← The a16z Show
The a16z Show
Why $1B Exits are Dead
00:33:31
David George, General Partner at a16z, and David Clark, CIO at VenCap, discuss how AI is reshaping venture capital and the technology industry itself. They examine why today’s AI companies are scaling faster than any previous generation of startups, and why the eventual outcomes may be significantly larger than most investors currently expect. The conversation covers frontier AI models, coding agents, open source competition, data center constraints, and who ultimately captures value in the AI ecosystem. They also discuss what these shifts mean for venture capital itself, including larger company outcomes, faster value creation, and the growing challenge of identifying durable winners in a market evolving at unprecedented speed.
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- [00:00]
David George
Anthropic and OpenAI 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.
- [00:14]
David Clark
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 10 XED. Yeah, over the space of kind of
- [00:28]
David George
24 months, when the models get really good and the products that get built around them get really good. You see this takeoff in usage happening.
- [00:34]
David Clark
Are we in an AI bubble?
- [00:35]
David George
I feel pretty confident saying that we're not in a bubble right now. The one thing that could shift that
- [00:40]
Podcast Host
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 Vencap CIO David Clark discuss AI, venture capital and the next generation of massive technology companies.
- [01:31]
David George
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 like 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. How 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 too, which we could talk about. But 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 Anthropic and OpenAI 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% now within coding and in tech forward companies, yes, it's much more advanced, but as it relates to every other function in the enterprise, full sort of 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.
- [03:16]
David Clark
Yeah.
- [03:17]
David George
And so if you just look at the Fortune 500 or the S&P 500, they're actually pretty close. They generate like 2 trillion of profit per year at 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 like you can add even more on top of that. So we're already talking about like a 10% profit into the Fortune 500. And so I think the upper bound is going to be where the dollar is going to come from. And one of the implications, like to buy this stuff, you know, one of the implications of this is we had all these theories why open source and local were going to be really important. And it turns out that like 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 pilled 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 collar jobs. So like it's starting to happen in legal. Legal space is much smaller obviously than coding. But you know, when the models get really good and the products that get built around them get really good, you see this takeoff in 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.
- [04:46]
David Clark
And how much of that do you think is going to be native kind of AI applications? Because I kind of always go back to Chris Dixon's point around like the first three or four years, you kind of see these skeuomorphic applications that kind of come in and we've seen that at the minute 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 agentic AI. How do you think that alters the landscape?
- [05:12]
David George
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 layoff 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, a really interesting thing that's happening inside these companies where most of the resource devotion, at least for really good companies is, 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. Yeah, 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 and stuff, 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 capture as 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 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 look at SaaS companies, for example, I've written about this, we didn't realize how inefficiently they were running until much later.
- [06:56]
David Clark
It's like how much more quickly they could grow.
- [06:59]
David George
Yeah. 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 in 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 headcount 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 users. 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 gonna be the future. It's just really early, I think the skeuomorphic phase, I would say it's like everything that is reactive today, like, I think there's gonna 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. Yeah.
- [08:02]
David Clark
When I think of our priors sort of 12 months ago, there's a couple of things that I think have kind of 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. 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. We just upd. And if you look at just the exits that have closed, it's now at $32 billion. So Wiz is the threshold for the top 1%. And then if you then think about OpenAI and anthropic coming in, potentially we could be north of $100 billion by September.
- [08:52]
David George
That's incredible.
- [08:53]
David Clark
So we've 10 XED over the space of kind of 24 months what a top 1% exit looks like.
- [08:59]
David George
Yeah, I mean, just the combination of those large companies I think is larger than the entire Russell 2000 if I'm not mistaken. Yeah, 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 observ is the outcomes keep getting bigger, but it's happening much faster. Yeah, the pace of value creation is,
- [09:43]
David Clark
I mean particularly something like wiz and cursor. You'd kind of like four, five, six years. And to get from nothing to well, $30 billion and then potentially $60 billion.
- [09:53]
David George
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, you know, 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, in the, the winners. You know, now we believe is a great time to be in the market investing. You know, chat GPT 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, which is another thing where priors change all the time. Yeah, but we believe, you know, now is the moment where the companies are getting created that are going to be the generational companies of the next 10 years.
- [10:41]
David Clark
Yeah. So the other thing that, where my priors have kind of shifted a little bit as well is just around the speed of change and what happens to 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 one of the things we track is, you know, every year Forbes comes out with their AI50 startups list. And what was really interesting was from last year to this year, 40% of the companies that were on that list last year dropped off wow. So the half life of these companies feels kind of incredibly short. So I think where our kind of priors 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.
- [11:36]
David George
Yeah.
- [11:37]
David Clark
Is that something that you guys are seeing like internally in your portfolios?
- [11:40]
David George
Yeah, it is getting, 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.
- [11:56]
David Clark
Yeah.
- [11:56]
David George
You know, when we first we invested in as, you know, OpenAI before ChatGPT, 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 legging 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.
- [12:31]
David Clark
Yeah.
- [12:31]
David George
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 they're 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. 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. Yeah, you know, right now the number is smaller, it's not five. There's a tremendous amount of inelasticity 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.
- [14:06]
David Clark
Yeah.
- [14:08]
David George
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.
- [14:34]
David Clark
Yeah. So a couple of my colleagues are in China at the minute and it's been really interesting just getting their feedback relative to what we're seeing in the US and one of the things that they were saying was 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.
- [14:53]
David George
Yes.
- [14:54]
David Clark
And so one of the unknowns I think at the minute is 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 and what can be captured by that next level down. And 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 at 10% of the cost and over time those capabilities extend and it's harder to be at that frontier.
- [15:24]
David George
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 yet. But the optimization phase is probably going to happen sooner than we would have expected is my sense. There's all these other open questions about, you know, the future of open source, like how, how capable are these players of distilling the big models? Like the big model companies don't want their models to still. And so 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 if that continues to hold and be possible, that probably bodes well for open source. If not, it probably doesn't bode 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 on the frontier is massively exceeding that in terms of dollars.
- [16:27]
David Clark
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 thought 2021, the market there was kind of peak emerging manager because a lot of these managers had done the seed round. 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 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 historically, when we look at our early stage funds, 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.
- [17:28]
David George
Yeah.
- [17:28]
David Clark
So how, how, how do you kind of think around, around where we are kind of in the, in that cycle today? Because at some stage, like the laws of gravity will, will reassert themselves.
- [17:39]
David George
Yeah. Maybe it's helpful to sort of explain our philosophy at the early stage because we, we also don't want to target a low loss ratio. Like, that's not. No, we're not taking appropriate amount of risk if we have a low loss ratio. And so, you know, we, we joke all the time. There's a, you know, 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, that's not a point of pride. Like, that's a horrible data point. Like that's not what you want.
- [18:05]
David Clark
Yeah, yeah, that's a PE firm.
- [18:07]
David George
Yeah, exactly. And so like, 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, Chris Dixon 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, we should try and back 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. The bad box of what I described is the space works out and we pick the wrong one and those are the things that we really scrutinize and we try and make sure that we get right.
- [18:59]
David Clark
Yeah.
- [19:00]
David George
So, you know, I don't know, there's, there's many examples of spaces that didn't quite work out. But we did back the leading entrepreneur and they're talented entrepreneurs and they were competing and there were lots of players in the space. That's totally fine. Yeah, with us. And so that's the philosophy that underpins how we could have a loss rate that, you know, 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 shouldn't have as high of a loss rate as of right now. Everything is so early that we don't know. There's all these unknowns about who captures value. As you said, I'm sure loss rates are going to go up over time. All we can think about is how we build the firm and, you know, the results will play out over time. Again, we think there's just massive power law, as we talked about, and the winners are 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 scale is because that's what the entrepreneurs want. And, you know, they, you know, they. That expresses itself in, you know, high win rates of deals and large ownership of, of things that matter. So, you know, 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. And so we need to adapt the way we build our firm. And so, you know, that's part of the reason that we've scaled up, you know, some hiring. We're building out a much broader platform that, that includes things like international, like channel, you know, where we, we've already got like experts in pricing and, and you know, how you scale a salesforce 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 need it really early in their lives. You know, like Cursor as an example is, you know, billions of dollars of revenue and they're very small and it's very early in their life. And the previous generation of technology didn't happen so fast, so they didn't encounter things like major business deals. They had to negotiate, you know, supplier relationships that were complex, cloud deals, you know, international expansion. It's all just happening so much sooner. And so I think part of the market share gains, if you will, that, that we've seen is just this is entrepreneurs expressing their preferences.
- [21:24]
David Clark
Yeah, yeah. So it's funny, one of my colleagues was at a conference yesterday that was run by the UK Venture Capital association and they surveyed the audience saying, you know, what do you think about evaluations today? You know, too high, about right, you know, too low. 80% said too high, about 6% said too low. And as I kind of think of that like the AI universe, it feels like that's probably about like 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.
- [21:55]
David George
Yeah.
- [21:55]
David Clark
And 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, you know, where they are, where they're being valued today. And I think from an LP perspective, I really would struggle to be in your shoes today because like having to pick those individual companies, I know you can kind of put a portfolio together. But you know, one of the advantages I think from, from, from being in the LP seat is that we can have a really broad and diversified portfolio of, you know, the potential outliers in that AI space. And we know historically that that basket will increase in value over time, even as the majority of those companies might fall away.
- [22:33]
David George
Yeah, yeah, yeah. Look, this is, this dynamic is exactly why it's so important for our business to be centered around early stage. And so we, 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, you know, our business kind of starts and ends with how successful the early stage business is and then at the growth stage. You know, a lot of the stuff that we spend our time trying to think about, you know, is similar to the venture stuff that I described, our lens on the venture side. But also, you know, how much do we invest in a given company in a given situation. And so, you know, 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.
- [23:19]
David Clark
Yeah, yeah. We also get lots of questions around are we in an AI bubble? And one of the things that feels different today is that typically bubbles are characterized by excess supply destroying the economics today that we are, we're in a situation where there's, there's, there's scarcity, you know, 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 constrained. Yeah. And you know, how do you think that kind of changes the shape of the, the sort of cycle?
- [23:58]
David George
You know, first of all, it's, it's probably a healthy thing right now that we're supply constrained only in the sense that, you know, it probably makes it less likely that we have a bubble. Yeah, I feel pretty confident saying that we're not in a bubble right now. I'm less confident, you know, that we won't be in a bubble three years from now. 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 of what people would expect for data center buildup 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. You know, 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 resistance stuff is absolutely crazy. You know, the arguments that I see are just wild. You know, the best data center operators are going into communities and saying, you know, we're going to fund a nature preserve and we're going to fund high speed Internet in Your school and you know, 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 like that should all be good things. And then, you know, we're met with resistance like oh, it consumes too much water and I'm like, well I'd rather eat four or five fewer almonds and like make sure that I have capacity to do all the things that I need to do. You know, my, my yard consumes a lot more water than data centers. And so we'll see if there's you know, sort of melting resistance to 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 then, then we end up in bubble territory. I would say the one thing that could shift that would be, you know, massively smaller models. You know, and that probably comes from like an algorithmic breakthrough of some sort. You know, we do have companies that are working on that. You know, if you just start with the human brain, like the human brain is just far more efficient at learning and you know, 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 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, you know, if we spend 5 trillion of CapEx, you know, can you get one or two trillion dollars of revenue as a, as a return on that? And we could 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 investing right now.
- [27:09]
David Clark
Yeah, we touched earlier on just the size of companies today as we're sort of thinking about SpaceX, IPO and then potentially second half of the year, OpenAI and Anthropic, that could be 4 to 5 trillion dollars of, of value that's created just by those three companies. 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 indigestion post those IPOs?
- [27:42]
David George
Yeah, look, I think having these companies get into the public markets while they're in hypergrowth 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.
- [27:59]
David Clark
Yeah.
- [28:01]
David George
And you know, my parents retirement funds are in index accounts like I would love for them to be able to directly own. Sure. You know, SpaceX and OpenAI and you know, anthropic as a consequence of that. And so my hope is, you know, and it seems like it's going to go that way, that there'll be index inclusion and broader ownership. And so I think it's a good, 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 Mag 7 are all growing sub 30% at this point. You know, all the software companies are growing sub 30%. You know, all the Internet.
- [28:53]
David Clark
Yeah. Palantir is the only one that, Palantir
- [28:55]
David George
is really 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 hypergrowth 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 7 where we say, wow, you never would have thought 10 years ago that we were going to have a $4 trillion company or $5 trillion company, but here we are. So I think there'll be 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. It's a great thing.
- [29:35]
David Clark
Yeah. One last thing I'm keen to get your thoughts on, David, is if the optimistic case For AI is right. What do you think the VC industry looks like in five years time?
- [29:44]
David George
That's a great question. There's so many unknowns that drive this.
- [29:49]
David Clark
If we can't speculate on a podcast.
- [29:51]
David George
I know, exactly, yeah. 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, the role open source plays, how much competition for tokens. There is. 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, and AI and intelligence. And we're at the very early stage of seeing those. So we just need to be in position to, to back those founders. You know, if you look at sort of health, 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 backing 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 and it's probably an and 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. And 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 really extraordinary outcomes.
- [32:05]
David Clark
Yeah. 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. And I just find it the pace of change is a real opportunity, but you've got to get things right as well. And I just feel super excited about what we're seeing and what the potential is for venture to really be at the, at the, at the center of, of, you know, changing the way we live and work.
- [32:33]
David George
Yeah. Yeah, same here. I mean, the, the opportunity is so great, I think. You know, changing the way we live and work, I I happen to 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.
- [32:51]
David Clark
Cool.
- [32:54]
Podcast Host
Thanks for the Listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating, or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. As a reminder Reminder the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures sa.