How AI Is Rewriting the Power Law of Venture Capital

14:41David George: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.

15:19David George:Now they care about the partner, right? Like they care about the person, 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 it'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.

15:52David George: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 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?

16:35David George:They care about that because there's important brand signaling and that has knock-on effects for them. And then secondly, by being a part of the winners and helping them 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 seats. 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 andreason 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 being the number two or number three like you said you have to be in the category winner so you rather wait for that round and actually double down and lead the a or the b 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.

17:34Do you agree with that? Yeah, yeah, yeah.

17:37David George:And look, we have like very healthy relationships with seed funds across the ecosystem. We also do do seed ourselves, right? But pre-seed, for sure. You know, it's earlier than we typically do. The seed you would do, I think, is like a bigger seed, right? They used to be A's.

17:53Jen Kha:That definitely is our seed. With that said, you know, we have our speed run program, which we just came from actually earlier this morning. And I don't know, I think the market is evolving where because founders have such preferential attachment, as DG was saying, to the brands, we get the 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.

18:27Jen Kha: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 poll 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, right, right. And so this is, you know, a new phenomenon that worked 10 years ago. So again, this is starting to really take shape as more of the early stage folks started doing later stage and then extending process tech, but not quite in the way that it is today.

19:03Jen Kha:And DJ, 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 to come behind it as well.

19:13David George: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 said 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.

19:45Jen Kha: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. 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 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 pro rata and then some. And so that's obviously the business. Yeah, we're big believers that the strongest late stage franchises have a huge early stage franchise attached to them.

20:19The ability to win is multiplied when you have an early stage franchise. 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 five 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, no, it's very hard.

20:45David George:Yeah, I've lived that world. How do you think about, from the LPC,

20:51Jen Kha: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 subclasses within venture as you think about portfolio construction as well. Yeah, I mean, in a very simplistic way, in our mind, there's four ways to do venture. Pre-seed seed, so think sub-150 funds. There's 1 ,000, close to 2 ,000 today. in the US alone. Messy middle, we talked about. And then there's a lot of firms there by there. Thousands, not thousands, hundreds of firms.

21:24And 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 selectively 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.

22:04Company comes out of Pick Your Accelerator. I went from zero to five minute ARR in a month. 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 sung 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 actually arr that is 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 attraction, what's not.

22:40Valuations 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?

22:55Jen Kha: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. $300 million ARR,$400 million round or somewhere maybe around there. something like that. Yeah. Like that. Okay. So a lot of people would say like, that's crazy. Why did they do that? Yeah.

23:12David George: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 verse, you know, kind of misleading interaction 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

23:39Jen Kha:some of that, too. Yeah, there's probably some of that, too,

23:41David George:but, like, I go back to...

23:42Jen Kha: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 industry. This is always helpful, yes.

23:50David George:Yeah, but, like, I don't know, come back to, you know, is the market demanding more of your product? That is always the question that's supposed to note on my computer screen. Like, is the market... 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. 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? They did a really good job commercially early days, like, because they were smart.

24:18David George:They were, there was like a research plus lawyer combo. You know, they got some momentum and some, you know, high profile law firms to sign up early days. 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. You know, an AI company. From a retention standpoint. Not retention. No, it's actually usage. Usage of it. Now, fast forward, post-reasoning models, like that totally flipped. And you could see, you know, absolute takeoff of adoption, right? And so a bunch of different things happened at the same time.

24:55David George: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 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 layer down. Like everyone can do cohort analysis.

25:30David George:Everyone can look at renewal data. 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. That's why it's so important to have the early state business because they're the deepest in the technology and the products. And they, you know, they obviously saw it in Cursor 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? They keep you very gainfully

25:55Jen Kha:employed at this firm. 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 worries around, you know, are we catching a falling knife here? Just like the timing of the market where we are, like are things overheated, et cetera. And so we talked a lot about this at 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 you mentioned earlier one aspect of it around AI making it more challenging to evaluate opportunities and funds.

26:33Jen Kha:But the other aspect of it as well is the LP historically has not been incentivized to actually embrace change in some respects, right? You know, this is very much a job where the end goal is actually somewhat diametrically posed with the risk tolerance of the GP. and this is just the mechanics of the industry, but oftentimes 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 error of omission. And like that is fireable. But LPs on the flip side only get fired if you invest into a mentor. So in some respects, like the incentive outcomes are actually completely opposite of the GP.

27:16You don't get fired for investing in IBM if you're an LP.

27:19Jen Kha: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. Right. Maybe slightly below the benchmark, you're keeping your job. Right, right. That's fascinating. And also for most folks, and I'll leave fund-to-funds out of the equation because it's a different piece, but 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...

27:49It's incentive misalignment.

27:50Jen Kha:It's actually quite... So where do we go from there in terms of the LP kind of role? And see, like, I think we also have an important role and function. We oftentimes 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. 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.

28:25Access selection sizing is what LPs do. So access, you could argue you have the data to figure out who has done well historically. Out of those 20 firms, out of 3 ,000, like, you are not going to see consistency, right? Maybe half of them are consistent. 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 sizing and it's critical from an LP standpoint 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.

29:01So 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. And again, going back to the average in venture, that's just not what one should be. It's 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 one and a half to two X net without the lockup, without the risk you're taking in venture. You talked about 6 % loss ratio. P doesn't have that. P has other problems we can talk about today when it comes to AI software.

29:30But so portfolio construction sizing for an LP is critical. 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. Great, you 10x'd it, it returns 10 % of your fund. It has not moved the needle at all.

29:50Jen Kha:Yeah, yeah, yeah.

29:51David George: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 hard for me to answer because all I do is venture growth. So I would be biased to say it should be supersized.

30:18Jen Kha:We like that answer though. Look at the public markets today. We vibe coded like something that created a great way for us to assess AI resiliency of public companies. And now we're doing that on the private side and it's helping us hugely in our growth equity portfolio. But in the SaaS public markets, there are only like 15 to 20 companies max trading above 10 times revenues, which is quite an insane number because it used to be dozens and dozens a few years ago. And every one of those companies, for the most part, is showing acceleration of growth from AI. You read them monitoring security, deployment of agents, etc.

30:53So 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 that in your portfolio. 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. That's just not happening. They're looking for the system of record that can show acceleration with an AI native management team. So that connective tissue of AI is actually across every asset class today.

31:29And one of the strongest way to play it is probably through venture. But that's where selection access and portfolio construction is really critical. Yeah.

31:38Jen Kha:Well, even the exits that we were talking about, you know, Silver Lake potentially, you know, buying Workday, for example, just like doing more provocative things, for example, in private equity land when you have the capabilities to potentially infuse and bring them into the future as a part of that. The other version of it is also exits in venture now way exceed private equity. Like, you know, like I was looking this up last night. Private equity this year, the biggest exits are the buyout of EA, which was like around$50 billion, and Medline, which is around$50 billion. Cursor, let's not, let's exclude the IPOs.

32:12Jen Kha:Like, Cursor was, the M &A sale to SpaceX was way bigger than that. The problem with that is, like, you look at the pre-Chat GPT vintages in private equity. you would have paid, I don't know, 15 to 20 times EBITDA for a software asset that's growing 10, 20 % max. If you look at the public markets today, that asset is trading at two times revenue. And the problem is not that 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.

32:50Our data shows one percentage of growth in the public markets is equivalent to three percentages of EBITDA. So, by the way, it's funny because in COVID, everyone was like, we need to be profitable. It was the inverse.

32:59David George:And now it's the opposite. No, no, 21, it was the inverse. Inverse, exactly. It's basically like fully aligned with risk, right? Exactly. It's correlated with like risk in the public markets. Exactly. So, unfortunately, a lot of those private equity deals, they're not growing fast enough. 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 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.

33:31David George:It's really hard to do. I just spent a little time with the founder and 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. He did. This is the thing that like is really, really hard to do. Yeah. You know, it's an end of one right now. But you know, there's a bunch of really good founders who are capable of those businesses, public markets, private markets, who I think are going to take a crack at it. So we'll see.

33:55Jen Kha:Yeah. Maybe on that thread though, DG, because we also sometimes get the pushback as well, like for folks who have been in venture and allocated to venture, they might also have a similar problem where they do have the legacy SaaS businesses also as well. Like what's the balance between how you think about the historical stuff? Let me ask you that question. So I'm going to piggyback off of Jim's question to you. You got a pick at 2016 through 2021, pre-chat GPT vintage software company that was fine, but doesn't have that AI-nated features anymore. It's not accelerating. It's growing like 30%.

34:27On the venture books, it's at like 10, 20 times revenue. Eastlake can't go public anymore. No one cares to take that public. Silverlake 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 yeah so i would say

34:46David George:it's like 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 like what do you actually think is happening and um you know not don't give me 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 are able to adapt, right? Part of the thing that we're monitoring, which makes us extremely bullish about AI is just actual diffusion into the real economy, right?

35:26David George:So coding, I think if you were to paint the bullish scenario for regular software and for slower pace of change, you would say, you know, coding hit, but that's kind of a head fake, right? Like coding is perfectly documented, right? So it has like perfect data, it's verifiable and it's simulatable, right? And so 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. And then some of them will evolve and have AI, you know, solutions and they'll change their business models.

36:07David George:And I think that's a must. 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 months, like that's, I think there's a little bit of a growing realization in that. All that makes me super, super, super bullish on AI though, right? So like if you look at our portfolio, you know, we have some of those companies put about, 95 % of our nav is not in those companies, right? Like it's in the companies that are, you know, growing very fast, accelerating, et cetera. You know, the average, I think it was the median company in the U.S.

36:43David George:is spending$12 per employee on AI per month. The top 1 % of the data set that we've seen

36:52Jen Kha:is spending$7 ,000 per employee on AI per month.

36:58David George:so 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 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 mega cap tech companies. And yet it's probably on the back of adoption of like 10 million users, maybe 20, maybe 30 max.

37:34David George:And, you know, there's one and a half billion knowledge workers in the U.S. And I think it's going to transform the way we do a lot of work.

37:40Jen Kha:Yeah, Calper's famously lost out on billions of gains by not investing in their back. And they're making up for lost time now. They've converted their portfolio from 91 % to 58 and venture and growth from 9 % to 43%. There's probably some balance in between those things, but they're leaning hard into. But there's going to be a lot of value still that's going to be accreted in some of these historical companies. And I started joking around about Bendings, but that was probably a great outcome for Airtable, outside of the fact that they're going to actually spin off the hyper-agent piece of the business and actually, I think, do really interesting things with that.

38:13Jen Kha:But in the scheme of things, I think there's going to be a lot of homes for a lot of things. And this zero-sum thinking, I think, is probably the pitfall of which we would advise against.

38:20David George:So we talked about venture growth. We've talked about private equity. We've talked about public markets. And by the way, the composition of all of those have radically changed over the last 10 years. We also have had the emergence of entirely new categories that are available in the private markets, like private credit. 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. The vantage point we have is looking at private credit, which resides in a lot of private equity software portfolios, which is hundreds of billions. I'll give you one statistic.

39:01So you look at 21-22, about$200 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 and private credit is exactly that. They're looking at the public markets. You've had cesspocalypse. It's been a massive correction in software. 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.

39:46Jen Kha:By the way, even the 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 competitor is like, oh, the first thing I'll do is, of course, hire AI customer service agents because that's like an easy, low-hanging fruit.

40:22Jen Kha:Like, it turns out, if you don't actually build on 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. So oftentimes, 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 panacea for generating returns, especially when it's just so categorically different from a technological perspective to actually infuse that throughout the company as well.

40:54Yeah, you can't just throw an operating partner at the company and say, let's put AI on it. It just doesn't work. You need to completely, and 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.

41:12Jen Kha:Yeah. Yeah. Yeah.

41:13David George:So obviously this is a group that is very pro, you know, venture and growth, this category. 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, you see maybe the same logo in five, six different firms. And the question is, how do you get out of it?

41:50Jen Kha:And an IPO isn't actually a distribution milestone. It could take 12, 24 plus months before you actually get liquidity out of an IPO. Especially if you own 10, 15 % at IPO, it's going to take a long time if you're in the generational company. So we get that pushback a lot in terms of timeline to liquidity.

42:08David George:And then what is the sort of counter to that pushback? Well, counter to that pushback is going back to 3 ,000 firms, 20 do well 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. would you have wanted to sell Stripe, Gatterbricks, or any of those other companies three, four years ago? Like, the answer is unanimously no. 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 five years later. Cursor, from first acquisition, from first financing to acquisition, is short.

42:48So the best venture firms actually have fund-returning liquidity pretty quickly, maybe even quicker than private equity. But that subset of firms is tiny. Yeah.

42:56Jen Kha:Yep. Actually, very famously, a year and a half ago or so, we went to our Fund One LPs. At that point in time, the Fund One 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 on this? We recognized, you know, the job that we came to do is now done 16 years in. 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 make that exit because it's 17 years in. We've got to get this liquidity out.

43:27Jen Kha:We've got to wrap up the fund, etc. But, you know, so many LPs, I think, is very specific to certain categories, right? Endowments would prefer to let it run. Family offices, quite frankly, don't want the money back because they don't want to pay taxes on it. They'd rather have it continue to compound. Yeah. And so there is specific nuance with each LP group where it's very hard to paint a broad brush stroke on like across the board on everyone wanting the same thing. But I also think, to your point, the very best LPs, excuse me, the very best GPs have manufactured along the way liquidity. 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.

44:05Jen Kha: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 winner's IPO over time with the fullness of compounding as well?

44:16David George:Yeah, exactly.

44:16Jen Kha:I'm going to end on this note because I thought this is an interesting question that you and Gavin were tossing back and forth, DG, on. He 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, what do you think is going to be the next$100 trillion market cap? Oh my gosh, here we go.

44:41David George:I can't even think of those things. Yeah, that's probably two tech cycles away, not just one. 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 is in new product areas that haven't yet been touched, right? So like what, I talked about the sort of diffusion of the technology into the enterprise, like we're nowhere, right? You know, a year ago, everyone talked about consumer all the time. No one 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.

45:21David George:Like that's the skewmorphic version. 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 create a ton of value for consumers. And we're kind of nowhere on that. I mean, yeah, there's, you know, a billion ChattuckT users, but, you know, like, that's like scratching the surface. We are nowhere on robotics, but I think robotics is going to be bigger than the language stuff. And I think it's going to happen in the next 10 years. We are almost nowhere on autonomy, right? Like, there's fewer than 10 ,000 Waymos live in the U.S., way fewer robo-taxis.

45:55David George: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 yeah like we we've we've done nothing to scratch the surface either on care delivery or on drug discovery yet 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 all things physical world from defense to manufacturing to data centers. 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 done a lot with AI already.

46:37David George:But 10 years from now, we're going to look back and say, oh my gosh, like those other major areas created a ton of value. And so I'm excited that I think the next SpaceX AI or OpenAI are probably going to get created. and they'll probably be in those kinds of domains.

46:52Jen Kha: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 opportunities fund that you did. 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. I think the new fund you have is really going to help on the left side as well because the U.S. doesn't have a problem with energy generation it has a problem with speed to power that's permissioning, transmission, that's regulatory other countries are putting out 10x more renewable capacity a year so that is a real bottleneck and that means reimagining the data center you talked about the density being 10x plus you can't just repurpose an old data center for a new AI facility so this is where the new fund you have can create not 10, 50, but 100 billion plus opportunities as well.

47:49That can really solve the bottleneck. And I think that is a real concern because demand is not the concern. A lot of, I've heard LPs say, this is like the dot-com or this is COVID. It's not because the traction is real and it's not ephemeral revenue like COVID. The bottleneck could be supply, but if you have the right inputs, like the fund that you're now backing those companies, next generation chip companies, memory, et cetera, that's a huge opportunity.

48:12Jen Kha: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 ya. Thanks for listening to this episode of the A16Z podcast. If you liked 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 X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

49:13closures.