the companies that are the winners. Now we believe is a great time to be in the market investing. You know, Chad, GKT moment is I think less than four years ago. So we're just now seeing some of the most interesting things happening on the back of the foundational technology. We could have a long talk about who captures it. Another thing where prior change all the time. But we believe now is the moment where the companies are getting created that are going to be the generational companies of the next 10 years. So the other thing that where my prize have kind of shifted a little bit as well is just around the speed of change and what happens to the the defensibility of the leading companies. Because we've seen in prior generations that it's not necessarily the first movers that ultimately captured the economic value of a market. So you know, think Google wasn't the first search engine. Facebook wasn't the first social media site. And and one of the things we track is you know, every Air Forbes comes out with a AI 50 start-ups list. And what was really interesting was, you know, from from last year to this year, 40% of the companies that were on that last year dropped off. Well, so that the half life of these companies feels kind of incredibly short. Yes. So, you know, that I think I think where our kind of prize have evolved a bit is, yes, we think the outcomes are going to be much larger. But trying to predict who captures that feels like it's getting much harder. Yeah. Is that something that you guys are seeing like internally in your portfolios? Yeah, it is getting much harder because the shift in the technology has happened so much faster. And so, you know, we always talk with our founders about, you know, the shifting sands underneath you, like that is very, very true. And our priors have been updated a ton about where value is going to be captured. You know, when we first, we invested in, as you know, OpenAI before Chattraputee. And, you know, there were moments of time in the early days where we said like, model companies are going to be everything. There's never going to be any more application companies. They're all going to go away. And then we went through a cycle where we said there's going to be application companies for everything. And the model companies are just going to be APIs. And then now we're back in this moment where the model companies are kind of lagging their way up into the application. And, you know, this is their biggest way to drive stickiness. So as it relates to assessing something's place in the world, first of all, like right now, you have to be in the token path. Yeah. Like that is the number one thing that we're looking to for our companies. And the reason that's so important is what I had said earlier. So, there's actually cost pressure happening at buyers of technology already. Like very, it's happened very fast. So, there are not going to be increasing budget for things that are like previous generation software. In fact, they can't even cover the growth in their cost that is happening from AI with that, with reductions in that. So, there's going to be pressure on those. And, you know, honestly, it's probably going to have to come from either higher prices that they can charge or restructuring of the labor force. The biggest driver of where value is going to get captured right now is I would say something that is totally unknowable, which is what is the market structure of the model companies? How much competition is there? If there's a couple at the frontier, token prices will probably be higher. Yeah. If there are five at the frontier, token prices will probably be lower. Token prices being lower probably is better for the overall economy because there's not this pressure to kind of restructure the labor force as quickly as things get really, really big. You know, right now, the number is smaller. It's not five. There's a tremendous amount of any elasticity for frontier intelligence right now. There's also a question of how much does that change over time? Like, are a lot of the jobs that can be done, fine to be done with previous generations of models? That's not the way anyone is consuming tokens today. Yeah. So, you know, that's an unknowable, the market structure is an unknowable. You know, what role does open source play? You know, that's a tenuous situation. You know, how much can you run locally? How much can you run with small models? Like, these are all the open questions that I think will determine who captures value. But for the broader ecosystem to thrive, it's probably competition that keeps token prices lower. Yeah. So, a couple of my colleagues are in China at the minute. And it's been really interesting just getting their feedback, you know, relative to what we're seeing in the US. And one of the things that they were saying was that the leading LLMs in China are probably six months behind where we are in the US in terms of the capability of their models. But they're 10x cheaper. Yes. And so, like, one of the unknowns, I think, at the minute, and is, you know, to what extent are they? What percent of the market will those type of companies capture? How much of what we end up doing over the next decade will need to be done through the very frontier models? Yeah. And what can be captured by that next level down? And it's, you know, it's the classic innovators dilemma, isn't it? That you get the next generation product that can do 80% of what the frontier product can do, but a 10% of the cost. Yeah. And over time, those capabilities extend, and it's hard to be at that frontier. Yeah. As of right now, we've been surprised at how voracious the appetite is for the absolute frontier. That's probably partially because we're not in like the optimization phase. And yet, but the optimization phase is probably going to happen sooner than we would have expected. It has my sense. There's all these other open questions about, you know, the future of open source, like how capable are these players of distilling the big models, like the big model companies don't want their models to still. Yeah. And so, you know, it probably costs in the order of like 2% of the actual training cost, pre-training cost of a model to distill it. And so, you know, if that continues to hold and be possible, you know, that probably boats well for open source. If not, it probably doesn't boat well for open source. So as of right now, yeah, you're exactly right. The sort of per token cost like for like is going down more than 10X year over year. But the appetite for tokens in the frontier is massively exceeding that in terms of dollars. Yeah. Yeah. How do you factor that in when you're then thinking about valuations of these companies? Because I think one of the concerns that I would have is a bit like in 2021, I think 2021, the market there was kind of peak emerging manager because a lot of these managers had done the seed rounds, you know, established firms were coming in and writing things up, six months after the seed round had done and there was basically a zero loss ratio. And, you know, we know that's not how venture works. It feels like we're in a little bit of that situation today, but with the more established firms because it's the established firms that have been by and large capturing the early breakouts in the AI space. But when I look at it, like historically, when we look at our early stage firms, there's a 60% loss ratio. So 60% of deals don't return the capital that was invested in them. If I was looking at the loss ratio for the last couple of years in the AI space, I mean, it's not it's not zero, but it's probably single figures percentages. And like that's not sustainable. Yeah. So how do you kind of think around around where we are kind of in that cycle today? Because at some stage, like the laws of gravity will reassert themselves. Yeah. Maybe it's helpful to sort of explain our philosophy at the early stage because we also don't want to target a low loss ratio. Like that's not no, we're not taking the burden on risk if we have a little. Yeah. And so, you know, we joke all the time, there's a, you know, a prominent VC around in our ecosystem. And, you know, one of his big points of pride is that he's never lost money on a deal. And we're like, that's not it. That's not a point of pride. Like that's a horrible data point. Like that's not what you want. Yeah. Yeah. That's a P E F. Yeah, exactly. And so like, this is certainly you can make the case that you're not taking enough risk if that's the way you approach it. The way we've approached it historically, and this is sort of a, you know, critics and philosophy is, you know, any major space where there are multiple very talented entrepreneurs building where we think there's tailwinds where we have a point of view on the technology that it's good. We should pick the best founders and we should try and back the the leaders at the early stage, the market leaders. And, you know, if the space happens to work out and we've got the leader, excellent. If the space happens to not work out and we have the leader, no harm, no foul. Actually, that's part of our business. Yeah. That's what we should be doing. Yeah. Yeah. The, the bad box of what I described is the space works out and we pick the wrong one. Yeah. And those are the things that we really scrutinize and we, and we try and make sure that we get right. Yeah. Um, so, you know, I don't know, there's, there's many examples of spaces that didn't quite work out. Um, but we did fact the leading entrepreneur and their talented entrepreneurs and they were competing. There were lots of players in the space. That's totally fine. Yeah. With us. Yeah. And so that's the philosophy that underpins how we could have a loss rate that, you know, and, and, and sort of how we think about balancing taking an appropriate amount of risk. Obviously, that's a little bit different at the growth stage. Yeah. And so, you know, we, we shouldn't have a side of a loss rate. As of right now, everything is so early that we don't know. Yeah. There's all these unknowns about who captures value. As you said, um, I'm sure loss rates are going to go up over time. All we can think about is how we build the firm. Um, and, you know, the results will play out over time. Again, we think there's just massive power laws. We talked about it in the winners. They're going to take care of themselves and, you know, we'll do our best for the things that don't work out. The way that we're building our firm, I think is catering to what the entrepreneurs want. So, you know, you asked about, you know, emerging managers versus, you know, large platforms like ours. The reason we built our large platform the way we have with a lot of scales, because that's what the entrepreneurs want. That expresses itself in high wind rates of deals and large ownership of things that matter. One of the consequences of how fast this has happened, this AI wave, is the companies run into big company problems very early in their lives. So we need to adapt the way we build our firm. That's part of the reason that we've scaled up, some hiring. We're building out a much broader platform that includes things like international, like channel, where we've already got experts in pricing and how you scale a sales force and all those things. In addition to all the things that we've always done for companies, the reason is the companies are staying private longer and the companies just meet it really early in their lives. Like cursors and examples, billions of dollars of revenue and they're very small. And it's very early in their life. The previous generation of technology didn't happen so fast. So they didn't encounter things like major business deals. They had to negotiate, supplier relationships that were complex, cloud deals, international expansion. It's all just happening so much sooner. I think part of the market share gains, if you will, that we've seen is just, this is entrepreneurs expressing their preferences. Yeah. So one of my colleagues was at a conference yesterday that was run by the UK venture capital association and they surveyed the audience saying, what do you think about AI valuations today? Too high, about right, too low. 80% said too high, about 6% said too low. As I think of the AI universe, it feels like that's probably about the right balance because I think 80% of companies probably are overvalued today because we know most of the companies aren't going to work historically. There's probably going to be a small subset of those companies that are massively undervalued because they're the ones that are going to emerge as the leaders and we'll see multiples of where they are, where they're being valued today. I think from an LP perspective, I really would struggle to be in your shoes today because having to pick those individual companies, I know you can kind of put a portfolio together. One of the advantages I think from being in the LP seat is that we can have a really broad and diversified portfolio of the potential outliers in that AI space and we know historically that basket will increase in value over time even as the majority of those companies might fall away. Yeah, yeah. Look, this dynamic is exactly why it's so important for our business to be centered around early stage. And so we have to do the early stage investments in those companies that ended up working out and then many won't work out, but that's the nature of the beast. And so in our business, it kind of starts and ends with how successfully early stage business is. And then at the growth stage, a lot of the stuff that we spend our time trying to think about is similar to the venture stuff that I described, our lens on the venture side, but also how much do we invest in a given company and a given situation. And so, slugging percentage is very well covered as an industry topic, but we really have to get slugging percentage, right? Because of that sort of risk dynamic that you described. Yeah. Yeah. We also get a lot of questions around are we in an AI bubble? And one of the things that it feels that feels different today is that typically bubbles are characterized by excess supply destroying the economics. Today, we are in a situation where there's scarcity. There's not enough compute, not enough memory, not enough data centers, not enough power. It feels like we are we are supply constrained, not demand constraints. And how do you think that kind of changes the shape of the cycle? First of all, it's probably a healthy thing right now that we're supply constrained, only in the sense that it probably makes it less likely that we have a bubble. I'm less confident that we won't be in a bubble three years. But all I can speak to is where we are right now. We're massively supply constrained. You can't get data center capacity at scale until late 28 early 29 right now. And that's just a fact. I think that's going to get harder. I think we're probably a year behind schedule, what people would expect for data center build up in the US. So we're already behind. We're supply constrained in pretty much everything in the supply chain for the data center. Part of that is TSMC showing restraint and trying to be balanced. But part of that is just other components that are hardware that are hard to manufacture and spin up to meet demand. I think this data center resistant stuff is absolutely crazy. The arguments that I see are just wild. The best data center operators are going into communities and saying we're going to fund a nature preserve and we're going to fund high speed internet in your school. We're going to make it beautiful and we're going to create a bunch of jobs and we're going to create a bunch of tax revenue and that should all be good things. And then we're met with resistance like it consumes too much water. And I'm like, well, I'd rather eat four or five fewer almonds and make sure that I have capacity to do all the things that I need to do. My yard consumes a lot more water than data centers. So we'll see if there's sort of melting resistance in this and it has an effect on the ecosystem. But I think it's more likely we remain supply constrained for the next three years than we end up in bubble territory. I would say the one thing that could shift that would be massively smaller models in that probably comes from like an algorithmic breakthrough of some sort. We do have companies that are working on that. If you just start with the human brain, the human brain is just far more efficient at learning. And requiring context for intelligence than models. And so I would expect there to be some shift in that. Everything won't be so token-consumptive in the future. If we had some massive unexpected step change in that, maybe we could end up in an oversupply situation. But I think that's unlikely in the short term. And then if you look at the build out expectations over the next four or five years, if we spend five trillion of cat-backs, can you get one or two trillion dollars of revenue as a return on that? And we can debate how much of a return you should get. But that's probably a reasonable expectation. If the two big model companies alone end this year at $200 billion of revenue run rate, I think everyone should feel pretty comfortable with that equation over the next few years. Again, it's hard to say what's going to happen with the supply side. And the supply is obviously, I think, what would drive a bubble. But I think we're so far from it right now that we feel pretty confident, you know, investing right now. Yeah. We touched earlier on just the size of companies today. What will that mean for the public markets kind of generally, do you think that is there enough capacity in the public markets to consume that and to digest it? And what does it mean for the next generation of companies that are coming along? Is there going to be some intergestion post those IPOs? Yeah. Look, I think having these companies get into the public markets while they're in hyper growth is an excellent thing for the investor community. It's really, really good. You know, there's been all this debate about the inclusion of those companies into indexes, for example. And my parents' retirement funds are in index accounts. My hope is, you know, and it seems like it's going to go that way that they'll be indexing inclusion and brought around our ship. And so I think it's a good thing. You know, we've been going through this shift over the last, you know, whatever, 20 years where the number of public companies has shrank in half. So I think this is going to be a good shot in the arm to bring some very high growth interesting stuff into the public markets. I've talked about this a lot. If you exclude the data center supply chain stuff right now, there are very few companies that are growing fast that are available for people to buy in the public markets. You know, the bag seven are all growing, sub 30% at this point. You know, all the software companies are growing sub 30% you know, all the internet. Yeah. Palanty is the only one that's actually the only one growing, you know, whatever 70% or whatever it is. So I think it's, I think it's good for the market to get some high growth. And so they just happen to be at larger absolute values. But again, I think that the, the future of those companies is probably hyper growth for many, many years. And, you know, we'll look back 10 years from now and say, wow, look at how big the biggest companies got in the same way that we have about the mag seven. Yeah. Where we say, wow, you never would have thought 10 years ago that we were going to have a four trillion dollar company or five trillion dollar company. Yeah. But here we are. Yeah. So, you know, I think there'll be some, some shifting of ownership of things to make space for buying those companies. But I think the market's really going to be able to bear it. And so it's a great thing. Yeah. One last thing I'm keen to get your thoughts on David is like, if the optimistic case for AI is right, like what do you think the VC industry looks like in five years time? That's a great question. There's so many unknowns that drive this. If we can't speculate on a podcast, you know. Exactly. Yeah. There's, yeah. The thought leadership of totally unknowables. So the number one thing that I think is going to drive the next five year structure of our industry is what I had talked about the sort of market structure of the model industry and the labs. You know, the role of the source play is how much competition for tokens there is. You know, there's the Bill Gates quote, which is, you know, the value of a platform, I'll butcher it. But it's like effectively, you know, if you're a platform the value of the companies that are built on top of you. need to exceed the value of the platform itself. And so if that's the future, I'm very optimistic that we're going to have a massive wave of really valuable companies that get built on top of tokens, you know, an AI and intelligence, and we're at the very early stage of seeing those. So we just need to be in position to back those founders, you know, if you look at sort of health of our business, like we measure it by, you know, are we, are we seeing and doing the best companies at the early stage and then following on and back into those founders on and on, you know, time and again. And that all looks really good. But I think there's, you know, this sort of market structure question of the, of the labs and what happens to token costs that's probably the biggest driver of how value is going to get created in the VC industry in the next five years. I tend to think that there's enough smart people working on this, that it's going to work out. It's probably an end where the labs are extraordinarily valuable and then there's this massive ecosystem of companies that are built on top of intelligence that are really, really valuable. You know, and then, and then lastly, I'd say some of the biggest outcomes, probably the biggest outcomes, you know, tend to come from the consumer side. We've spent a lot of our time talking about the B2B side. We're very early in shifts in consumer. One of the things that I'm most excited about is, you know, the last 10 or so years has basically been a story pre-AI of time spent getting captured by all the big tech companies and then competing with them was extremely hard. So I'm optimistic that with all these technology changes and breakthroughs, we're going to see a shift in time spent, you know, consumer attention, which I think will probably create, you know, really extraordinary outcomes. Yeah. I mean, I've been investing in VC funds for 34 years and like this is, by a distance, the most exciting and scary time that I've been involved with. I just find it, you know, the pace of change is, you know, a real opportunity, but you've got to get things right as well. And I just feel super excited about, you know, what we're seeing and what the potential is for venture to really be at the center of, you know, changing the way we live and work. Yeah. Yeah, same here. I mean, the opportunity is so great. I think, you know, changing the way we live and work, I haven't even feel strongly that it's going to societally make the way we live and work a lot better. And so, you know, I think the way that we do things is going to change a lot and I think there's going to be a lot of value that gets created out of that. Cool. Thanks for listening to this episode of the A16Z podcast. 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