The Hidden Economics Powering AI

26:51Google is also very focused with their Gemini models on coding, and we've seen a bunch of really good improvements and promising progress out of them. So as long as there's multiple players in the market, I think you'll continue to see costs go down. And again, in light of that, relative to, you know, sort of mature industry type SaaS stuff or infrastructure stuff, we're a little bit more lenient, you know, on assessing a company's gross margins today. We don't want to go invest in a bunch of companies with zero gross margins, and we don't do that. But, you know, if you sort of took like gross margins, retention rates, and sort of like ease of customer acquisition, like I place far more emphasis on making sure that we feel like there's greatness in those latter two.

27:36And, you know, give them a little bit more benefit of the doubt that they can improve on the first. For sure. Yeah, I flip back to this, because I, I've seen this personally myself, like a dozen plus times, but but something you said that really just clicked for me, which is to say, like, you know, a lot of people try to make comparisons to the dot com era. And they're like, Oh, remember we measured eyeballs as well, right? Like, isn't that akin to in terms of retention or usage? And it's like, yes and no. This is reaching such an in mass so quickly because of all the reasons you alluded to earlier, David, in terms of ability to just pull up a website and actually try it.

28:09But also people are paying for this. And it's an interesting juxtaposition when you think about the last cycle of things we paid for, so to speak, like a Spotify, you know, subscription or a Netflix subscription, where as soon as, you know, obviously pressures come from from a budget standpoint, those are first to go. But this, you'd probably sacrifice a few things, just given how much of an impact it has made either professionally for you personally, and accelerated your productivity and hopefully time in terms of acceleration as a part of that. Thomas's question, I'm happy to take Thomas's question.

28:44Yeah, it's from, this is from Thomas. So it seems like there's downward pressure on the likes of open AI on consumer pricing, yet the cash burn of open AI is ramping up to levels not seen before previously, you know, reports of a billion dollars plus per month. How does a cash burn moderate in the future relative to what you think, in your opinion? Yeah, so, um, so, yeah, so, so what I was saying is, like, I think, effectively, like, there's greater consumer stickiness than you would think. And there have been a tremendous amount of free alternatives thrown at consumers over the last 12 months.

29:18And it hasn't had any impact on their business. Now that could change over time. But so far, what we've seen is no effect that creates price pressure. And if you think about what I had described earlier, which is like, call it a billion people using it and only 30 million people paying for it. I think there's way more upside to monetize the base than there is risk of price pressure on, you know, today's 30 million people paying for it. And so, so I think there will end up being like, if it's a P times Q, and this is, you know, we're talking about chat GPT, I would say it probably applies to, you know, most of the consumer facing stuff in the industry.

29:56If there's a P times Q, which is like price times quantity, which is the really simple way to think about these consumer internet businesses. Q is like, at this point, they've gotten so big on the, on the monthly active users, like, over the course of the next five years, like maybe it gets to 2 billion or something, I don't know. But like the Google and Facebook properties are in the twos, billions. So there's, you can only get so large. But I think there's a tremendous amount of room to run actually on the upside on the P on the price. So again, if you think about, you know, how are they monetizing today?

30:26You know, it's 30 million people out of a billion, you know, at a modest subscription that is not really reflective of like real price discrimination yet. So I suspect the P is probably like a thing that we get surprised on the upside by. I think about lessons learned from previous internet era companies. And I remember looking at internet companies 10 years ago, and we would always look at Facebook and Google. And we're like, okay, Facebook and Google, they're monetizing their users at X. That's the max we could get to. And the big story about what's happened over the last 10 years is they've 8X'd their own monetization of their users.

31:07And so I suspect that if they have some thoughtful ways of monetizing free usage while still maintaining trust, there's probably more upside than downside on pricing to Thomas's question. And then on the burn, most of the burn comes from research, like R &D, and so future investments. And we could apply this to the whole industry of all the model companies. But we could talk, if it's specifically OpenAI, I'm happy to address that one. But on the open AI side, I think they're an advantage position because they have the consumer base and that's stickier. Like my family, like my parents in Kentucky use ChatGPT.

31:59Like if there's some better, slightly better model that comes out, like they're not going to switch. And so I think that's pretty durable. and so it's probably a better position to be in to fund those research efforts for continued model development. There used to be a thing which was enterprise companies are stickier than consumer companies. In this case, these are developers buying these things on the B2B side for the most part today, like buying kind of raw access to the models. And that's not very sticky yet. I think it's possible that it does get sticky over time. But as of right now, it's not very sticky.

32:45So to the point about coding models, if there's a new coding model that comes along that's better than the latest version from Anthropic, like our coding companies will just switch. And it's pretty easy to do because it's an API call. And so I think interestingly, enough, it's a little bit different this time where the consumer's a little stickier. I think that gives you an advantage. And I think the companies will not irrationally spend on new model development if there's not a financial return. I would say one of the things that we've observed over the last now almost five years of spending time with these companies is a lot of the founders started as like research brain AI people that were like, Like we're going to, there's going to be no economy and like everything's going to end because we're going to have AGI.

33:37And it's like, and then you know what like has happened? Like competitive forces have kicked in and they've become like hard capitalists. And so, you know, my expectation and from conversations with them, you know, on an ongoing basis is, you know, they're not going to do totally irrational things on the research side if there's not going to be a financial payback for them. So can DG also comment on durability of revenue for many of the AI applications built on LLMs, i.e. outside of OpenAI, AnthropicXAI? Yeah. It depends on the nature of the use. So I think some of them are really sticky. So, you know, companies, you know, like medical scribe stuff, I think is pretty sticky because there's a bunch of doctor workflow built around it.

34:27Um, uh, you know, customer support, I think is pretty sticky. Some of like the high end financial analysis type stuff, I think is pretty sticky. Um, those specific areas you think are more stickier than others. Yeah, I think the more stuff that gets integrated and the more company specific, uh, like kind of rules built around the model stuff you have, the stickier it's going to be. and so you know I think stickiness comes in the form like in software in applications like the same way it's kind of always come with software I'm sure one of my early stage partners has written a blog post about it because we talk about it all the time but you know it's stuff like integrations you know rules engines workflows you know and stuff like that and you know sort of enterprise capabilities.

35:19So like, I think that... If you play the customer, for example, the rules to go down the sequencing of how to troubleshoot are so embedded that you probably wouldn't experiment a ton once you've got a workflow, you know, across multiple different scenarios that... Yeah, yeah. And even like a style with which something engages. Like a lot of companies that are in customers of these things are like brands and they care about the way that, you know, like the customer support agent interacts. And so I think that stuff's probably pretty sticky. I think there's a bunch of stuff that's not sticky at all.

35:55So some of the emergent behavior that's like, you know, I don't want to talk negative about anything. But like some of the stuff that's like not as sticky is like experimental usage of tools to build, you know, some of the internal tooling, software replacements or like very low end prototyping of websites and things like that. I just think it's like TBD, you know, who the players are and what the use cases are. And I think, I think the market for that will probably segment out. Like we're already seeing it some where some of the tools are just being kind of used for prototyping. And then some of the cool tools are being used to like actually build and deploy apps.

36:38And I think you'll see, you know, kind of continued bifurcation of those things. But, you know, it's so early. I don't think that like companies are going to vibe code up like they're Salesforce.com. It's just not worth it. Like it's not like core competencies. I hope they would. I wish we could vibe code away our Salesforce.com. Okay, I'm going to speed run through some of these questions because I recognize we've opened the floodgates on them and I want to make sure we get through as many as possible. So Umberto asks, given the speed of go to market for AI companies, do you think 100 million AR is the right milestone to measure against?

37:14Or are you starting to move the goalpost on what success is there? Yeah, it's so funny. It used to be, I can't even remember, one of the VCs coined the term triple, triple, double, double or something like that. And it's like, that looks very modest. It's something like that, yeah. And that time compression has massively gone down. So I got to look up the stat. We ran the analysis on it recently, but it was like the top companies that we've seen have gotten to 10 million, then 100 million like four times faster or something like that. For us, you know, it's really important that we have market context and like real-time market context to make judgments about what great looks like.

38:01And so I don't have an absolute answer for you, but if you were to sit there and look at, and like, and listen to one of our investment discussions, we are comparing like the growth of, you know, XYZ new app to like cursor and, you know, nothing looks like cursor. So it's unfair, but like cursor and Decagon and Abridge and 11 labs, you know, and not like Shopify and DocuSign and, you know, those companies. So, you know, if you're not like fully in the market and seeing everything and meeting all the companies, you're not going to be able to have that context to assess what grade is at any given time.

38:47For sure. For AI companies, what do you think is a good question from Aditya. What do you think about seat-based pricing versus usage-based? And what are the experiments that some of the startups you've seen do well around configuring pricing in this age of AI? Yeah, this is like the big question. I love this question because I feel like on X and other blogs, people have written all these like really long posts about, you know, the new prices that are going to happen and with AI and like, it's a, to me, it's like a little hand wavy, like, one, it's subject to, you know, innovations in the technology side and the products getting better.

39:27And then two, we've really only seen a true business model innovation, like early, early days in one area. And that's customer support, because you can kind of like, definitively resolve a task. So even with that, like, it's hard to do. And so, you know, the question, maybe to reframe it would be like, we had, actually, we had like licenses, right? Like, you know, perpetual licenses with maintenance. And then we switched to SaaS, and that was mostly seat based. And that was huge innovation, very disruptive. That was a huge enabler of the startups actually going to beat the incumbent software companies because it was so disruptive at the time.

40:09Then we got to usage-based. And so, you know, obviously all the cloud companies run that way. You know, Databricks runs that way, Snowflake, et cetera. And then the hope would be that I've seen, you know, and heard about is like, with AI, can you just monetize the replacement of tasks humans do? And so when I talked about the customer support piece, like, that's the furthest along in the monetizing the tasks that humans do. But that's super, super early. In all the other areas, I think it's really early. End customers want to buy stuff on seat and consumption-based pricing. And you kind of got to meet the market where it is.

40:57So for now, we're not seeing hugely disruptive things. I think as the capabilities get much better and sort of measurability of the task completions gets more objective, perhaps, maybe we get there. But I would say it's like super early days and I'm low conviction that we end up, you know, five years from now with all the software companies monetizing in a completely different way. Absolutely. I mean, by the way, sorry, this is one more thing. This also goes to my point that I made earlier about surplus. Like, it's going to be really hard to capture that surplus unless you can monetize based on the value of the completed task.

41:39But because that's going to be really hard to measure, and there's going to be competition in the market, I suspect that a lot of the surplus or savings may end up in the hands of the customer. Again, you could probably still build really interesting, good companies that have really high market cap. But that is like, you know, sort of directly related to who captures the value and the surplus. Like, like, like the, I always say like the steam engine got invented and like you didn't price that like the steam engine didn't get priced based on like the calculation of how many, you know, humans it replaced in doing a specific task.

42:17Competitive forces kicked in and, you know, it was priced at some like appropriate, you know, competitive level with a return on capital while still like capturing a lot of value, but delivering much more value to the end customers or the users of the steam engine. I think the same will happen here. For sure. Actually, it's a good transition for us to actually talk about what this all means for growth, given this setup. I try and sit in on as many pitches in our early stage practice as I can, just because I feel like I learn a lot about, you know, what's starting, what are interesting founders starting to hone in on and, you know, in what areas are they building.

42:58And our early stage team is doing a great job of, you know, doing the most exciting early stage deals. So that to me just bodes really well for the next 12 to 24 months of deal activity for us, you know, looking out.

43:14All right. Growth partner. So this is a crazy looking chart. I mean, it's not surprising because it was like the first thing we talked about, which is, you know, tech markets are big. And of course, companies are staying private longer. But you can see it in the chart here. It used to be that companies would go public within the, call it like five to 10 year window of inception. And despite the fact that the companies are growing much faster and getting better quicker, they're staying private way longer. So all that is to say, it's like 14 years. And I think that's getting even longer. If you take the market cap of the private markets valued above a billion dollars, We could argue are some of them overvalued, undervalued.

44:06But that whole value in aggregate is like$3.5 trillion. And that's like 11%. $3.5 trillion is like 11%, 12 % of the NASDAQ, 10 % of the NASDAQ, something like that. If you go back 10 years ago, that whole private market cap of$3.5 trillion was like$500 billion. So over the last 10 years, the market cap of these private companies is like 7X. So there's huge growth. Like, again, that is related to this point, which is some of the best companies taking longer, deciding to stay private longer. But, you know, it's pretty stark. So the other thing that's going on, and I mentioned this earlier with Snowflake, is like the public markets are no longer the place of extremely high growth.

44:53Like it, because of this, and it's sort of logic, It's logical. It follows, right? But something like 5 % of software and internet public companies are forecasting 25 % plus next 12 months growth. So 95 % of the public market universe in software and internet is growing less than 25%. So if you want access to like the, and this is, it's a little bit too bad because, you know, there's obviously implications for retail and stuff like that. But the reality of what's happened is the high growth segment of new technology companies is all living in the private markets now, you know, for the most part.

45:35And so I don't see that changing. I do think, you know, Figma and a bunch of good companies, like I think a bunch of good companies will go public. But this is a trend that, you know, is pretty longstanding and I don't see it reversing anytime soon. So we have a bunch of really good AI companies. Some people have asked, they're like, oh, isn't there like crowding that's happened in some of the best companies? And what I would say is like, one, we're getting in a lot of these companies much earlier than others are able to. And two, even for the ones that are later where there are bigger rounds, it's really important that we have that early relationship because it gives us ball control and access into the rounds.

46:20and XAI, first outside money and beyond Elon. So not only, you know, and a lot of these are growth fund kind of first money in. So, you know, it's important to get in earlier to generate returns, but also to position us, you know, with management and to have access to shape later rounds. If I were just to spend a second on, you know, how we have like our shaped our AI strategy, there's basically two parts to what we're doing. Um, one is the companies where they're like flying off the page, you know, uh, like undeniable momentum. Uh, so, you know, companies like Cursor and Decagon and 11 and Abridge and, and, you know, others.

47:03Um, the second bucket is, um, very early deals in the very, very, very best teams in the market. And I say very, very best teams in the market, like with emphasis, um, because I think we're talking about like the top five teams in the world and anything beyond that we're not doing. These companies, they're sort of a different shape. They're growth dollars earlier than normal. And there's a higher degree of variance in the business outcomes. But because the teams are so special and so great, we feel like business risk looks very different from capital risk. And so we feel like there's it's kind of they're kind of asymmetric bets where the asymmetry lies in the fact that we're probably downside protected because the team quality is so high and there's so much demand for talent.

47:58Like, you know, we would be downside protected even if it doesn't work out. And so high business, you know, sort of variance, but, you know, sort of asymmetric capital or returns profile that looks a little bit different than a typical early stage investment. Talk about timelines to exits, because on one dimension, you know, folks may take the reaction of, gosh, you know, private for longer means just extension of hold periods for LSV companies. Maybe the reframe on it is, why is that great for us, but frankly, as private investors? And why do we think it's actually advantageous in some respects for some companies to actually delay time to IPO?

48:33And how are you counseling folks as a part of that? Yeah, I mean, it's a tricky issue. I mean, I said it right up front, which is like, we get paid on DPI too. That's how we compensate ourselves and our teams. And so we care a lot about it. I would say we're in a fortunate position where our portfolio is pretty good. And we've had a number of companies that have exited to the public markets or sold themselves. And so, you know, we're not in a position where we haven't been able to deliver some liquidity. For those sort of champion companies that have been private for a long time and are getting a lot of coverage, I think each kind of has idiosyncratic reasons why they've wanted to stay private.

49:17And, you know, I think I'm confident that they will go public. So I think most of them will probably end up going public. It just will take a longer amount of time. One of the big things that we've observed is the private markets have adapted to some of what you get from the public markets. And we've fortunately been very active in some of those situations. So things like, you know, tender offers in the private markets. A big thing that is hard for private companies is competing for talent. And, you know, part of the reason why it's so hard is because public companies grant RSUs that typically invest on a quarterly basis.

49:59And so they hit your account. They're already tax withheld. And it's basically like getting paid a lot of cash. And it's hard for private market companies in some cases to compete with that. So, you know, some of the bigger, better ones have done more regular tenders. and we've been very active in shaping those with the company. So to me, it's a balance. We kind of want what's best for the company. If there's strategic reasons why it benefits them to be private, that's great and we'll help enable that. But we do recognize that obviously, you know, our goal is to monetize great investments. We're not there yet where we feel like there's a need to do unnatural things.

50:37I just don't see that being the case. For sure. Sure. And I do want to call out as well, I'm going to brag on behalf of you, David, because I do think DPI generally is an issue and challenge across the industry. I don't think it is our issue necessarily. Okay, I'm going to also cover a question here. Thank you for letting me do our commercial, David, on DPI. You know, it's my favorite topic. On the topic of when you think about some of the names that we have underwritten, you know, I think there is this broader question of, gosh, like there's a lot of names, for example, like, you know, the chat, OpenAIs of the world, and also Databricks that feel like, you know, they're doing more of these tender offers, They're doing more of these, you know, kind of SPBs.

51:26How do you counter that portfolio composition of names like that versus names like an Anduril or Flock, where it's probably very, very difficult to get any access? And how do you think about the configuration of the portfolio as a part of that? Yeah, I mean, look, I mean, the opening investment that we made, you know, we were able to write exactly the check that we wanted to write. in terms of portfolio construction. So to me, it doesn't seem... I'm sure some folks have had access because other managers are trying to do SPVs and stuff, but I think that's increasingly hard. It's also our decision on how much to weight these opportunities and build a portfolio.

52:13So I happen to think that the return profile of that recent one in Databricks is very attractive. But, you know, we don't want an entire portfolio of things that we think are like pretty safe 2Xs, you know, where I think the question is, can we make 5X on them? But like we feel pretty confident we can make 3 to 4X and 2X is pretty safe. Awesome. Speaking of public companies, we had a question from Paige here on, well, actually, it's two related questions. what publicly traded software companies do you think won't be disrupted by AI and have real moats and any obvious ones that you think it will be?

52:51And related to that, I think we had some questions earlier on around how much, if at all, any public companies you'd do in the fund? Yeah, probably very few public companies in the fund. I mean, we'd have to have a really, really strong thesis and relationship with the management team to want to do that. Um, again, the public universe is just way slower growing and there's really, really exciting stuff that we can do in the private universe. And so the bar is extraordinarily high, um, you know, for things in the, in the public markets. And we've done a couple of which have worked out, I would say.

53:22Um, but you know, the bar is very high and the opportunity cost is probably something that may be even higher growth, uh, in the, in the private markets. Um, yeah, the, what, what companies are going to get disrupted in the public markets? Like, I love this question. I don't know. I would offer this framework, which is maybe there's three interesting ingredients to consider when you think about the safety or durability of the public software companies. One would be UI UX. So to the extent that we get a complete reimagination of UI UX, that'll be really exciting. What is Salesforce.com? Salesforce.com is like a set of really uninteresting, you know, checklists and forms that people fill out for the most part on the front end.

54:12The promise of this stuff, you know, agents is like a way overused term. But like, like the new technology should be able to like do things for you as opposed to like keep your records for you. And so you could imagine a completely reimagined UI UX, which is proactive, which is like, hey, instead of you going and inputting things, I know what you're doing. I'm just going to tell you what to do. And so a total reimagination of the workflow would be one ingredient for a startup to win versus an incumbent. The second would be access to data. So an entirely new form of data that gets sucked in to take the actions on your behalf.

54:52Like Salesforce, I mentioned the form thing on the front end. The reason it's really, really sticky is the database on the back end. So to the extent that instead of using that sort of structured database on the back end, you take all of your unstructured data and dump it into a Databricks, for example, you know, and query it or access it through that, that would be another interesting sort of opportunity for a startup to attack. And then the third is a business model innovation. And I mentioned this already. It's super, super early days. But to the extent that startups can come up with a novel way of shifting the business model in a disruptive form against seat-based pricing at Salesforce.com, it would have a chance to win.

55:40So I think for startups to win, I think you need all three. And that's just in the head-to-head stuff. I think startups are already finding interesting windows of opportunity in and around these systems of record, which we've made a bunch of investments in. we haven't found the startup that's like got the killer idea for like dethroning salesforce.com yet. I hope we find it. So I know that's an unsatisfactory answer, but at least it's like a, that's the framework I would use to think about those, those three buckets. Awesome. Okay. I'm getting the hook because I know we only have a few minutes left of this webinar.

56:18So maybe in the last couple of minutes, we'll fast forward here to, to talk about the team. I don't know why it took us over an hour plus to get to this point. So maybe we'll go through that. And then, David, also how you work with the broader A16Z team, and that'll bring us all back home with how this all fits in in the broader franchise and firm as well. Yes. I love the team. They're very, very smart. They've come together in an awesome way. We have a really strong sort of team subculture. We are very, very, very intertwined with the early stage teams. If you think about where we get alpha in our business, there's access, which I've talked about already in terms of being already involved in the companies.

57:03And 80 % of the time when we make a new investment that's not an early stage investment, one of our early stage folks has some pre-existing relationship. So access is a big piece of it. Insights is the other piece. I think the way you get really outsized, upsized returns in our business is market and product insights. I think you have to do all the analysis around business model and financials because you can make big mistakes if you get that wrong. And we go super deep in each of those. But I think the real outlier opportunities come when you have a market or product insight that maybe the rest of the market hasn't figured out yet.

57:40And the fact that we're attached to the early stage team gives us the opportunity to have those and gives us a tremendous amount of leverage to have a relatively small amount of team members given how much ground we cover. I will also take the opportunity to brag on your team and my team because I think both of our teams got a 91 % on the employee engagement score. So not that we're competing across teams, but this is a good barometer for team subculture, no doubt, and something we always do around the firm year-round, which is very company-like, but hopefully gives you a sense of what we're measuring ourselves across as well in terms of team culture.

58:21But I want to spend a minute to illustrate the point that David alluded to around how we work in collaboration with the early stage and also think about the late-stage venture in terms of sitting across all the six different early-stage buckets across the firm. And so maybe, David, if you want to give just like the high level talk track on if you were to hypothesize what the composition of the portfolio would look like across these six different buckets, that'd be helpful. And then I saw a question around how much we would expect around crypto investments and particularly around token crypto investments.

58:54That would be helpful. Yeah, absolutely. The best part about where we are is we've got people at the early stage of figuring out where they think they should be spending their time. and typically, you know, our world is like 12 to 24 months downstream of that. So when you see the way we size our funds at the early stage, that's a reflection of what those early stage teams think is their opportunity set and, you know, ours sort of follows that. So I think the largest amount of opportunity will continue to be AI, infra AI apps. Next, American dynamism, you know, our early stage team is killing it in that area.

59:30You know, beyond just AI, There's obviously a pressing, you know, sort of market world need for American dynamism companies. So you had at the same time, you have market need. You had people that figured out how to do this inside of SpaceX and Palantir who have then left to start companies. So the talent is there to navigate, you know, a very complex go to market motion. And then you have advances in technology beyond Gen AI, like autonomy capabilities and vision advances that enable American dynamism. So we're very excited about that area. We're starting to see a little bit more interesting stuff in AI-enabled health things.

1:00:10So we've done a couple. You'll continue to see us active there, but it's probably a little bit less than the AI infra and app side. And then crypto, the way we're doing that is working hand-in-hand with Chris and the crypto team for their high-conviction bets where they're at a stage that they fit the growth fund. And I'll brag on them. I think they're the best crypto investors in the world, and we sort of are happy to attach ourselves to that. And it certainly depends on the opportunity set. We're seeing really exciting stuff in the enablement of stablecoins. and so to the extent that we see a massive takeoff in that market, it could be more.

1:00:52I always say it would be great if 100 % of our investment activity in the growth fund was follow-ons because that would mean that our early stage team is absolutely killing it and they're getting a lot of market share. But I think there will always be a place for us to do really attractive new things. And we don't build the portfolio based on some target around new versus follow-on, it's really like best ideas win, where do we have access, right to win, et cetera. Great. And then less than 60 seconds. Do you want to take Andy's question? So on the topic of portfolio construction, how much exposure do you want to top research teams with a wide fan of outcomes versus opportunities with a more narrow range?

1:01:38How many more researchers do you think are actually out there, beyond the ones that you've already backed that you will get more exposure to? There are extremely high-end researchers at some of the big labs that our early-stage team is tracking. And I should have mentioned this. When we do these, we do them with the AI infra fund. So we would not, you know, we rely on them heavily to make sure that we're doing the right assessment of the teams and, you know, the research ideas that they're pursuing. You know, in terms of portfolio construction, I quite like the way that we've done it, where we have some absolute champion companies that we think actually have a lot of room still to run.

1:02:23Where on the downside, we think if things really go poorly, we'd still probably make two times our money. And on the upside, there may not be the opportunity for 10x over five years, but we think there will be the opportunity for 5x over five years. The research team with the high variance is a little bit of an output of that great opportunity set. Like right now, there's not another Ilya floating around in the AI market. And so we would never try and say, oh, we want 10 % of our portfolio to be in these, so we need to find someone. It's more reactive when we do find those people who are really special.

1:02:56I think the only shift that we'll see over time is I think you're seeing a bunch of really, really, really exciting AI apps and American dynamism companies at the earlier stage that are poised to become kind of champion companies over the next five years. And so you'll probably see a lot of activity from us in those kinds of companies. Awesome. And with that, I'm going to close out here lastly. Thank you, David. Appreciate it. Great to hang as always. 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.

1:03:37For 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. 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 forward slash disclosures.

1:04:24Thank you.