Why Investors Are Rethinking Everything for the AI Era Transcript, AI Summary & Key Points

00:00 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.

00:17 >> AI is attacking every facet of the GDP, transportation, labor, services, capital, coordination. There hasn't been a technology [music] paradigm that hits on 30 trillion in GDP at the same time. >> Elon has talked publicly about Grok bot. On Sam's side, he's talked about Astra and some of the long-running capabilities that are going to come out soon.

00:34 >> What do you think is going to be the next hundred trillion dollar market cap company? >> It is possible that >> 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, Open AI, Anthropic represent somewhere between three and a half to five trillion dollars of potential enterprise value and shockingly before

01:06 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, um why power law is not just only in the venture capital industry and then particularly where and how value actually compounds today.

01:28 David George, around 30, 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.

01:50 Probably going back to, you know, the emergence of the network effect driven consumer companies. Um 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?

02:08 And they 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 a thing like, how do you screw up a a startup?

02:32 Um, 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 and dueling priorities and and it sort of gets messed up cuz you can't hire enough people to do enough things fast enough. Um, now that's not the case. You can throw dollars at compute and compute can make products and the business is better.

02:52 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 uh in the AI market and I think it'll continue to be the case. >> So Ram, so first of all, uh you're not just one of our long-time LPs at Accolade, uh but incidentally, it's been exactly 10 years since you were actually an employee of a16z.

03:12 And so for your 10-year anniversary since you were last here, I've brought this gem back. >> Oh my god. >> [laughter] >> Oh my gosh, this is amazing. How do you still have this? This is amazing. >> So we dug in the catacombs uh and we made the extra large version uh just for for posterity here. >> [laughter] >> We got this from the from the catacombs.

03:31 But incidentally, during the last 10 years, a lot has changed in the world. And 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. >> Yeah, the the first one at a billion, first venture fund. Exactly, exactly. And so, you know, a lot has happened since then.

03:49 How do you think about your venture portfolio juxtaposed against your private equity one and then just also generally asset allocation. We talked 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.

04:05 It took SAS 15 years to get to the same point. AI did that in 4 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 the func 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.

04:30 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 you should it's not a satellite position. You should be core or a super core in some shape or form. >> Mhm. >> I'm very biased, but if you just think about the the shape of the markets and how they've changed since I started my career um you know in private equity and growth equity you know that

05:00 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 in 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 GA. It's venture like outcomes in late stage which is now happening.

05:20 >> Mhm. >> So it's no longer just early stage and you IPO when you have 100 million in revenues. >> Yeah. The top 10 sell out comes I think used to be 10 billion and now they're like 40 billion or so. >> to be probably 100 billion by the time Anthropic and then Open AI came come out. Yeah. >> Yeah. Yeah. Yeah. And and look this makes sense, right? Like the last cycle created 25 trillion in market cap new market cap and a bunch of that went to the incumbents.

05:41 >> Yeah. >> But a lot of it went to to startups and the new startups and Now, 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.

05:58 Um like take health care. Health care spends like 60 to 100 billion on health care IT per year. But AI is hitting on actual labor and the value of tasks that are being performed in health care. That's claims, billing, administration. That's a trillion-dollar industry. So, the TAM of AI can be 10x plus bigger than traditional SAS or health care IT. And what is that value?

06:20 Well, what's the economic value of tasks that are 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 what how big the TAM can get. To your point, it's you look at every wave, the incumbents are 10x smaller uh 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.

06:46 >> Yeah, same here. Yeah, labor, I mean, look, if you just look at like 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 um labor is going to go away. Like I think labor is just going to get reinvented. And so, we'll we'll end up with um you know, a sort of reimagination of the tasks that humans do.

07:07 Um but I think that's actually the the 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 uh says to us often, it's like, "I love Harvey. All my clients think they're lawyers now. And they can actually spar with me on topics where they would have probably been like, 'I don't really understand this.

07:27 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 these use cases are are massively massively um probably underappreciated and we don't still even >> Yeah, and they're expansionary. Yeah, that's a great point. Yeah. >> And then there's there was this whole thesis where well, frontier labs are going to cannibalize the apps, which layer is going to win?

07:44 It turns out like everyone is sort of growing. >> Yeah. Yeah. Yeah. Yeah. We just you know, a friend of mine did a podcast where he describes like everything is going to work kind of thing. >> Mhm. >> Um and um you know, I I describe it slightly differently, but it's like, you know, we get questions all the time from LPs when they ask us like >> Which stack >> layer in the stack which layer in the stack is going to work and I'm kind of like, I don't know.

08:06 The 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. Um 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. Uh so we try and like remove ourselves from thinking in a zero-sum way like that.

08:27 >> Extract it out cuz why why do people think it's going to be a winner-take-all? And 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 we underwrite a lot of the fundamentals. So maybe extract it out. >> Yeah, it look, winner-take-all is an interesting way to describe it cuz like if you just look at the market cap growth of all the leading technology companies like there are many,

08:52 many, many that were successful. Like there did wasn't winner-take-all, right? Now, there's an important distinction like we very much are believers in the power law within any 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? Um uh but I think there will be a massive expansion of the amount of categories that we have, right?

09:15 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 and things like that. Um 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 um you You in our business we can tolerate loss, right?

09:36 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. >> early stage, yeah. >> On early stage. Um 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.

10:04 And so, um 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. Yeah. Yeah. And and embedded in that is also kind of timing cuz I I know, Romi and I lament on this in that I think a lot of folks often times think that things are overheated in that moment in time.

10:31 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 on the importance of consistency in venture capital, probably even more than any other asset class cuz you just never know when these technologies can come out.

10:49 Maybe walk through that cuz 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 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.

11:08 >> 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 five to 10 companies over the last five 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.

11:30 >> Sorry, say that one more 20% have >> 20, no, 20. >> [clears throat] >> 20 out of 3,000. >> [laughter] >> Less than 1%. >> Wow. >> Consistent 3x net returns. >> That's incredible. >> And it's actually you don't have you don't need seven eight funds in those 20 years. It was do you have three to four 3x net TVPI funds over a 20-year period. We found only 20.

11:48 >> Wow. >> Firms that have done that. >> Wow. >> 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. >> Yeah. >> Um 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.

12:08 >> Yeah. >> If you're late stage, DJ I'm curious if you agree, sizing is really critical. >> Yeah. >> Uh 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. >> Yeah.

12:27 >> Um 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 is 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 net.

12:44 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 I just pulled up a tweet from our friend endowment Eddie who posted actually this 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 {slash} hires.

13:07 LPs have slowly followed along, but most are still dragging their heels cuz it's actually it's 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, >> Mhm. >> or they're playing at a stage that's so much earlier than the bigger firms.

13:27 Where it 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 your 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. Yeah, death of the middle. >> Death of the middle.

13:42 Death of the middle. >> We said we're going to drink every time we said death of the middle, so [laughter] >> I'm very complimentary of of many of our peers in the in the venture ecosystem. Um you know, like but this this death of the middle thing, you >> How do you define it? Like what's the middle? >> Uh 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 um have done a pretty good job, right?

14:09 Like they've done a good job and 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 that is that is a reality. Um then I think there's like the you know, whatever we're you know, large you know, large scale venture, right? In the sense that we have main product lines and we can scale all the way from you know, a seed all the way through to when you go public.

14:30 Um 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 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? The what does the founder care about? The founder cares about um you know, sort of taking capital from a partner that they think can de-risk the outcome for themselves.

14:56 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 they care about a person, so you have to you have to be a good actor in all those things. But there's a reason why we build up a tremendous amount of resources, right? Like it's why we have 700 employees.

15:11 Uh 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. Um 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 and, you know, as Eddie said, we're doing an okay job with that.

15:37 What I agree with him about then is our LPs coming to invest in us is a byproduct of that, right? Um and so, you know, our business our business is a flywheel. Um the flywheel starts with, um you know, are we are we deep domain experts? Uh are we going to have a point of view uh that is the right point of view? Um can we demonstrate to the founder that we, you know, are the right partner for for her or him?

16:02 Um if so, we win the deal. If we can help to make the outcome better, that's great. Um and then if we do make the outcome better, there's two things that happen. One, um our business has persistence of returns partially because, um you know, the the new founder wants to be around the winners, right? Like they want they they care about that because there's important brand signaling and and that has knock-on effects for them.

16:24 Um and then secondly, you know, by being a part of the winners and helping them, you know, in small ways, uh we 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 your both of your takes, is take pre-seed seats, so sub 20, 30, 40 million dollar valuations, sub 100 million dollar funds, they can coexist with the big firms because at inception stage, say

16:47 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 cuz 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.

17:06 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 complimentary to the big firms. Yeah. Do you agree with that? Like, yeah. >> Yeah, yeah, yeah. Yeah. And look, we have like very healthy relationships with with seed funds across the ecosystem.

17:24 Um, we also do do seed ourselves, right? But pre-seed, for sure, you know, it's sort of earlier than we typically >> The seed you would do, I think, is like chunky or bigger seed, right? Yeah. >> [laughter] >> Yeah. >> That that definitely is our sweet spot. With that said, you know, we have our speed run program, which we just came from actually earlier this this uh this morning, and I don't know, I think I think the market is evolving, where where um because founders have such uh preferential attachment, as as DG was

17:53 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.

18:11 And so, I think we can coexist in this world, but I I think it's also very um 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 I think >> to a few founders at Speed Run today, and they very much are hoping to stay in the orbit.

18:32 >> Yeah. Right. Right. And so, this is, you know, a new phenomenon that were 10 years ago, again, this is starting to really take shape as more of the early-stage folks started doing later stage, and then extended across the but not quite in the way that it is today. And DG, I don't know if you 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 to come behind it as well.

18:57 >> Yeah, I mean, look, I'm very biased, uh 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, um you know, that provides us a tremendous amount of advantages at the growth stage, um, in terms of access, information, knowledge, um, relationships, etc.

19:15 Um, 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 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 to the late, too.

19:35 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 the same time, almost majority of those logos, so to speak, we have to get at the early cuz that's the only way we maintain the ball control and also participate in the pro rata and then some.

19:53 And so, that's obviously the the business that that >> Yeah, 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. Like, it's Um, and we talk 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

20:19 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 fund and write a $500 million check. >> Yeah, yeah, I know, it's very hard. Yeah. I I've I've lived that world. >> All right, well, how do you think about from the LPC, how venture is fundamentally perhaps a structurally different job than maybe when you started your career also as well?

20:42 And, you know, how do you think about also asset allocation within venture? Cuz there's actually sub-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 funds. There's thousand close to 2,000 today in the US alone.

21:01 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. >> Yeah. And then dedicated late stage. So, there's four ways to play it. Um we have done the larger firms for decades now. We've done the seed firms. We've selectively done a few in the messy middle.

21:21 And we haven't done dedicated late stage for the reasons we talked about. Um how is it changing? AI is actually making our jobs harder than ever before. Um it's making it harder because rounds are larger in 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.

21:49 Company comes out of pick your accelerator. I went from zero to five minute ARR in a month. >> Yeah. >> There's no renewal cycle yet on that company. >> Yeah. >> 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 a multiple by 12. So, but for every con for nine companies like that, there's one really special one that's doing a couple of million in ARR, actually ARR, that has a huge valuation that will go on to

22:15 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's a lot of certainty. When you guys at Cursor I don't think there was a lot of certainty in how many for how many months were people saying Cursor is dead?

22:39 >> Oh, even the morning of the acquisition announcement, people were still saying that Cursor's dead. I'm like they just announced that they were going to be acquired by SpaceX for $600 >> Tell me if I'm [laughter] wrong. $300 million ARR, $400 million round, or somewhere maybe around there. >> Yeah, yeah, it was something like that. >> Like that's a lot of people would say like that's crazy.

22:54 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. Um secondly, like it is hard to parse out real versus, you know, kind of misleading attraction.

23:16 And misleading in the sense that like you can't take market signal from it, not that anybody's doing any misleading. Um >> Probably some of that, too. >> Yeah, there's probably some of that, too, [laughter] but like I go back to >> every now and then people are like helping to redefine what AMR actually means. I'm like, this is a very helpful PSA for the >> This is always This is always helpful, yes.

23:33 Uh yeah. But like I I don't know. I come back to, you know, is the market demanding more of your product? Like that is always the question. That's a posted note on my on my computer screen. >> But 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.

23:47 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 over time as an example, right? Um they did a really good job commercially early days. Like cuz they were they were smart. They were They were like a research plus lawyer combo. Um you know, they got some momentum and some and some, you know, high-profile law firms to sign up early days.

24:08 Um but, you know, the usage was not very good, right? And so, like if you looked at the actual deployment of it, it just it looked like mediocre compared to some other software firms. Um you know, and AI companies >> retention standpoint? >> Not retention. No, actually usage usage usage of it. Um now, fast forward post reasoning models, like that totally flipped.

24:32 And uh you could see, you know, absolute takeoff of adoption, right? And so, a bunch of different things happened at the same time. Lawyers got way more value out of the product. You could see it in usage and engagement. Um and then it became, because it was like high utility usage and engagement, it almost became a flip from what was previously like, oh, we're scared of things like hallucinations, to No, no, no, every client is actually demanding the law firms use the product.

25:00 Um and so, you know, I think we look for markets like that. We try to catch them early. Like we try to catch them earlier than when than we did at Harvey. Um but, you know, that's the kind of signal that we look for. You got to go You got to go a layer down. Like everyone can do cohort analysis. Everyone can look at renewal data. Um but like understanding the texture of the market and what the customers actually want and need and their alternatives, I think, you know, that's how you make the decision.

25:23 That's why it's so important to have the early stage business um because they're the deepest in the in the technology and the products. Um and they, you know, they obviously saw it in Cursors and they've seen it in many other things. >> Jen, what's the biggest pushback? I mean, you're the most prolific fundraiser I know. So, what's the pushback you get from LPs?

25:39 >> me very gainfully employed at this firm. >> I [laughter] I agree that you are a prolific fundraiser. What's the biggest pushback you're getting from LPs on like the state of AI, the state of venture? >> So, a lot of it is is worries around um you know, are we catching a falling knife here? Just like the timing of the market where we are, like are things overheated, etc.

25:57 And so, we talked a lot about this at the the outset, you know, around valuations and what the potential of the market is, but I do get a lot of sentiment from the LPs that their job is also about to fundamentally change as well. And and you mentioned earlier one aspect of it around AI making it more challenging to evaluate opportunities and funds. But the other aspect of it as well as the the LP historically has not been incentivized to actually embrace change in some respects, right?

26:26 You know, this is very much a job where um the end goal is actually somewhat diametrically opposed with the risk tolerance of the GP. And and this is just the the mechanics of the industry, but often times say, you know, a GP can get fired for missing out, you know, the next, you know, Facebook, the next Uber, right? Like that is the error of omission and like that's fireable.

26:51 But LPs on the flip side only get fired if you invest into a manager. So in some perspective, like the incentive outcomes are actually completely opposite of the two >> get fired for missing IBM if you're an LP. >> Exactly. Yeah. And in fact, like you don't potentially even get fired for not investing at all. >> Yeah. >> And so even >> if you miss the frontier models, back to your first question as an LP, but you kind of were along the benchmark, maybe slightly below the benchmark, you're keeping your job.

27:17 >> Right. Right. And so >> And and also for most folks and I'll leave fund of funds out of the the equation cuz it's a different different piece, but but you know, for a lot of folks, the upside actually is not that interesting for them. So the pitch of like, "Hey, you're going to miss out on the next potential of generation of >> misalignment. >> It's it's actually quite um diverse.

27:36 So so where do we go from there in terms of the LP kind of role and see if like it I think we also have an important role and function. We often times talk about in the context of our job as a leader of the venture capital industry is we have to help folks understand where the future is going and part of that is understanding how to infiltrate not just within their venture capital allocation, but across their entire portfolio.

27:58 And that I think is way more interesting than just saying, "Hey, like you might miss out on this next generation returns or the optimization of like the next frontier model or, you know, one or two power law companies, etc." >> Access, selection, sizing is what LPs do. So access you could argue you have the data to figure out who has done well historically.

28:17 >> Mhm. >> Out of those 20 firms out of 3,000, like you were not going to see consistency, right? Maybe half of them are consistent. >> Mhm. >> But the LP's job is also to find the next gen firms as well as continue accessing that. So one is you access, two is selection, three is portfolio construction and sizing and it's critical from an LP standpoint.

28:36 Because if you have an asset class where 20 firms out of 3,000 do well, you should concentrate in those 15, 20 firms pretty consistently. So when I see a portfolio with 50, 60, 70 venture capital firms, it's very hard for me to imagine that the overall portfolio can generate better than the average. >> Mhm. >> And again, I'm going back to the average invention.

28:56 That's just not what one >> It's not not compelling enough for the liquidity. >> Relative to any of the other asset classes, public markets, private equity. I mean, private equity can probably get you like 1 and 1/2 to 2x net without the lockup, without the risk you're taking in venture. You talked about 60% loss ratio. PE doesn't have that. >> Right.

29:11 >> Uh PE has other problems we can talk about today when it comes to AI software, but um so, portfolio construction sizing for an LP is critical. Like, I've seen too many times an LP or an allocator find an interesting fund, actually get it right, and put 1% of their fund into it. >> Mhm. >> Great, you 10x'd it. It returns 10% of your fund. Does not move the needle at all.

29:34 >> Yeah. >> Yeah. >> Yeah, yeah. >> Yeah. >> Where do you all think we are today in that evolution that Jen was talking about of how you would I know you're biased, but the appropriate percentage of overall capital allocated to venture and growth compared to private equity or public markets or real assets, credit, whatever. >> Yeah, this is the this is hard for me to answer cuz all I do is venture growth, so I would be biased to say it's should be super sized.

30:01 Um >> We like that answer though. >> [laughter] >> Look look at the public markets today. We vibe coded like something that created a great way for us to assess AI resiliency. Um public companies, and now we're doing that on the private side, and it's helping us hugely in in our growth equity portfolio, but the in the SaaS public markets, there're only like 15 to 20 companies max trading above 10 times revenues, which is by the way an insane number cuz it used to be dozens and dozens a few years ago.

30:28 And every one of those companies for the most part is showing acceleration of growth from AI. You're either in monitoring, security, deployment of agents, etc. So, it goes back to the same principle where if you are in some shape or form tied to AI, which is the fastest growing facet of all the elements of the GDP, then you should supersize it in your portfolio.

30:46 That will have impacts in the public markets. Even private equity today, when they're doing a new investment, they're looking for something that's AI native. They're not looking to buy a workflow software company growing 10% that's seed-based. Like, that's just not happening. They're looking for the system of record that can show acceleration with an AI native management team.

31:06 >> Yeah. >> So, that connective tissue of AI is actually across every asset class today. >> Yeah. >> And the one of the strongest way to play it is, I mean, I'm it's it's probably through venture. >> Yeah. >> But, that's where selection access and portfolio construction is really critical. >> Yeah. Well, even the exits that we were talking about um you know, Silver Lake potentially, you know, buying Workday, for example.

31:26 Just like doing more provocative things, for example, in private equity land when you have the capabilities to potentially infuse and and bring them into into the future as a part of that. The other version of it is also exits in venture now way exceed private equity. Like, I you know, like I was looking this up last night. Private equity this year, the biggest exits are um the buyout of EA, which was like around 50 billion, and Medline, which is around 50 billion.

31:52 Cursor, let's not let's exclude the IPOs. Like, Cursor was the M&A sale to SpaceX was way bigger than that. And so, >> The the problem with that is, like, you look at the pre-ChatGPT vintages in private equity. >> Mhm. >> You would have paid, I don't know, 15 to 20 times EBITDA for a software asset that's growing 10, 20% max. >> Yeah. >> If you look at the public markets today, that asset is trading at two times revenue.

32:18 >> Yeah. >> And the problem is not the just the valuation. There might not be a buyer for that company because if you're looking at a software company today, the first thing you think about, what is the terminal value? Is it resilient from AI? The best way to show that is organic growth acceleration. Our data shows one percentage of growth in the public markets is equivalent to 3% of EBITDA.

32:39 >> Yep. >> So, by the way, it's funny because in COVID, everyone was like, "We need to be profitable." >> the inverse. >> And now it's the opposite. >> No, no, '21 it was the inverse. And in the post-COVID, it's it's basically like fully uh aligned with risk, right? It's like correlated with like risk in the public markets. >> Exactly. Um so, unfortunately, a lot of those private equity deals, they're not growing fast enough.

32:55 They're not showing that acceleration. And they may not have the management teams to revamp the business like what Intercom did is a great example. Bring the founder bank uh back, revamp the whole business, create an AI-native product, scale it, and then sell. Like it's almost like you're suiciding your existing business, which in private equity is really hard to do.

33:15 >> It's really hard to do. I just spent a little bit of time with the founder uh and uh I I went up to him at an event and I was like, I just gave him a big high five and I'm like, "You did it, man." This is the This is the thing that like is really, really hard to do, you know? It's an N of 1 right now. Um but, you know, there's a bunch of really good founders who are capable with those businesses, public markets, private markets, uh who I think are are going to take a crack at it.

33:36 Um so, we'll see. >> Yeah. May maybe on that thread though, DG, cuz we we also sometimes get the pushback as well. Like for folks who have been in venture and allocate to venture, they might also have a similar problem where they do have the legacy sound businesses also as well. Like what's the balance between how you think about the historical stuff?

33:55 >> Let me ask you that question. So, I'm going to piggyback off of Jim's question to you. You got a pick a 2016 through 2021 pre-ChatGPT vintage software company that was fine, but doesn't have that AI-native features anymore, is not accelerating. It's growing like 30%. On the venture books, it's at like 10, 20 times revenue. You say it can't go public anymore.

34:16 Like no one cares to take that public. Yeah. Silver Lake has no interest in that company anymore. They would have a year ago. They don't. What happens to that company? >> Um You know, look, it's >> Like we have a lot of exposure to those companies too. >> Yeah, look at I would say it's like it's it's very TBD, right? Like I you know, I was with um one of our CEO founders this weekend and you know, he was like you know, give me the straight scoop.

34:39 Like what do you actually think is happening? And um you know, not don't give me a he he actually said to me, "Don't give me a podcast answer." Um which is ironic. Um and you know, I think both things can be true that AI is like the biggest generational change that we've ever seen and it's going to transform industries. And also there will be some enduring value of software companies that adapt, right?

35:01 Um part of the thing that we're monitoring which makes us extremely bullish about AI is just actual diffusion into the real economy, right? So, coding I think if if you were to paint the the bullish scenario for regular software and for slower pace of change you would say um you know, coding hit, but that's kind of a head fake, right? Like coding coding is perfectly documented, right?

35:25 So, it has like perfect data. Uh it's verifiable and it's simulatable, right? And so, um like most tasks in business do not share those three attributes. And so, you know, maybe the diffusion into other knowledge work beyond, you know, coding will take a lot longer. That would be the case to make for the software companies. Um and then some of them will evolve and and have AI, you know, solutions and they'll change their business models.

35:50 Um and I think that's a must. Um but that would be the case for why maybe, you know, it's a little bit overblown. I think if you look at the way that a lot of the public SaaS companies have reacted over the last few months, like that's I think there's a little bit of a a growing realization in that. Um All that makes me super super super bullish on AI though, right?

36:07 So, like if you look at our portfolio, um you know, we have some of those companies put about 95% of our NAV is not in those companies, right? Like there it's it's in the it's in the companies that are, you know, growing very fast, accelerating, etc. Um you know, the average I think it was the median company in the US is spending $12 per employee on AI per month.

36:33 The top 1% of the data set that we've seen is spending $7,000 >> Wow. >> per employee on AI per month. So, um not only have we had like limited diffusion beyond coding, but if you just look at diffusion of, you know, the shape of who is consuming tokens and actually getting real value out of AI today, we're super early, right? Like banks, you know, the most cutting-edge banks are probably doing 1% of headcount cost on on AI tools.

36:58 Um and so, the reason this makes me very bullish is these are the fastest-growing companies we've ever seen, like of all time. Again, they're adding more revenue per month than the than the mega-cap tech companies. Um and yet, it's probably on the back of adoption of like 10 million users, maybe 20, maybe 30 max. And, you know, there's 1 and 1/2 billion knowledge workers in the US, and I think it's going to transform the way we do a lot of work.

37:24 >> Yeah, Calpers famously, uh lost out on billions of gains by not investing in the market. They're making up for lost time now. They're they've uh converted their portfolio from 91% to 58, and venture and growth from 9 to 43. There's probably some balance in between those things, uh but, you know, they're they're leaning hard into. Um but there's going to be a lot of value still that's going to be accreted in some of these historical companies.

37:45 And, you know, I sort of joking around about Bendings, but that was probably a great outcome for Airtable, yeah, outside of the fact that, you know, they're going to actually spin off the hyper agent piece of the business and actually, um I think do really interesting things with that. But, in the scheme of things, I think there's going to be a lot of homes for a lot of things.

37:59 And this zero-sum thinking I think is is probably the pitfall of which we would would advise against. >> So, we talked about um we've talked about, you know, venture growth, we've talked about private equity, you know, we've talked about um public markets. Um and by the way, the composition of all of those have like radically changed over the last 10 years.

38:15 Um we also have had the emergence of entirely new categories that are available available in the private markets like private credit. Um do you have a view on sort of outlook of those on a relative basis? >> In software in particular? >> Yeah, I'd say in technology. >> Uh the advantage point we have is looking at private credit which resides in a lot of private equity software portfolios, which is hundreds of billions.

38:43 >> Mhm. >> I'll give you one statistic. So, you look at 21 22. About two to 300 billion in LBO software transactions happened with over 200 billion in debt taken out. The average valuation for the software deals were 25 to 32 times EBITDA. Those companies today are worth probably half that. The reason you're seeing redemptions in the credit markets in private credit is exactly that.

39:05 They're looking at the public markets. You've had SPAC-alypse. It's been a massive correction in software. And the And you can see a contraction in valuations, which means the leverage ratios have gone up dramatically. So, if you are a software company that is in somewhat not resilient to AI, I think you're challenged both in terms of your equity position, also credit as well.

39:30 >> By the way, even the um AI version of private equity is not completely insulated. We oftentimes talk about like you know, just because you put Sears on a website didn't make it Amazon, right? You have to have the benefit of building Amazon from the studs logistically to make it Amazon. It's not just the website. And in a lot of instances with the private equity backed companies that are now just infusing AI, we've seen it actually in some of our companies as well, where the peer competitors like, "Oh, the first

40:00 thing I'll do is of course hire AI customer service agents cuz that's like an easy low-hanging fruit." Like, turns out if you don't actually build in the workflow, you start to turn customers very quickly if they're used to talking to a human. And for every dollar every drop in NPS is like a direct correlation with drop in revenue and then you start to spiral especially if you have debt laid on top of it.

40:20 So often times sometimes we hear from folks like well I'll just do the AI you know kind of version of private equity. It's not a pan panacea for for generating returns especially when it's just so categorically different from a technological perspective to actually infuse that throughout the company as well. >> Yeah, you can just throw an operating partner at the company and say let's put AI on it.

40:41 It just doesn't work. You need to completely if you have By the way, if you do have a founder mentality at the management team like it is possible but the board has to be aligned all the investors have to be aligned and you do have to make some really hard decisions the way Intercom did. >> Mhm. Yeah. Yeah. >> Yeah, so obviously that this is a group that is very pro you know venture and growth this category.

41:00 Let's talk about the legitimate opposition to it and what is the case you know for why maybe the you know the risk that you're taking or whatever it may be with venture and growth you know doesn't justify it. >> I mean the pushback we get a lot is timeline to liquidity. So it takes the average unicorn is private for 10 plus years typically and then you got all these follow on rounds that are happening pretty quickly one after the other.

41:27 You see maybe the same logo in five six different firms and the question is how do you get out of it? Um and so >> And an IPO isn't actually a distribution. >> It takes it could take 12 24 plus months before you actually get liquidity of an IPO. Like especially if you own 10 15% at IPO like it's going to take a long time if you're in the generational company.

41:47 So we get that pushback a lot in terms of timeline to liquidity. >> And then what is the what is the sort of counter to that pushback? >> Well counter to that pushback is going back to 3,000 firms 20 do all consistently. If you're in the top 1% of those firms and you have a category winner you want to make sure that compounds actually. >> Yeah. >> Um would you have wanted to sell a Stripe or any of those other companies 3, 4 years ago?

42:15 Like, the answer is unanimously no. Uh, now, could some of these companies go public earlier than not? Sure. Anthropic was really first funded in 2021. It's about to go public 5 years later. >> Yeah. >> Cursor from first acquisition from first funder financing to acquisition is short. Like, >> So, the best venture firms actually have fund returning liquidity pretty quickly, maybe even quicker than private equity.

42:38 But, that subset of firms is tiny. >> Yeah. >> Yeah. Yeah. Actually, very famously, uh, a year and a half ago or so, we went to our Fund 1 LPs. At that point in time, the Fund 1 was 16 years old, and we had this position in Stripe that we invested at the seed stage. And we asked all of our LPs, like, "Hey, do you want liquidity out of this?" We recognized, you know, the job that we came to do is now done 16 years in.

42:58 Like, do you want liquidity back on this? And every single one of those LPs said, "No. We'd rather let this continue to compound." And then ultimately, a year later, you know, we decided to to make that exit because 17 years in, we've got to get this this liquidity out, we've got to wrap up the fund, etc. But, you know, so many LPs, I think it's very specific to certain categories, right?

43:18 Endowments would prefer to let it run. Family offices, quite frankly, don't want the money back cuz they don't want to pay taxes on it. They'd rather have it continue to compound. And so, there is specific nuance with each LP group where it's very hard to paint a broad brush stroke stroke on like across the board on everyone wanting the same thing. But, I also think to your point, the very best LPs, uh, excuse me, the very best GPs have manufactured along the way liquidity.

43:43 And particularly in 2021, when a lot of folks didn't take money off the table, you know, I think that was a good sign of the first indicator. And now in this next cycle, it's can you actually get some early liquidity out through M&A, and then let, you know, potentially the winners IPO over time with the fullness of of compounding as well. >> Yeah, exactly.

44:01 >> I'm going to end on this note because I I thought this is an interesting question that you and Gavin were were uh tossing back and forth, E.G., on um he he uh didn't want to answer the question on what will be the next $10 trillion company, but he had a certainty around what will be the next $20 trillion market cap company. So, I'm going to ask both of you, uh what do you think is going to be the next uh $100 trillion market cap company?

44:24 >> gosh. Here we go. >> [laughter] >> I can't even think in those terms. Yeah, we're uh that's probably two two tech cycles away, not just one. Uh it is possible that we have entirely new companies that get created. And I think a lot of the market cap creation that you would talk about that would drive a $10 trillion outcome or more um, is in new product areas that haven't yet been touched, right?

44:45 So, like what I talked about the sort of diffusion of the technology into the enterprise. Like we're nowhere, right? Um you know, a year ago everyone talked about consumer all the time. Nobody even talks about consumer AI anymore. But that is going to like the end use case for consumers is not going to be a chatbot interface. Like that's the skeuomorphic version.

45:05 We're going to have a native version. It's going to be proactive. It's going to do work on our behalf. It's going to it's going to create a ton of value for for consumers. And we're kind of nowhere on that. I mean, yeah, there's, you know, a billion ChatGPT users, but, you know, like we're like that's like scratching the surface. Um we are um nowhere on robotics, but I think robotics is going to be bigger than the language stuff.

45:26 Um and I think it's going to happen in the next 10 years. Um we are almost nowhere on autonomy, right? Like there's fewer than 10,000 Waymos uh live in the US, way fewer robotaxis. Um and then, you know, a lot of open space for others to build in that area, too. Um we, you know, healthcare is 18% of GDP. >> Yep. >> Like we've we've we've done nothing to scratch the surface either on care delivery or on drug discovery yet.

45:52 I mean, there's some companies that are working on it, showing some early signs of progress, but I think the progress that we make there in the next 10 years is going to be massive. Um and then, you know, we're in this interesting era of reimagining um, all things physical world from defense to manufacturing to data centers. Um, and so, I look at the confluence of all these trends and I'm like, yeah, it may feel like we've, you know, we've we've done a lot with AI already, um, but 10 years from now we're going to look

46:23 back and say it, oh my gosh, like those other major areas created a ton of value. >> Yeah. >> Um, and so, I'm excited that I think the next SpaceX AI or OpenAI are probably going to get created, um, and they'll probably be in those kinds of domains. >> Yeah. >> Yeah, I'll add one category that to me is both a concern but a huge opportunity. It's a plug for your new fund on the on the opportunities fund that you did.

46:45 I think very simplistically about like the bottleneck in AI today is not demand. It's on the supply side. So, you got energy, the grid, data center, then you got chips, then you got frontier models and apps. The US is amazing at the right side of that. So, like chips and onwards. The VC ecosystem supports that well. Um, I think the new fund you have is really going to help on the left side as well cuz the US doesn't have a problem with energy generation.

47:07 It has a problem with speed to power. Yeah. That's permissioning, transmission, that's regulatory. Other countries are putting out 10x more renewable capacity a year. >> Mhm. >> So, that is a real bottleneck and that means reimagining the data center. You talked about the density being 10x plus. Well, you can't just repurpose an old data center for for new AI facility.

47:25 So, this is where the new fund you have can create not 10, 50, but 100 billion plus opportunities as well. That can really solve the bottleneck and I think that is a real concern because demand is not a concern. Um, a lot of I've heard LP say, this is like the dot com or this is the COVID. It's not cuz the traction is real and it's not ephemeral revenue like COVID.

47:46 >> Mhm. >> The bottleneck could be supply, but if you have the right inputs, like the fund that's you're now backing those companies, next generation chip companies, memory, etc., that's a huge opportunity. >> It's time for machine age. Let's bring the machines. >> I love it. >> All right, let's close on that. Thank you both so much. It was super fun. >> Thank you for having us. >> Awesome. >> See you.