How AI Is Rewriting the Power Law of Venture Capital

a16z’s Jen Kha and David George sit down with Accolade Partners’ Aram Verdiyan to discuss how AI is changing the power law of technology investing, why the largest companies can compound advantages in ways that weren’t possible before, and what that means for how investors construct portfolios. They explore why AI may be much bigger than traditional software, with applications reaching into labor, healthcare, transportation, services, and other major parts of the economy. David explains why capital itself can now reinforce an AI company’s advantage by buying more compute, while Aram makes the case that AI should increasingly be treated as a core allocation rather than a satellite position. The conversation also gets into the changing economics of venture and growth investing, how to distinguish real AI traction from early hype, what AI means for legacy software and private equity, and why some of the largest opportunities may still be ahead in robotics, autonomy, healthcare, energy, and physical infrastructure.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  We've looked at the data of 3,000 venture capital firms in the US. Only 20 have achieved consistent three X net returns over the last two decades.

• 0:06Speaker 01

  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.

• 0:18Speaker 00

  AI is attacking every facet of the GDP. Transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time.

• 0:28Speaker 01

  Elon has talked publicly about Brockbot on Sam's side. He's talked about Astro and some of the long-running capabilities that are going to come out soon.

• 0:35Jen Kha?

  What do you think is going to be the next $100 trillion market cap company?

• 0:39Speaker 01

  It is possible that AI isn't just creating faster-growing companies. It may be changing the power law itself. In this episode, A16Z's Jen Kha and David George sit down with Accolade partner, Aram Verdiyan, to unpack what that means for technology investing and portfolio construction. For most startups, too much capital can become a liability. But David argues that frontier AI is different. Dollars can be converted directly into compute and compute can make the product better, reinforcing advantages of companies already at the frontier. They discuss why AI's addressable market could extend far beyond software, why Aram believes AI is becoming a core rather than satellite allocation,

• 1:21Speaker 01

  and why access, selection, and position sizing matter even more as the power law becomes more extreme. They also look at what comes next from robotics and healthcare to energy, chips, and data centers, and why some of the biggest outcomes of the AI era may still be in categories that barely exist today.

• 1:42Jen Kha?

  Welcome back to the A16Z podcast. Something fundamental has changed in how value gets created. Power law used to be just a feature of a cottage industry in venture capital, and now it's systemic throughout. And particularly, the three frontier model companies SpaceX, OpenAI, Anthropic, represent somewhere between $3.5 trillion of potential enterprise value. And shockingly, before SpaceX went public, a lot of our LPs and also the broader institutional allocator community didn't have a lot of exposure to it. And so we'll talk about today why potentially portfolio construction and asset allocation may have changed, why power law is not just only in the venture capital industry, and then particularly where and how value actually compounds today.

• 2:26Jen Kha?

  David George, Aram Verdiyan, thank you for joining me.

• 2:29Speaker 01

  Great to be here. Thanks for hanging out.

• 2:30Speaker 00

  Thank you for having me.

• 2:31Jen Kha?

  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?

• 2:41Speaker 01

  Right now, clearly, the power law is more extreme than it has been in the last 10 to 20 years of technology investing, probably going back to the emergence of the network effect-driven consumer companies. There are many reasons why that's the case. Increasing returns to scale have always been a dynamic in our business. Obviously, it's well-covered how a network effect business can have increasing returns to scale. But so can software businesses, right? And they can take different forms, but 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 in my career, you can take capital and throw it at a company and it compounds their advantage.

• 3:24Speaker 01

  And this is a thing, like, how do you screw up a startup? Well, throw too much money at it and have them hire 1,000 people. And then you create all these coordination issues and overhead issues and dueling priorities. And it sort of gets messed up because you can't hire enough people to do enough things fast enough. Now, that's not the case. You can throw dollars at compute and compute can make products and the businesses better. And so to me, it's not terribly surprising that the power law is more extreme. Right now, economies of scale are a very real thing in the AI market. I think it'll continue to be the case.

• 3:55Jen Kha?

  So, Aram, so first of all, you're not just one of our longtime LPs at Accolade, but incidentally, it's been exactly 10 years since you were actually an employee of A16Z. And so for your 10-year anniversary since you were last here, I've brought this gem back.

• 4:09Speaker 01

  Oh, my goodness.

• 4:09Speaker 00

  Oh, my gosh, this is amazing. How do you still have this?

• 4:13Jen Kha?

  So we dug in the catacombs and we made this extra-large version, not just for posterity here. I got this from the catacombs. But incidentally, during the last 10 years, a lot has changed in the world. And if you remember, at that point in time, people were bellyaching about fund sizes being too large back then.

• 4:31Speaker 00

  And you had one at a billion.

• 4:33Jen Kha?

  Yeah, the first one at a billion, first venture fund. Exactly, exactly. And so a lot has happened since then. How do you think about your venture portfolio juxtaposed against your private equity one? And then just also generally asset allocation, we've 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?

• 4:52Speaker 00

  Let's take venture today. We've reached $100 billion in revenue in AI. It took SaaS 15 years to get to the same point. AI did that in four years. And we're not even close to anywhere in terms of the penetration of demand. The reason DG is saying you can throw capital at it, that's a function of unlimited demand for inference. And we are at a point where AI is attacking every facet of the GDP. Transportation, labor, services, capital, coordination. There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time. And so you have the fastest-growing technology. It's hitting on all parts of the GDP. So as an allocator, it's hard not to make the case. It's not a satellite position. You should be core or a supercore in some shape or form.

• 5:38Speaker 01

  I'm very biased, but if you just think about the shape of the markets and how they've changed, since I started my career in private equity and growth equity, that was, I guess, 18 years ago. This was a cottage industry, and now it's not. And I talk about this all the time, but our asset class is $5 trillion to $6 trillion of value. And the dynamics around companies staying private longer, they're not going to reverse.

• 6:00Speaker 00

  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. So it's no longer just early stage and you IPO when you have 100 million in revenues.

• 6:12Speaker 01

  Yeah, the top decile outcomes, I think, used to be $10 billion and now they're like $40 billion.

• 6:16Jen Kha?

  Yeah, it seemed to be probably $100 billion by the time Anthropic and then OpenAI come out.

• 6:20Speaker 01

  Yeah, yeah. And look, this makes sense, right? The last cycle created $25 trillion in market cap, new market cap. And a bunch of that went to the incumbents.

• 6:27Jen Kha?

  Yep.

• 6:28Speaker 01

  But a lot of it went to startups and the new startups. And each one of these subsequent waves gets bigger than the prior one. And so our expectation is, take the $25 trillion and it's going to be a larger number.

• 6:38Speaker 00

  Yeah, and I'm constantly confused about the TAM of AI. Love your thoughts on this. Take healthcare. Healthcare spends $60 billion to $100 billion on healthcare IT per year. But AI is hitting on actual labor and the value of tasks that are being performed in healthcare. That's claims, billing, administration. That's a trillion-dollar industry. So the TAM of AI can be 10x plus bigger than traditional SaaS or healthcare IT. And what is that value? Well, what's the economic value of a task that's being performed? 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, you look at every wave, the incumbents are 10x smaller over time. So it's hard to estimate it, but I can tell this for myself.

• 7:24Speaker 00

  I've been chronically wrong about how big these outcomes can get.

• 7:27Speaker 01

  Yeah, same here. Yeah, labor, I mean, look, if you just look at like how much of dollars are spent in the US economy on labor versus software, it's something like 40 times more. Now, that doesn't mean importantly that labor is going to go away. I think labor is just going to get reinvented. And so we'll end up with sort of reimagination of the tasks that humans do. But I think that's actually the whole point of AI is 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.

• 7:53Jen Kha?

  Our legal counsel says to us often, it's like, I love Harvey. All my clients think they're lawyers now. And they could actually spar with me on topics where they would have probably been like, I don't really understand this. I'm just going to defer to you. So my billable hours have only gone up with the advent of the usage of AI. And so all those use cases are massively, massively, probably underappreciated. And we don't still even know.

• 8:12Speaker 01

  Yeah, they're expansionary. Yeah, that's the point. Yeah.

• 8:14Speaker 00

  There was this whole thesis where frontier labs are going to can advise the apps. Which layer is going to win? Turns out, everyone is sort of growing.

• 8:21Speaker 01

  Yeah, yeah, yeah. We just, a friend of mine did a podcast where he described everything is going to work kind of thing. And I describe it slightly differently, but we get questions all the time from LPs when they ask us like,

• 8:32Speaker 00

  Which stack, layer in the stack?

• 8:33Speaker 01

  Which layer in the stack is going to work? And I'm kind of like, I don't know, the market is going to be so big. Like, I think everything might work. Now, there's going to be a lot of companies that don't work. And there may be idiosyncratic categories that don't work. But I think by and large, it's far too limiting to think, oh, if open source does a good job, it's bad for the labs and vice versa. So we try and remove ourselves from thinking in a zero-sum way like that.

• 8:54Jen Kha?

  Extract it out because why do people think it's going to be a winner-take-all? And we can hypothesize, but you know, in the last era of technology, it was probably winner-take-all in a lot of categories. But this feels categorically different because we're re-underwriting a lot of the fundamentals. So maybe extract it out.

• 9:09Speaker 01

  Yeah, look, winner-take-all is an interesting way to describe it. Because if you just look at the market cap growth of all the leading technology companies, like, there are many, many, many that were successful that wasn't winner-take-all, right? Now, there's an important distinction. We very much are believers in the power law within a given category. So the winners will capture the vast majority of the market share and market cap. And second place is playing for scraps. But I think there will be a massive expansion of the amount of categories that we have, right? And so if you go back 20 years, CRM was not really a category. I mean, it was small. It was like Siebel Systems and things like that. But you know, now it's a massive category. And so I think the same thing will happen. We've seen it in every technology market that we invest in.

• 9:52Speaker 01

  Again, our approach is, in our business, we can tolerate loss, right? And 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 if you look at our best-performing venture funds over time, I think the loss rate is 60% or so.

• 10:11Jen Kha?

  On early stage, yeah.

• 10:12Speaker 01

  On early stage. Now, at the growth stage, 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 10x or more in returns. And so 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 the risk that we live with.

• 10:37Jen Kha?

  Yeah. And embedded in that is also kind of timing because I know, Aram, you and I lament on this in that I think a lot of folks oftentimes think that things are overheated in that moment in time. And then you look back in retrospect and it turns out everything was actually quite cheap. But then there's these aberrations in the market where it's probably actually true. And so I know you advise a lot of your LPs on the importance of consistency in venture capital, probably even more than any other asset class because you just never know when these technologies can come out. Maybe walk through that because there's a lot of institutional allocators out there who actually don't have access to a lot of the frontier certainly models. Now they're trying to play catch up and in some instances, probably maybe introducing some adverse

• 11:18Jen Kha?

  behavior that is a little bit too reflective of things being a little bit too frothy. So maybe unpack that for us.

• 11:24Speaker 00

  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. 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.

• 11:45Jen Kha?

  Sorry, say that one more. 20% have achieved.

• 11:48Speaker 00

  20.

• 11:50Speaker 00

  Less than 1%.

• 11:51Jen Kha?

  Wow.

• 11:51Speaker 00

  Consistent 3X net returns.

• 11:53Speaker 01

  That's incredible.

• 11:54Speaker 00

  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 TPI funds over a 20-year period? We found only 20 firms that have done that.

• 12:06Speaker 01

  Wow.

• 12:06Speaker 00

  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.

• 12:15Jen Kha?

  Yeah.

• 12:17Speaker 00

  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.

• 12:24Speaker 01

  Yeah.

• 12:25Speaker 00

  If you're late stage, DG, I'm curious if you'll agree, sizing is really critical.

• 12:29Speaker 01

  Yeah.

• 12:30Speaker 00

  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.

• 12:42Speaker 01

  Yeah.

• 12:43Speaker 00

  So we have found the right portfolio sizing and the ones that consistently have gotten access are in the 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. 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.

• 13:07Jen Kha?

  Yeah, for sure. Yeah, I just pulled up a tweet from our friend Endowment Eddie, who posted actually this morning. He said, "Interest in big VC funds has been driven by founders, not LPs. Founders more often than not want the brand that can scale, be a lifecycle investor, and help land customers/hires. LPs have slowly followed along, but most are still dragging their heels." Because it's actually, it's counter to conventional wisdom.

• 13:30Speaker 00

  Yeah. And the outcomes are larger, so funds can be larger. There are some exceptions. In those 20, there's some small firms that are focused on niche, vertical markets, or they're playing at a stage that's so much earlier than the bigger firms, where like there is not a lot of competition with the bigger firms. Now, the problem with that strategy is you have to stay consistent in terms of fund size and their strategy. If you start getting bigger over time, then you bump into the big firms. And I think it becomes really, really hard to stay consistent.

• 13:56Jen Kha?

  Yeah, yeah. Death of the middle.

• 13:58Speaker 01

  Death of the middle.

• 13:59Jen Kha?

  We said we were going to drink every time we said death of the middle, so.

• 14:02Speaker 01

  I've heard a couple of the many of our peers in the venture ecosystem, you know, like, but this, this death of the middle thing.

• 14:09Speaker 00

  How do you define it? Like, what's the middle?

• 14:12Speaker 01

  So I think like sort of what you described, like the highly specialized funds, you know, some of the ones that were very early to AI with like deep, deep, deep domain experts have done a pretty good job, right? Like they've done a good job and, 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. 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 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, there's a few other folks

• 14:55Speaker 01

  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, you know, sort of taking capital from a partner that they think can de-risk the outcome for themselves. Like that, if you were just to simplify it, that is the simplest way to describe what the founder really cares about. Now, they care about the partner, right? Like they care about the person. So you have to, 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.

• 15:41Speaker 01

  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. 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, 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, for her or him? If so, we win the deal. If we can help to make the outcome better, that's great.

• 16:24Speaker 01

  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, 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 that has knock-on effects for them. And then secondly, you know, by being a part of the winners and helping them, you know, in small ways, we, 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.

• 16:53Speaker 00

  One theory, curious to get your, both of your takes, is take pre-seed seeds. So sub 20, 30, $40 million valuation, sub $100 million funds. They can coexist with the big firms because at inception stage, say there are seven AI companies kind of doing the same thing. I would think a large firm like Andreessen would want to wait for a round or two until there's more relative certainty. Because one thing you don't want to do is be in the number two or number three. Like you said, you have to be in the category winner. So you'd rather wait for that round and actually double down and lead the A or the B. So the small firms, they can carve out a niche for themselves, a clip or two earlier than the big firms and actually have a right to win. They can do really well and be complementary to the big firms.

• 17:34Speaker 01

  Yeah.

• 17:34Speaker 00

  Do you agree with that or like?

• 17:36Speaker 01

  Yeah, yeah, yeah. And look, we have like very healthy relationships with, with seed funds across the ecosystem. We also do do seed ourselves, right? But pre-seed for sure, you know, sort of earlier than we typically.

• 17:46Speaker 00

  The seed you would do, I would think is like chunkier, bigger seeds, right? It's like a serial entrepreneur.

• 17:50Speaker 01

  They used to be As, yeah.

• 17:53Jen Kha?

  Yeah. That, that definitely is our seed. But that said, you know, we have our speedrun program, which we just came from actually earlier this, this morning. And I don't know, I think, I think the market is evolving where, where

• 18:05Jen Kha?

  because founders have such a 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. 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.

• 18:44Speaker 00

  Yeah, I spoke to a few founders at Speedrun today and they very much are hoping to stay in the orbit.

• 18:48Jen Kha?

  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 extending across the stack, 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.

• 19:13Speaker 01

  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, et cetera. 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.

• 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, there's 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, the business that.

• 20:12Speaker 00

  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.

• 20:22Jen Kha?

  Yeah.

• 20:22Speaker 00

  Like it's, and we talked about sizing in late stage where you can, whatever the size of your late-stage fund is, if you can at scale put 5% to 10% of your fund in one of the category-defining companies, the way you could do that is because you had an early-stage franchise that developed that relationship with the entrepreneur and the management team early on. It's really hard to come in as a de novo late-stage firm and write a $500 million check.

• 20:43Speaker 01

  Yeah, yeah, no, it's very hard. Yeah. I've, I've lived that world.

• 20:47Jen Kha?

  Aram, 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? 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.

• 21:05Speaker 00

  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 thousands, thousands, close to 2,000 today in the US alone. Messy middle, we talked about. And then there's a lot of firms that are better. Thousands or not thousands, hundreds of firms. And then the big firms. And then dedicated late-stage. So there's four ways to play it. We have done the larger firms for decades now. We've done the seed firms. We've selectively done a few in the messy middle and we haven't done dedicated late-stage for the reasons we talked about.

• 21:43Speaker 00

  How is it changing? AI is actually making our jobs harder than ever before.

• 21:49Speaker 00

  It's making it harder because rounds are larger in general. They're faster.

• 21:56Speaker 00

  The traction that's happening in the industry is confusing. And here's why it's confusing. You can have a company, I'm actually really curious to hear this from you because we hear this a lot. Company comes out of pick your accelerator. I went from zero to five million ARR in a month.

• 22:08Speaker 01

  Yeah.

• 22:09Speaker 00

  There's no renewal cycle yet on that company.

• 22:11Speaker 01

  Yeah.

• 22:12Speaker 00

  And they're raising off of that traction at huge multiples.

• 22:16Speaker 01

  Yeah.

• 22:16Speaker 00

  And a lot of times they're selling to each other in a cohort potentially. And it's not even ARR, but they're multiplying by 12. So, but for every, for nine companies like that, there's one really special one. That's doing a couple of million in ARR, actual ARR, that is a huge valuation that will go on to be the next cursor.

• 22:33Speaker 01

  Yeah.

• 22:33Speaker 00

  So it is really, really tough actually today to parse out like what's real traction, what's not. Valuations are really high. This is why the big firms do well. I actually think they can wait or they have enough relative certainty in the next round, then lead that round. But even then, there isn't a lot of certainty. When you guys did cursor, I don't think there was a lot of certainty. And how many, for how many months were people saying cursor is dead?

• 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.

• 23:03Speaker 00

  Tell me if I'm wrong, 7 million ARR, 400 million dollar round, or somewhere maybe around there.

• 23:07Speaker 01

  Yeah, something like that. Yeah.

• 23:08Speaker 00

  Like, that's, a lot of people would say like, that's crazy. Why did they do that?

• 23:11Speaker 01

  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.

• 23:28Speaker 01

  Secondly, like, it is hard to parse out real versus, you know, kind of misleading traction. And misleading in the sense that like you can't take market signal from it, not that anybody's doing any misleading.

• 23:39Jen Kha?

  Probably some of that too.

• 23:40Speaker 01

  Yeah, there's probably some of that too, 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.

• 23:48Speaker 01

  This is always helpful, yes. Yeah, but like, I don't know, come back to, you know, is the market demanding more of your product? Like, that is always the question that's supposed to know on my, on my computer screen, like.

• 23:59Speaker 00

  But how do you know that when the company's been only operating or selling for a couple of months?

• 24:02Speaker 01

  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 over time as an example, right? They did a really good job commercially, early days, like, because they were, they were smart. They were, there was like a research plus lawyer combo. You know, they got some momentum and some, 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.

• 24:38Speaker 00

  From a retention standpoint?

• 24:39Speaker 01

  Not retention, no, actual use, 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 happen at the same time. Lawyers got way more value out of the product. You could see it in usage and engagement. 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. 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.

• 25:25Speaker 01

  But, you know, that's the kind of signal that we look for. You gotta go, you gotta go layer down, like, everyone can do cohort analysis, everyone can look at renewal data. But like, understanding the texture of the market and what the customers actually want, need in their alternatives. I think, you know, that's how you make the decision. That's why it's so important to have the early stage business. Because they're the deepest in the, 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.

• 25:50Speaker 00

  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?

• 25:55Jen Kha?

  They keep me very gainfully employed at this firm.

• 25:57Speaker 01

  I agree that you are a prolific fundraiser.

• 26:00Speaker 00

  What's the biggest pushback you're getting from LPs on like the state of AI, the state of venture?

• 26:05Jen Kha?

  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. But the other aspect of it as well is the LP historically has not been incentivized to actually embrace change in some respects.

• 26:43Jen Kha?

  Right? You know, this is very much a job where the end goal is actually somewhat diametrically posed with the risk tolerance of a GP. And, and this is just the, 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.

• 27:16Speaker 00

  You 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.

• 27:24Speaker 00

  Yeah.

• 27:24Jen Kha?

  And so.

• 27:25Speaker 00

  If you miss, if you miss the frontier models, back to your first question as an LP, but you kind of were along the benchmark.

• 27:30Jen Kha?

  Right.

• 27:31Speaker 00

  Maybe slightly below the benchmark, you're keeping your job.

• 27:33Jen Kha?

  Right, right.

• 27:34Speaker 01

  Yeah, that's fascinating.

• 27:35Jen Kha?

  And, and also for most folks, and I'll leave fund to funds out of the equation because it's a different, different piece. But, 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.

• 27:49Speaker 00

  It's incentive misalignment.

• 27:50Jen Kha?

  It's, it's actually quite direct. So, so where do we go from there in terms of the LP kind of role and seat? Like, it, I think we also have an important role and function we oftentimes talk about. In a 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, et cetera.

• 28:25Speaker 00

  Access selection sizing is what LPs do. So access, you could argue you have the data to figure out who has done well historically.

• 28:35Speaker 00

  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 your 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. 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. And again, going back to the average of venture, that's just not what we want.

• 29:14Speaker 01

  It's not compelling enough for the liquidity.

• 29:16Speaker 00

  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.

• 29:26Speaker 01

  Right.

• 29:27Speaker 00

  P has other problems we can talk about today when it comes to AI software, but.

• 29:32Speaker 00

  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. 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, yeah.

• 29:51Speaker 01

  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

• 30:02Speaker 01

  percentage of overall capital allocated to venture and growth compared to private equity or public markets or real assets? Credit, whatever.

• 30:11Speaker 00

  Yeah, this is the, 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.

• 30:22Speaker 00

  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 10x revenues, which is by the way, 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're either monitoring security, deployment of agents, et cetera. 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. That will have impacts in the public markets.

• 31:07Speaker 00

  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. But they're looking for the system of record that can show acceleration with an AI native management team.

• 31:22Jen Kha?

  Yeah.

• 31:23Speaker 00

  So that connective tissue of AI is actually across every asset class today.

• 31:28Jen Kha?

  Yeah.

• 31:29Speaker 00

  And the, one of the strongest way to play it is, I mean, it's probably through venture.

• 31:33Jen Kha?

  Yeah.

• 31:34Speaker 00

  But that's where selection access and portfolio construction is really critical.

• 31:37Jen Kha?

  Yeah. 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, 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, 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. Like cursor was, the M and A sale to SpaceX was way bigger than that. And so.

• 32:16Speaker 00

  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.

• 32:29Jen Kha?

  Yeah.

• 32:31Speaker 00

  If you look at the public markets today, that asset is trading at two times revenue.

• 32:34Speaker 01

  Yep.

• 32:35Speaker 00

  And the problem is not 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 three percentages of EBITDA.

• 32:55Speaker 01

  Yep.

• 32:56Speaker 00

  By the way, it's funny because in COVID, everyone was like, we need to be profitable.

• 32:58Speaker 01

  It was the inverse.

• 32:59Speaker 00

  And now it's the opposite.

• 33:00Speaker 01

  No, no, twenty one, it was the inverse.

• 33:01Speaker 00

  Inverse, exactly.

• 33:02Speaker 01

  And then post COVID, it's basically like fully aligned with risk, right?

• 33:06Speaker 00

  Exactly.

• 33:06Speaker 01

  Correlated with like risk in the public.

• 33:07Speaker 00

  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.

• 33:31Speaker 01

  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.

• 33:39Speaker 00

  He did.

• 33:40Speaker 01

  This is the thing that like is really, really hard to do.

• 33:43Speaker 00

  Yeah.

• 33:43Speaker 01

  You know, it's an N 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?

• 34:11Speaker 00

  Let me ask you that question. So I'm going to piggyback off of John's question to you. You got a pick at twenty sixteen through twenty twenty one, pre chat GPT vintage software company that was fine, but doesn't have that AI native features anymore, is not accelerating, it's growing like thirty percent. On the venture books, it's at like ten, twenty times revenue. It's not can go public anymore, like no one cares to take that public.

• 34:34Speaker 01

  Yeah.

• 34:35Speaker 00

  Silver Lake has no interest in that company anymore. They would have a year ago, they don't.

• 34:39Speaker 01

  Yeah.

• 34:39Speaker 00

  What happens to that company?

• 34:43Speaker 01

  You know, look, it's like.

• 34:43Speaker 00

  We have a lot of exposure to those companies too.

• 34:45Speaker 01

  Yeah, I would say it's like, it's very TBD, right? Like, you know, I was with 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, you know, don't give me, he actually said to me, don't give me a podcast answer. Which is ironic. 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?

• 35:26Speaker 01

  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, 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. And I think that's a must.

• 36:09Speaker 01

  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 ninety five percent of our NAV is not in those companies, right? Like it's in the, 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 US is spending twelve dollars per employee on AI per month.

• 36:49Speaker 01

  The top one percent of the data set that we've seen is spending seven thousand dollars per employee on AI per month. 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 one percent 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, than the mega cap tech companies. And yet it's probably on the back of adoption of like ten million users, maybe twenty, maybe thirty max.

• 37:34Speaker 01

  And, you know, there's one and a half billion knowledge workers in the US 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 that they've converted their portfolio from ninety one percent to fifty eight and venture and growth from nine to forty three. There's probably some balance in between those things, but you know, they're leaning hard into. But there's going to be a lot of value still that's going to be created in some of these historical companies. And I was sort of joking around about Bendings, but that was probably a great outcome for Airtable. You know, outside of the fact that, you know, they're going to actually spin off the hyper agent piece of the business and actually, I think do really interesting things in that. 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, would advise against.

• 38:20Speaker 01

  So we talked about, we've talked about, you know, venture growth, we've talked about private equity, you know, we've talked about public markets. And by the way, the composition of all of those have like radically changed over the last ten 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?

• 38:46Speaker 00

  In software in particular?

• 38:48Speaker 01

  Yeah, I'd say in technology.

• 38:51Speaker 00

  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.

• 39:00Speaker 00

  I'll give you one statistic. So you look at twenty one, twenty two, about two to three hundred billion in LBO software transactions happened with over two hundred billion in that taken out. The average valuation for those software deals were twenty five to thirty two 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 SaaS apocalypse, 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. Like 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.

• 40:28Jen Kha?

  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 related on top of it. So oftentimes, sometimes we hear from folks like, whoa, 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.

• 40:54Speaker 00

  Yeah, you can't just throw an operating partner at the company and say, let's put AI on it. It just doesn't work.

• 40:59Jen Kha?

  Yeah.

• 40:59Speaker 00

  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, yep.

• 41:13Speaker 01

  Yeah, 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.

• 41:30Speaker 00

  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? And so

• 41:51Jen Kha?

  And an IPO isn't actually a distribution

• 41:54Speaker 00

  It takes, it could take 12, 24 plus months before you actually get liquidity out of an IPO. Especially if you own 10, 15% at IPO. Like, 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.

• 42:08Speaker 01

  And then what is the, what is the sort of counter to that pushback?

• 42:14Speaker 00

  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.

• 42:25Speaker 01

  Yeah.

• 42:27Speaker 00

  Would you have wanted to sell Stripe, Garter Bricks, 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.

• 42:41Speaker 01

  Yeah.

• 42:41Speaker 00

  Cursor, from first acquisition, from first funding to acquisition is short. Like, so the best venture firms actually have fund returning liquidity pretty quickly, maybe even quicker than private equity, but that subset of firms is tiny.

• 42:56Jen Kha?

  Yeah, yeah. 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 decision because it's 17 years in. We've got to get this liquidity out, we've got to wrap up the fund, et cetera. But, you know, so many LPs, I think it's 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.

• 43:42Jen Kha?

  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. And now in this next cycle, it's can you actually get some early liquidity out through M and A? And then let, you know, potentially the winners IPO over time with the fullness of compounding as well.

• 44:15Speaker 00

  Yeah, exactly.

• 44:17Jen 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?

• 44:39Speaker 01

  Oh, my God. There we go.

• 44:41Speaker 00

  I can't even think of those terms.

• 44:43Speaker 01

  Yeah, we're, that's probably two tech cycles away, not just one.

• 44:48Speaker 01

  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. Like, that's the skeuomorphic 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, it's going to create a ton of value for, for consumers. And we're kind of nowhere on that.

• 45:31Speaker 01

  I mean, yeah, there's, you know, a billion chat G users, but, you know, like, we're, 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 US, way fewer robotaxis. And then, you know, a lot of open space for others to build in that area, too. We, you know, healthcare is 18% of GDP.

• 46:03Jen Kha?

  Yep.

• 46:03Speaker 01

  Like, 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. 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, we've done a lot with AI already. 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.

• 46:43Jen Kha?

  Yeah.

• 46:44Speaker 01

  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.

• 46:52Speaker 00

  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. 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.

• 47:14Speaker 01

  Yep.

• 47:14Speaker 00

  The VC ecosystem supports that well.

• 47:18Speaker 00

  I think the new fund you have is really going to help on the left side as well. Because the US doesn't have a problem with energy generation. It has a problem with speed to power.

• 47:26Speaker 01

  Yeah.

• 47:26Speaker 00

  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. Well, you can just repurpose an old data center for the, 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. That 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's, you're now backing those companies, next generation chip companies, memory, et cetera,

• 48:11Speaker 00

  that's a huge opportunity.

• 48:12Jen Kha?

  It's time for machine age. Let's bring the machines.

• 48:15Speaker 01

  I love it.

• 48:15Jen Kha?

  All right, let's close on that. Thank you both so much. It was super fun.

• 48:18Speaker 00

  Thank you for having us.

• 48:19Jen Kha?

  Awesome.

• 48:19Speaker 01

  See ya.

• 48:22Erik Torenberg?

  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.

• 48:48Erik Torenberg?

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.