We've looked at the data of 3,000 venture capital firms in the US. Only 20 have achieved consistent 3x net returns over the last two decades.
Right now, clearly the power law is more extreme than it has been in the last 10 to 20 years of technology investing. For the first time, you can take capital and throw it at a company and it compounds their advantage.
AI is attacking every facet of the GDP: transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time. Elon has talked publicly about Grokbot on Sam's side. He's talked about Astra and some of the long-running capabilities that are going to come out soon.
What do you think is going to be the next hundred trillion dollar market cap company?
Welcome back to the A16Z podcast. Something fundamental has changed in how value gets created. Power law used to be just a feature of a cottage industry in venture capital and now it's systemic throughout. And particularly the three frontier model companies — SpaceX, OpenAI, Anthropic — represent somewhere between three and a half to 5 trillion dollars of potential enterprise value. And shockingly, before SpaceX went public, a lot of our LPs and also the broader institutional allocator community didn't have a lot of exposure to it. And so we'll talk about today why potentially portfolio construction and asset allocation may have changed, why power law is not just only in the venture capital industry, and then particularly where and how value actually compounds today. David George, Ron Bhatt, thank you for joining me.
Great to be here. Thanks for hanging out.
Thank you for having us here.
Awesome, awesome, awesome. Okay, so DG, so if you add up every venture-backed IPO for the last six years, all of them together, where does it go from here?
Right now, clearly the power law is more extreme than it has been in the last, you know, 10 to 20 years of technology investing, probably going back to, you know, the emergence of the network-effect-driven consumer companies. There are many reasons why that's the case. Increasing returns to scale have always been a dynamic in our business. Obviously, it's well covered how a network-effect business can have increasing returns to scale, but so can software businesses, right? And they can take different forms. But, you know, brand reputation in the market, the accumulation of resources, all provide competitive advantages. That all still is the case. But right now, especially with the labs, for the first time, you know, in my career, you can take capital and throw it at a company and it compounds their advantage. And this is a thing — like, how do you screw up a startup? Well, you know, throw too much money at it and have them hire a thousand people, and then, you know, you create all these coordination issues and overhead issues and dueling priorities, and it sort of gets messed up because you can't hire enough people to do enough things fast enough. Now, that's not the case. You can throw dollars at compute, and compute can make products and the businesses better. And so to me, it's not terribly surprising that the power law is more extreme. Right now, economies of scale are a very real thing in the AI market, and I think will continue to be the case.
So, Ron, so first of all, you're not just one of our longtime LPs at Accolade, but incidentally, it's been exactly 10 years since you were actually an employee of A16Z. And so, for your 10-year anniversary, since you were last here, I've brought this gem back.
Oh my god. Oh my gosh. This is amazing. Why do you still have this? This is amazing.
So, we dug in the catacombs and we made this extra-large version just for posterity here. I can't believe that I got this from the catacombs. But incidentally, during the last 10 years, a lot has changed in the world. And if you remember, at that point in time, people were bellyaching about fund sizes being too large back then.
And you had one at a billion. I remember the first one out of a billion. First venture fund. Exactly, exactly.
And so, you know, a lot has happened since then. How do you think about your venture portfolio juxtaposed against your private equity one? And then just also generally asset allocation. We were talking about this on the way in. If you were to start from a blank sheet of paper again, knowing what you know now, how would you have constructed differently?
Yeah, let's take venture today. We've reached 100 billion in revenue in AI. It took SaaS 15 years to get to the same point. AI did that in four years, and we're not even close to anywhere in terms of the penetration of demand. The reason DG is saying you can throw capital at it — that's a function of unlimited demand for inference. And we are at a point where AI is attacking every facet of the GDP: transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time. And so you have the fastest-growing technology, it's hitting on all parts of the GDP. So as an allocator, it's hard not to make the case — it's not a satellite position. You should be core or a super core in some shape or form.
I'm very biased, but if you just think about the shape of the markets and how they've changed since I started my career — you know, in private equity and growth equity, you know, that was I guess 18 years ago. This was a cottage industry, and now it's not. And I talk about this all the time, but our asset class is 5 to 6 trillion dollars of value. And the dynamics around companies staying private longer — they're not going to reverse.
Yeah. I mean, you had that insight in 2019 when you left G. It's venture-like outcomes in late stage, which is now happening. Like, so it's no longer just early stage and you IPO when you have 100 million in revenues.
Yeah. The top outcomes, I think, used to be 10 billion and now they're like 40 billion.
Yeah. Or soon to be probably 100 billion by the time Anthropic and then OpenAI come out. Yeah.
Yeah, yeah, yeah. And look, this makes sense, right? Like the last cycle created 25 trillion of market cap, new market cap, and a bunch of that went to the incumbents. But a lot of it went to startups and the new startups. And, you know, each one of these subsequent waves gets bigger than the prior one. And so, you know, our expectation is, you know, that take the 25 trillion and, like, it's going to be a larger number.
Yep. Yeah. And I'm constantly confused about the TAM of AI. We'd love your thoughts on this. Like, take healthcare. Healthcare spends like 60 to 100 billion on healthcare IT per year. But AI is hitting on actual labor and the value of tasks that are being performed in healthcare. That's claims, billing, administration. That's a trillion-dollar industry. So the TAM of AI can be 10x plus bigger than traditional SaaS or healthcare IT. And what is that value? Well, what's the economic value of a task that's being performed? Like, that's the TAM you're looking at. And then there's some capture rate that the AI company will take. But we have no idea how big the TAM can get. To your point, you look at every wave, the incumbents are 10x smaller over time. So it's hard to estimate it, but I can tell this for myself: I've been chronically wrong about how big these outcomes can get.
Yeah, same here. Yeah, labor. I mean, look, if you just look at how much of, you know, dollars are spent in the US economy on labor versus software, it's like something like 40 times more. Now, that doesn't mean, importantly, that labor is going to go away. Like, I think labor is just going to get reinvented, and so we'll end up with, you know, sort of a reimagination of the tasks that humans do. But I think that's actually the whole point of AI — is like you're going after this different thing. And so to equate it to software and say, 'Oh, it's the next evolution of software,' is far too limiting.
Our legal counsel says to us often, it's like, 'I love Harvey. All my clients think they're lawyers now, and they could actually spar with me on topics where they would have probably been like, I don't really understand this, I'm just going to defer to you.' So my billable hours have only gone up with the advent of the usage of AI. And so all those use cases are massively, massively probably underappreciated, and we don't still even know...
They're expansionary. Yeah. Is the whole point. Yeah.
And then there was this whole thesis where, well, frontier labs are going to cannibalize the apps, which layer is going to win. Turns out, like, everyone is sort of growing.
Yeah, yeah, yeah. We just, you know, a friend of mine did a podcast where he described, like, everything is going to work, kind of thing. Mhm. And, you know, I describe it slightly differently, but it's like, you know, we get questions all the time from LPs when they ask us, like, you know, which layer in the stack is going to work. And I'm kind of like, I don't know, the market is going to be so big. Like, I think everything might work. Now, there's going to be a lot of companies that don't work, and there may be idiosyncratic categories that don't work. But I think by and large, it's far too limiting to think like, oh, if open source does a good job, it's bad for the labs, and, you know, vice versa. So we try and, like, remove ourselves from thinking in a zero-sum way like that.
Yeah. Extract it out, because why do people think it's going to be a winner-take-all? And, you know, if we can hypothesize, but, you know, the last era of technology, it was probably winner-take-all in a lot of categories. But this feels categorically different because we're re-underwriting a lot of the fundamentals. So maybe extract it out.
Yeah, look, winner-take-all is an interesting way to describe it because, like, if you just look at the market cap growth of all the leading technology companies, like, there are many, many, many that were successful. Like, there wasn't winner-take-all. Right now, there's an important distinction. Like, we very much are believers in the power law within a given category. So, like, the winners will capture the vast majority of the market share and market cap, and second place is like playing for scraps, you know. But I think there will be a massive expansion of the amount of categories that we have, right? And so, you know, if you go back 20 years, like, CRM was not really a category. I mean, it was small. It was like Siebel Systems and things like that. But, you know, now it's a massive category. And so I think the same thing will happen. We've seen it in every technology market that we invest in. Again, our approach is, you know, in our business we can tolerate loss, right? And, like, if we're not losing money in a given fund on a given amount of investments, we're not taking enough risk, right? And so, you know, if you look at our best-performing venture funds over time, I think the loss rate is 60%.
Or so on early stage. Yeah.
On early stage. Now, at the growth stage, like, the loss rate will be lower, but it's probably going to be in the 10 to 20% range. And that's appropriate because with that, you will get investments that we make that, you know, 10x or more in returns. And so, you know, if we're doing a good job, we're backing the leading company in every category that is a credible category. And if the category works out well, then we do a great job. And if the category doesn't work out well, that's okay. That's kind of like the risk that we live with.
Yeah. Yep. Yep. And embedded in that is also kind of timing, because I know around you and I lament on this in that I think a lot of folks oftentimes think that things are overheated in that moment in time, and then you look back in retrospect and it turns out everything was actually quite cheap. But then there's these aberrations in the market where it's probably actually true. And so I know you advise a lot of your LPs on the importance of consistency in venture capital, probably even more than any other asset class, because you just never know when these technologies can come out. Maybe walk through that, because there's a lot of institutional allocators out there who actually don't have access to a lot of the frontier — certainly models now. They're trying to play catch-up, and in some instances probably maybe introducing some adverse behavior that is a little bit too reflective of things being a little bit too frothy. So maybe unpack that for us.
Yeah, I mean, the extremeness of the power law that DG talked about — if you as an allocator have not had access to the top 5 to 10 companies over the last 5 to 10 years, you're significantly behind in terms of returns. And let's take a step back. Like, we've looked at the data of 3,000 venture capital firms in the US. Only 20 have achieved consistent 3x net returns over the last two decades.
Sorry, say that one more. 20% have 20...
Consistent 3x net returns.
That's incredible. And it's actually — you don't need seven, eight funds in those 20 years. Or was it you have three to four 3x net TVPI funds over a 20-year period?
We found only 20 firms that have done that.
Consistency in venture is really, really hard. But what's interesting is the consistent ones consistently had access to the category-defining companies every vintage.
Now, there are exceptions, and by the way, just having the logo is not sufficient enough. If you're early stage and you have a large fund, you need to own enough of it. If you're late stage — DG, I'm curious if you agree — sizing is really critical.
So venture-like returns are possible in late stage, but your best company should be 5, 10% plus of your fund. That way you can actually return the fund on a single company. Fund-returning math in late stage didn't exist before. It now does.
So we have found the right portfolio sizing, and the ones that consistently have gotten access are in that top 20 out of 3,000. So if you don't have them, there's a huge dispersion of returns. And if you don't have those, you're getting the average venture return. If you look at Cambridge data, the average venture return over the last 10 years is 1 to 2x. You'll do better in private equity. You'll definitely do better in the public markets. You don't need to lock up your money for 10 years.
Yeah, for sure. Yeah. I just pulled up a tweet from our friend Eddie who posted actually this morning. He said, 'Interest in big VC funds has been driven by founders, not LPs. Founders more often than not want the brand that can scale, be a life-cycle investor, and help land customers/hires. LPs have slowly followed along, but most are still dragging their heels because it actually is counter to conventional wisdom.'
Yeah. And the outcomes are larger, so funds can be larger. There are some exceptions in those 20. There are some small firms that are focused on niche vertical markets, or they're playing at a stage that's so much earlier than the bigger firms where, like, there is not a lot of competition with the bigger firms. Now, the problem with that strategy is you have to stay consistent in terms of fund size and their strategy. If you start getting bigger over time, then you bump into the big firms, and I think it becomes really, really hard to stay consistent.
Yeah. Yep. Death of the middle.
Death of the middle of the middle.
We said we were going to drink every time we said death of the middle. So...
I'm very complimentary of many of our peers in the venture ecosystem. You know, but this death of the middle thing...
How do you define it? Like, what's the middle?
So I think, like, sort of what you described — like the highly specialized funds, you know, some of the ones that were very early to AI with, like, deep, deep, deep domain experts — have done a pretty good job, right? Like, they've done a good job, and, you know, sometimes they can move fast, get into things, or take shares of deals that we want to do, and, you know, that is a reality. Then I think there's, like, the, you know, whatever we're — you know, large-scale venture, right? In the sense that we have many product lines and we can scale all the way from, you know, a seed all the way through to when you go public. And I think we have some peers who employ a similar strategy, right? And, you know, there's — I'd like to think that we're the best, but there's a few other folks who do that. Everything else in between that, I think, struggles to compete a little bit for the reasons that Eddie said, right? What does the founder care about? The founder cares about, you know, sort of taking capital from a partner that they think can de-risk the outcome for themselves. Like, that — if you were just to simplify it — that is the simplest way to describe what the founder really cares about. Now, they care about the partner, right? Like the general partner. So you have to be a good actor and all those things. But there's a reason why we build up a tremendous amount of resources, right? Like, that's why we have 700 employees. That's why we take, you know, the management fees that we make on our funds and we invest them in operating resources because we think that it will, one, bend the curve on the outcome, and two, help us to win deals. And so, you know, when founders select their partners, often, you know, if it's a hot deal, like, they'll have many alternatives. Like, that's the revealed preference. And, you know, as Eddie said, we're doing an okay job with that. What I agree with him about then is our LPs coming to invest in us is a byproduct of that, right? And so, you know, our business is a flywheel. The flywheel starts with, you know, are we deep domain experts? Are we going to have a point of view that is the right point of view? Can we demonstrate to the founder that we, you know, are the right partner for her or him? If so, we win the deal. If we can help to make the outcome better, that's great. And then if we do make the outcome better, there's two things that happen. One, our business has persistence of returns, partially because, you know, the new founder wants to be around the winners, right? They care about — because there's important brand and that has knock-on effects for them. And then secondly, you know, by being a part of the winners and helping them, you know, in small ways, we create sort of killer references, and the founders then tell the other founders, 'Hey, you should work with these folks.' And so that's the way the flywheel works in our business.
One theory, curious to get both of your takes, is — take pre-seed. So sub-20, 30, $40 million valuation, sub-$100 million funds, they can coexist with the big firms because at inception stage, say there's seven AI companies kind of doing the same thing. I would think a large firm like Andreessen would want to wait for a round or two until there's more relative certainty, because one thing you don't want to do is be in the number two or number three. Like you said, you have to be in the category winner. So you'd rather wait for that round and actually double down and lead the A or the B. Mhm. So the small firms, they can carve out a niche for themselves a clip or two earlier than the big firms and actually have a right to win. They can do really well and be complementary to the big firms. Do you agree with that, or...
Yeah. Yeah. Yeah. Yeah. And look, we have, like, very healthy relationships with seed funds across the ecosystem. We also do seed ourselves, right? But pre-seed for sure, you know, sort of earlier than we typically.
The seed you would do, I think, is like chunkier, bigger seed, right? Like a serial entrepreneur.
They used to be a... Yeah. Yeah. That definitely is our... That said, you know, we have our Speedrun program, which we just came from actually earlier this morning, and I don't know, I think...
I think the market is evolving where we're — where, because founders have such a preferential attachment, as DG was saying, to the brands, we get to look at everything, and sometimes it does make sense for us to do the pre-seed and seed. And so, you know, you kind of want that flex of capability. And for the founder, to your point, they kind of don't really care where your focus is; they just want to be in that orbit, and, like, you'll find the funds to kind of match to it. And so I think we can coexist in this world. But I think it's also very important — like, our business is principally an early-stage business. We have to be first to the pole, and we may not actually do the investment, but we have to at least understand the landscape and market to be able to actually make the informed decisions later on. And so...
I spoke to a few founders at Speedrun today, and they very much are hoping to stay in the orbit.
Yeah, long term, right? And so this is, you know, a new phenomenon that, 10 years ago, again, this is starting to really take shape as more of the early-stage folks started doing later stage and then extending the product stack, but not quite in the way that it is today. And DG, I don't know if you would agree with that — like, it's just virtually impossible to have this sort of mid-stage business effectively without having the early stage and then also the late stage come behind it as well.
Yeah, I mean, look, I'm very biased, but I think the reason that we've been successful as a growth fund is because of our early-stage business. I say that all the time — like, our business starts and ends with early stage. And so, you know, that provides us a tremendous amount of advantages at the growth stage in terms of access, information, knowledge, relationships, etc. And I would think that, you know, our early-stage partners would probably say that the growth business provides them benefits too, because it allows, you know, us to scale up and deepen partnerships with founders over time, and that helps to win deals at the early stage.
Yeah. Totally. Totally. And you can't — there's both sides. You can't only do the early, you can't also only wait till the late, too. And so your point earlier around it looks like increasingly that firms are converging to a handful of names — like, that's probably true because, you know, again, this power-law dynamic. But also at the same time, almost the majority of those logos, so to speak, we have to get at the early because that's the only way we maintain the ball control and also participate in the prata and then some. And so that's obviously the business that...
We're big believers that the strongest late-stage franchises have a huge early-stage franchise attached to them. Like, the ability to win is multiplied when you have an early-stage franchise. Yeah. Like, it's...
And we talked about sizing in late stage, where you can — whatever the size of your late-stage fund is, if you can at scale put 5 to 10% of your fund in one of the category-defining companies. The way you could do that is because you had an early-stage franchise that developed that relationship with the entrepreneur and the management team early on. It's really hard to come in as a de novo late-stage firm and write a $500 million check.
Yeah. Yeah. Yeah. No, it's very hard. Yeah. I've lived that world.
How do you think about, from the LP side, how venture is fundamentally perhaps a structurally different job than maybe when you started your career also as well? And, you know, how do you think about also asset allocation within venture, because there's actually sub-asset classes within venture as you think about portfolio construction as well.
Yeah, I mean, there's — I mean, in a very simplistic way, there's — in our mind, there's four ways to do venture: pre-seed, seed — so think sub-$150 million funds. There's a thousand, close to 2,000 today in the US alone. Messy middle, we talked about, and then there's a lot of firms there, by the way — thousands of — not thousands, hundreds of firms. And then the big firms. And then dedicated late stage. So there's four ways to play it. We have done the larger firms for decades now. We've done the seed firms. We've effectively done a few in the messy middle. And we haven't done dedicated late stage for the reasons we talked about. How is it changing? AI is actually making our jobs harder than ever before. It's making it harder because rounds are larger in general. They're faster. The traction that's happening in the industry is confusing. And here's why it's confusing: you can have a company — I'm actually really curious to hear this from you because we hear this a lot — company comes out of Pick Your Accelerator: 'I went from zero to five million ARR in a month.' There's no renewal cycle yet on that company. And they're raising off of that traction at huge multiples. And a lot of times they're selling to each other in a cohort potentially. And it's not even ARR, but they're multiplying by 12. So, but for every — for nine companies like that, there's one really special one that's doing a couple of million in ARR — actual ARR — that has a huge valuation that will go on to be the next Cursor.
Yeah. So it is really, really tough actually today to parse out, like, what's real traction, what's not. Valuations are really high. This is why the big firms do well. I actually think they can wait, or they have enough relative certainty in the next round, then lead that round. But even then there isn't a lot of certainty. When you guys did Cursor, I don't think there was a lot of certainty. And how many — for how many months were people saying Cursor is dead?
Oh, even the morning of the acquisition announcement, people were still saying that Cursor is dead. I'm like, they just announced that they were going to be acquired by SpaceX for 60.
Tell me if I'm wrong. 3 million ARR, $400 million round, or somewhere maybe around there.
Yeah. Something like that. Yeah.
Like, a lot of people would say, like, that's crazy. Why did they do that deal?
Yeah. Yeah. Yeah. Yeah. Well, look. Okay. So, like, founder judgment is a very important thing, right? And so, you know, getting to know founders over time, spending a lot of time with them, seeing how they think — like, I'd like to think that, you know, especially my early-stage partners are pretty good at that. Secondly, like, it is hard to parse out real versus, you know, kind of misleading traction. And misleading in the sense that, like, you can't take market signal from it. Not that anybody's doing any misleading. There's probably some of that too. Yeah, there's probably some of that too, but, like, I go back to...
I love every now and then people are, like, helping to redefine what ARR actually means. I'm like, this is a very helpful PSA for the...
This is always helpful. Yes. Yeah. But, like, I don't know, come back to, you know, is the market demanding more of your product? Like, that is always the question. That's Post-it note on my computer screen. Like...
How do you know that when the company's been only operating or selling for a couple of months?
You're not going to be able to do it with financial analysis. You'll have to do it by really understanding the customers, talking to the customers. And then, you know, it's like one of the things that I said about Harvey overtime as an example, right? Um, they did a really good job commercially early days, like because they were smart. There was like a research plus lawyer combo. Um, you know, they got some momentum and some, you know, high-profile law firms to sign up early days. Um, but you know, the usage was not very good, right? And so like if you looked at the actual deployment of it, just it looked like mediocre compared to some other software firms, um, you know, and AI.
