The Hidden Economics Powering AI

For the last decade, the largest companies in the world have been technology companies. Now, something strange is happening. The most important technology companies may never go public at all. For most of modern financial history, innovation followed a similar path. Companies were bored small, raised capital privately, and eventually crossed the threshold where public markets took over. That structure shaped how growth was financed, how risk was priced, and where value ultimately accrued. Over the last 15 years, the Taiwan has quietly broken. Software companies stayed private longer, market capitalization concentrated. Today, the most valuable companies in the world are US technology firms, built on infrastructure that barely existed a generation ago. Now, AI has accelerated that shift. In the last two years, the cost of accessing frontier models has fallen by more than 99 percent, while model capabilities have doubled roughly every seven months. At the same time, the largest technology companies are investing hundreds of billions of dollars to build infrastructure underneath it all. This creates a paradox. The buildout is larger than anything we've seen before. It demand is arriving faster than any previous technology cycle. The question is not whether AI is transformative. The question is whether markets, capital, and companies can absorb something this quickly without repeating the mistakes of the past. Today, A16Z's Genka, head of investor relations, sits down to David George, general partner, to examine how lakesage markets are evolving, how AI is changing scale and timing, and what this moment means for returns, durability, and value creation in private markets. It was like a very simple premise when we started. It was like tech markets are bigger than ever. Companies are staying private longer than ever. And as a result of that, the opportunity set for us is huge. I was looking at it last night, and I think, I mean, it kind of oscillates a little bit, but I think six of the most valuable, I think the six most valuable companies are US-based tech companies. It's definitely five, and then sometimes it bounces around on number six. And then it bounced around a little bit, but seven or eight of the top ten are US-based technology companies. So technology has kind of swallowed the whole market, and I think increasingly we'll take market cap over time. We've got some slides showing this whole trend, and I guess Data Brits was an appropriate way to kick off, talking about the trend of companies staying private longer than ever. That's obviously a double-edged sword for us. It gives us an opportunity to invest in companies more while they're in the private markets, but we also are very mindful about generating returns and DPI. And then the big thing that's changed from when we started the growth fund is just AI. We've got some slides on it. It's massively expanding the market. The AI companies are getting bigger faster than anything we've ever seen. The investment amounts are bigger than anything we've ever seen. And so it looks to be a huge tailwind for us over the next ten or so years as we look to make new investments. So let's jump into the details. So AI, I mentioned this already. The groundwork is being laid in a way that's very different than previous cycles. And the groundwork that's being laid is bigger than anything we've ever seen before. So I'm all over the team. I'm like, this is too conservative. These numbers are going to end up way bigger. Because I think just the big tech companies in their latest quarter, if you run rate their capex from the latest quarter, I think it's like $400 billion of annual capex. And most of that is going into AI infrastructure and data centers. And so what that means is the infrastructure is going to get built for all of the training and inference needs that the market is going to need. And this is great for all the companies that are building on top of this. The best part about this is it's mostly the large tech companies that are bearing the burden of the build out. And so you've probably all seen the charts of capex spend as a percentage of their overall sales. It turns out there are the best companies probably ever created. Companies like Google, Facebook, Amazon, and Microsoft. And they can bear potential capacity over build and things like that. And so if you just put it in a conservative view, which again, I think the number is going to end up way bigger than this. So the build out is massive. And this boat's very, very well for our portfolio companies that are building on top of it. At the same time, this is happening. The input cost and input quality is getting remarkably better, like faster than Moore's Law. So on the left hand side, you don't need to look at the details of this. Just trust me when I tell you, the cost of the inputs of accessing these models has declined 99% or a little more than 99% over the last two years. So sort of 100x declines greater than Moore's Law decreased. At the same time, the models have been improving in sort of frontier capabilities by a double factor every seven months. So massive decline in the input cost at the same time that the quality is going way up. And this boat's really well for building new stuff and new capabilities on top of AI. I think our house for you now is that AI is going to end up like electricity or Wi-Fi. If you're accessing electricity at somebody's house, you're not like, hey, let me chip in a few pennies for sitting in a room with light in your house. And I think it'll end up being the same thing in the fullness of time with AI. The market opportunity for AI is so much greater than the software market. And I think that's really exciting. If you look at the previous cycle that we went through of mobile phones plus cloud computing, the big story behind that was basically creating 10 trillion or so of new market value across software companies, internet companies, mega-cap tech companies. And I think AI is going to be much larger because I think the impact on the economy is going to be much larger. And so if you look at the simple math that we have on the page, the US software spend is like 1% at GDP. US white collar payroll is like 20% at GDP. And so there's a lot of areas where I think we'll see augmentation or potential cost savings or efficiencies or replacements using technology. There's always a question when these things happen of how much the new companies are able to capture versus the end customers. My rule of thumb is like 90% of the value goes to the end customers and 10% of the value goes to the companies serving them. And it turns out that's just a massive amount of market cap if you're the 10% that you're capturing. The examples I always gave are like, what is your iPhone cost? I don't know, the latest. Give or take a thousand bucks. If you're going to your head, what would you pay for an iPhone? If you're on the higher end income spectrum, like probably far greater than a thousand dollars. And the difference between that and a thousand dollars you pay is the surplus. And it turns out Apple's still a really great business. Or if you take the Google properties like search and Gmail and now I guess increasingly AI stuff, they monetize you per year. First of all, you get it for free, which is massive surplus. But they're only monetizing you per year, probably like 200 bucks or something like that. And there's a tremendous amount more value delivered. I would argue than that per user. So I think the big story is going to be massive new surplus created. A ton of it gets captured by end customers and users, whether it's businesses or consumers. And a massive amount of new market cap goes to companies that are capturing that opportunity. There's a great name the other day. That's somewhat said. Imagine if Google had known that users were willing to pay, you know, 100, 200 bucks. Like they are with chat GPT if they had known that people would pay for that with a similarly magically delightful product like Google. Like God only knows what the market shop what of Google would be today. Obviously, maybe even more of a dominant market share contributor than it already is. But it is amazing when you think about sort of the reconfiguration of how we will view monetization in this new world. And before we go even further, David, I think these last couple of slides actually set up probably 95% of a lot of the questions around the market. Because if you go back to slide 7 Monique. I think that slide is daunting for a lot of folks in part because obviously you see it bundled with the headlines. But also because it just feels like we've all many of us had lived through the early 2000s where it just ended in a very less than desirable picture. So maybe if we can summarize what's your case for why it's different. And then particularly talk about the timing cycles because incidentally enough despite the massive broadband build up and then the glut. We actually, of course, get the benefits share of that today. Well, we grew into it. I mean, we ended up growing into it. It was just a time lag in that case. And it was not the strongest companies in the world building that out. And the thing to watch will be like, what role does leverage play? And so I read an article this weekend that was like, is there systemic risk in data center build out? And first of all, most of the people with their neck on the line again, but there's a role that as of right now private capital is playing. There's a role in the biggest funder of private capital is actually banks or private debt. So banks are the ones that are funding the private tech companies and increasingly they all have insurance companies. So maybe there's insurance companies that are kind of backdoor funding this build out. That's a really good sign for the stability of the build out. So that's like the nature of the supply side, which again, it feels different this time given who's actually doing the build out and who the tenants are. The man side is the bigger, more interesting thing. So I read a stat yesterday that the time to get to 365 billion searches on chat GPT was two years, the time for Google to get to 365 billion searches was 11 years. Because five and a half times longer. So the big story on the demand side for me this time around is AI is built on the back of the internet and cloud computing. And because of that, it sort of allows for immediate global distribution. If you look at the way Google and Facebook started, for example, like they started much more like small build, both had network effect dynamic, which just takes longer. And we didn't have full sort of internet proliferation across five and a half billion people in the world and smartphones in hands of everybody able to access the internet. And so what that means is because of the nature of this technology, you don't have to deliver a new hardware product. And because we have global internet build out and because we have cloud computing, the whole world can access this. And so if you just take chat GPT, again, they got to the scale that they're at five and a half times faster than Google, which is staggering. But you know, there's probably, I don't know, a billion, I think the latest they said, there's more than a billion monthly active users. There's probably another billion or so people who have tried it. So if you add up all the different platforms and a bunch of people have probably tried Google products and Facebook products, it's probably well over half of the global internet population has used AI tools already. And we know that there's probably in some shape or form somewhere between one and a half and two billion active users of these products. So just the speed at which they got to distribution is unlike anything we've seen before. And so that is heartening to me that the supply build out will be utilized, maybe in a more predictable way than, you know, broadband in the early internet build out days. Just because it, you know, it's built on the back of the previous infrastructure stuff. You know, I also want to comment on the Google thing you said, which is like, imagine if they, you know, could get 200 bucks or something from users. The beauty of as consumers, the beauty of like Google Facebook Apple for us, is there's not like a clean way to price discriminate for those companies. Like, like if, if, you know, like they don't know that I'm willing to pay more and they can't charge me more than, you know, the other user of an iPhone. But in the case of AI, because of the way the business model is structured and we've already seen some proof points of this, I think there will be greater success. So just today, or I guess yesterday, technically, open AI released their India subscription product. And I think it's something like three or four bucks a month. That makes total sense. In the US, there are high end subscription products that are 200 to 300 bucks a month that are like flying off the shelves, like consumers can't buy enough of. And so to me, the real story of the growth in this market is there's going to be an evolution of the business model that allows these companies to address the user base and actually price discriminate. I think in an effective way. So they can do a combination of subscriptions, you know, for higher end users and sort of get to the point, you know, where they're willing to pay more. And then also, you know, probably end up with premium products where they monetize through some form of advertising. It's hard to speculate now on what that would look like. I think it's probably some form of like an affiliate type thing that has like a dirty connotation because that's kind of a backwater industry in today's internet. But I think it will end up looking like that. And I think I think the way you see like one way to see it in the product is if you haven't done this already, go into like one of the deep research products. And sorry, this is Catherine Boyle calling me about a deal, I think. And I don't know how to turn this call onto silent. So one way to try this out in the product is like go into one of the deep research products, either, you know, whatever, open eye or rock or or whatever, maybe. And have it do like a really sophisticated shopping research project for you. And it comes out with incredible stuff. Like I had it. I had to do this for my son's literally baseball bat because it required a bunch of different specifications and things. And I wanted to look at year over year like, you know, what's the what's the better value and all these things. And it came back with like extraordinary answers. And this is a far superior experience than research like typing into Google and then clicking around and, you know, having seven sponsored links above, you know, seeing anything organic. So I think there's going to be an opportunity for them to monetize for users. And so the big story, you know, in the case of open eye is like, there's, you know, whatever, probably 30 to 40 million paying users today. The other platforms are kind of a rounding error relative to that. So maybe add another 10. So there's like 40 million people paying for this stuff today at some level. And, you know, there's probably two billion using it. So like, you know, and again, Facebook, Facebook and Google monetize their properties that like, you know, for us users call it between 150 and 200 bucks a year per user. So there's just a massive amount of opportunity to monetize. And on the consumer side, there's probably going to continue to be tremendous surplus. I find tremendous surplus in it today. But, you know, active daily active users of chat GPT already today spend like 30 minutes at 20, 28, 29 minutes a day on the product. And, you know, to put that into context, I think Instagram's like 50 minutes a day. And, you know, TikTok like sadly is like 70 minutes a day. So like this is like real time spent and real sort of consumer value already. So I just think I know the question was about like risk on the supply side and infrastructure build out. But the usage, the actual usage and distribution that we've seen this time around makes me think that it's kind of different. Like we, we have a really good view of demand signals that is far, that it took many, many years to get, you know, in the case of, you know, the internet. Or even in the case of mobile phones just because, you know, you had to manufacture phones and convinced people to buy them. So, you know, in that way, it's built on the back of the previous technology cycles. And it, and that's great. But it also in my mind, de-risks the sort of forward growth potential of the, of the AI companies that were investors in. So like pulling up the website of chat you can and doing it being able to do it excessively and almost like the party trick of like, hey, let me show you the cool thing that I just did like that user ability. Obviously, it's very different than we, in the prior cycle where we literally had to wait for the infrastructure to be built that in the device and hardware to catch up as well. Our early stage team also did a great post on this topic if you're curious about the future of commerce, what that looks like with AI. And even the recent, if you have any folks on this call who spend time at a public site. You know, one of the things that has been significantly observable is the number of public companies who've reported at the Klein in referral traffic and engagement. Largely because, you know, with Google search now, they're just doing the summary, the AI summary version of the result below, and we could talk about the implications for what that means in terms of the downstream effects as a part of that. But that is certainly something top of my work during a lot about. From folks in the Fortune 500 just given you know, obviously this real answer business on how do they engage with the consumer when you could actually do a really detailed search is David described with his son's baseball that without ever actually having to go on any website at all. I told my son, I'm like, you have no idea how much research I did man. Hi, I scout the end of the internet for you, buddy. And they're desire to have endless amounts of research on, you know, kind of arbitrary things to say the least. Yeah, I'll send that there was a good little snippet on X about some of that Google search traffic stuff is like I see. Target rules. Yeah, but yeah, but that earnings call that that Martin drop the other day, just so folks can see like again, it's the folks are usually what's expect, right? Like folks like Groupon, for example, I see who are seeing the impact and in real time. There's one question from Chris, we should take while we're just on this slide here. And I'm going to throw in another one that's related to it around the shifts in bottlenecks, right? You know, right now there's obviously this massive bottleneck as it relates to compute. But Chris, this question was, is there enough energy to actually power this build out as well? And, you know, we should lay into there what we think the next bottleneck actually beyond energy as well. I mean, yeah, like, as of right now, through our current means of energy production. Yeah, I mean, energy is a bottleneck. And so, you know, we've made investments on the nuclear side. I'm quite optimistic that, you know, we now have, you know, like an embrace, I would say of, you know, nuclear power. I think three mile islands going to get powered back up the big tech companies are building data centers near, you know, nuclear power plants. You know, we have figured out there's a lot of natural gas in, you know, places like West Texas that, you know, can be, you can, you can build training, large training clusters, like very near to them. And pretty efficiently power those data centers. But yeah, we're going to find, we're going to need, you know, different, different sources, I think is the short answer. And, you know, we're probably most optimistic about nuclear. And then the ability to just build these things, I mean, they're like massive scale operations. And so, you know, like one of the most remarkable things, and we're large investors in XAI. And one of the most remarkable things about what XA did is they stood up the biggest data center of the time in, you know, like a quarter of the time that anyone else had done the same thing. And they had to do crazy unnatural things like, you know, get, you know, every backup generator in the multi-state region bought out and, you know, buy labor off of different projects, but they did it. And so, just, you know, the actual construction and build, you know, is massive. My view on like chips and infrastructure is production capacity of those will typically scale to meet demand. There's, there's always a dislocation and, you know, you've seen it. And so, you know, I think energy ultimately in the next call it five years will probably be the bottleneck. And that's why we're so excited about nuclear and making investments in that area. Absolutely. Yeah. And just to extrapolate out, you know, once we figure out that piece, which inevitably, you know, with any technology, if we always do, if then the bottleneck shifts to another big component that I think most folks have not yet. Realize your zone in on is is the cooling piece. And so you'll see a whole wave of innovation around that part as well. Once you figure out how to generate all this energy, how to actually cool all the stuff down without boiling our oceans and making the world meltdown. And making the chips meltdown and make the chips meltdown. Yes, that's right. There's one question here, David, if I can interject because you are the business model. So this is a perfect question for you, which is there's a lot of debate on whether the gross margins of a lot of AI company should be more scrutinized. IE particularly, you know, kind of there's a lot of of turmoil around, for example, the relationship between cursor and anthropic and whether, you know, the growth for a lot of companies might be actually masked by a reliance on some of these models. And also what is the actual unit economics that is considered best in class, maybe help us distill how you and the team think about that and particularly, you know, what is this topic around gross margins where you're willing to make, you know, some short term exceptions for versus long term, you know, hopefully benefit and output on the other side. Yeah, I love this topic and I would just say that like the reason that this industry in this job is so fun right now is because the range of outcomes is so much greater than before, like the variance is so high, you know, I was talking to the team at the offsite yesterday. And I was like, you remember in 2000, some of them weren't in the industry, but you know, the ones that were in the industry, like, I learned born yet too, I know there's something more born yet. Yeah, so some young folks who are very and native and very smart, but you know, like late cycle, there's questions that you are trying to answer that are interesting, but they're far less interesting. So you're like, oh, just how big can data dog get or just how big can, you know, whatever pick, pick your pick your sass at like how big can it get. And now we have all these questions around business quality, market power, who the winners are going to be, even if you are a winner, is there going to be value that accrues to you and where you are in the stack. And so just the range of outcomes is is so much higher. And I think if we do a good job in that period of time, what that means is, you know, hopefully we can get, you know, greater degree of variance in in our own outcome, you know, as investors. And so I'm very excited about that. So yeah, so what are what are we thinking about in business models? So one sort of value proposition to the customers is the number one thing that we care about. Like is there a customer love of your product? And is that love enduring? So, you know, if you if you made me pick two top line stats to look at to assess the business model, it would be gross retention rate. So gross because, you know, I like looking at we have we always get to look at net retention rates too, but for gross retention rates, it's sort of like our people getting value out of your product. And what gross retention is is basically, if you have 100 customers, you know, in absolute terms, you know, dollar weighted, how many of them are sticking around. And so we look for things where like 90% plus customers are sticking around. And hopefully they're expanding their usage. And so that would be expressed in net retention, but just sort of core value proposition is is shown in gross retention. And then ease of customer acquisition. And so the way you see that is like organic customer demand, you know, high value of dollars that they're willing to pay relative to how much it costs to acquire them either via marketing or sales. And so if you have things that are sort of like being pulled off the shelves and you have a high endurance of the customer relationship, that's typically like the best things that you can get for, you know, business model quality on the top line. You mentioned gross margins. So we care a lot about gross margins. And there's a bunch of debate right now around some of the AI native application companies and their gross margins. I think our hypothesis and hope in the market is if there are multiple model providers that are somewhat close to parity, you're going to see input costs go down significantly over time. But if you recall, you know, 100 extra client in the input cost over the course of two years, the hope would be that that continues and all all indications suggest on our side that that will continue and maybe it abates a little bit, but it will continue pretty significantly down over time as long as there's competition at the model layer. And so, you know, coding is one of the areas that people have spent a lot of time scrutinizing this area and sort of assessing the business, you know, business models and business quality. I'd say relative to like mature SaaS apps, we probably are a little bit more lenient on assessing a company's gross margin today because we strongly believe that their input costs are going to go down over time. And because of the model improvements, they'll be able to harness better models and deliver better products to consumers over time. So they won't need to increase price, but they'll deliver a lot more value and stickiness while their input costs go down. That's the hypothesis. I think that's subject to there being multiple players in the market that serve, you know, models now we're thrilled that GPT 5 is out and it's a very credible alternative and it will put pricing pressure on entropic. Google 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. 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 sort of mature industry type SaaS stuff or infrastructure stuff, we're a little bit more lenient on assessing a company's gross margins today. We don't want to go invest in much 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 and you know give them a little bit more benefit of the doubt that they can improve on the first. Yeah, I've looked back to this because I I've seen this personally myself like a dozen plus times, but but something you said they're really just clicked for me, which is to say like, you know, a lot of people try to make comparisons to the dot com error and they're like, oh, remember we measured eyeballs as well, right? Like isn't that the 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. But 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 Netflix subscription whereas 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. It has made either professionally for you personally and accelerated your productivity and hopefully time in terms of acceleration as a part of that. That's just question i'm happy to take time is this question is 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 a eyes ramping up to levels not seen before previously, you know reports of a billion dollars plus per month. How does it cash burn moderate in the future relative to what you think in your opinion. Yeah so so yeah 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 and it hasn't had any impact on their business. Now that could change over time but so far what we've seen is 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 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 you be I would say it probably applies to you know most the consumer facing stuff in the industry. If 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're 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 2's billion. So there's you can only get so large but I think there's a tremendous amount of room to run actually the upside on the P on the price so so again if you think about you know how are they monetizing today you 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. You know I think about like lessons learned from previous internet era companies and I remember looking at internet companies 10 years ago and we would we would always look at like Facebook and Google and we're like okay Facebook and Google they're monetizing their users at X like that's the max we could get to. And the big story about what's happened over the last 10 years is they've like 8x their own monetization of their users and so I suspect that if they you know have some thoughtful ways of monetizing free usage while while still maintaining trust there's probably more upside and downside on on pricing to Thomas this question and then on the burn. The actual like most of the burn comes from recent like research like R&D you know so future investments and you know I we could apply this to the whole industry of all the model companies. But we could you know we could talk if it's specifically open and I'm happy to address that one but on the opening I side I think they're an advantage position because they have the consumer base and that stickier like my family like my parents and Kentucky use chat you be like 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 better a better position to be in to fund those research efforts for continued model development. There used to be like a thing which was you know enterprise companies are stickier than consumer companies in this case their developer come like these are like developers buying these things on the B2B side for the most part today like buying kind of you know 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 so to the point about coding models you know if there's a new coding model that comes along that's better than the latest version from an topic like our coding companies will just switch and it's pretty easy to do because it's an API call. And so so you know I think interestingly enough it's a little bit different this time where the consumers will stick here I think it 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 we're like we're going to there's going to be no economy and like everything's going to have a GI and it's like and then you know what like has happened like competitive forces have kicked in and they become like. Our 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 him so can DG also come under ability of revenue for many of the AI applications built on elements I. Outside of open AI and for Epic X. Yeah it depends on the nature of the use so I think some of them are really sticky so you know companies you're like medical scribe stuff I think is pretty sticky because there's a bunch of doctor workflow built around it. You know customer support I think is pretty sticky some of like the high end financial analysis type stuff I think is pretty sticky. Why those specific areas you think are more stickier than others yeah I think the more stuff that gets integrated and the more company specific 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 stuff like that and you know sort of enterprise capabilities so like I think that 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 cross multiple different scenarios that. Yeah yeah you know even like a style with which something engages like these like a lot of companies that are in customers these things are like brands and they care about the way that you know like the. The customer support agent interacts and so so that's stuff's probably pretty sticky I think there's a bunch of stuff that's not sticky at all so some of the emerging behavior that's like. You know I don't want to talk negative 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 and 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 their sales force calm 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 sales force calm. So I'm going to speedrun through some of these questions because I recognize we've opened the floodgates on the minute I want to make sure we get through as many as possible. So I'm burped to ask given the speed of go to market for AI companies you think a hundred million AR is the right milestone to measure against or are you starting to move the goal post on what successes there. Yeah it's so funny like you know the it used to be I can't remember one of the one of the VCs coin the term you know like triple triple double double or something like that and it's like that looks like very modest. And something like that yeah and that that that time compression has like massively gone down. So I got to look up the stat we we ran the analysis on it recently but it was like you know the top companies that we've seen. You know I've gotten to 10 million and a hundred 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. And 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 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 a bridge and eleven labs. You know and not like Shopify and doc you sign 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 great is at any given time. For sure for a copies what he did is a good question from a deep down what do you think about see based pricing versus usage base 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 like I feel like on X and like 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 the products getting better. And then to we've really only seen a true business model innovation like early early days in one area and that's customer support it 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 sass 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 then we got to usage based and so you know obviously all the cloud companies run that way. You know database runs that way snowflake et cetera and then the hope would be that I've seen you know and 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 like and customers want to buy stuff on seat and consumption based pricing. And you know you kind of got to meet the market where it is so for now we're not seeing hugely disruptive things I think is the capabilities get much better and sort of measure ability 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 there's 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 but 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 in 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. Competitive forces kicked in and you know it was priced at some like appropriate 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. And 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 I try and sit in on as many pitches in our stage practice as I can just because I feel like I learn a lot about you know what's starting what what are interesting founders starting to hone in on and you know in what areas are they building. And 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 alright growth partner so this is a this is a crazy looking charm 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 but you can see it in the chart here used to be the companies would go public you know within call it like five to 10 year window. Of of inception and despite the fact that the companies are growing much faster and getting better quicker they're saying private way longer so. You know all all is that all that is to say you know it's like 14 years and I think that's getting getting even longer if you take the market cap of you know the. The private markets value above a billion dollars you know we could argue are some of them over valued undervalued but that whole value in aggregate is like three to half trillion dollars and that's like 11% 3 1/2 trillion 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 1/2 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 it's pretty it's pretty stark so the other thing that's going on I mentioned this earlier with snowflake is like the public markets are no longer the place of extremely high growth. Like 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 in the private markets now you know for the most part and 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 it's it's pretty longstanding and I don't see it reversing anytime soon. So we have a bunch of really good AI companies so people faster like oh isn't there like crowding that's happened in some of the best you know companies and what I would say is like. One we're getting in a lot of these companies much earlier than other than others are able to into even for the ones that are later where you know they're bigger rounds like it's really important that we have that early relationship because it gives us ball control and access into the rounds. And X AI 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 around 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. One is the companies where they're like flying off the page you know like undeniable momentum so you know companies like cursor and decagon and 11 and bridge and and you know others the second bucket is 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. 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 there's sort of different shape on their 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 you know demand for talent like you know we would be down type protected even if it doesn't work out and so high business you know sort of variance but you know sort of asymmetric capital returns profile that looks a little bit different than a typical early stage investment. We talk about timelines to exits because on one dimension you know folks may may take the reaction of gosh you know private for longer means just extension of hold periods for LSD companies maybe the reframe on it is why is that great for for us but quickly as private investors and why do we think it's actually advantageous in some respects for some companies to actually delay time to IPO and how are you counseling folks as a part of that. Yeah I mean this is a trick it's a tricky issue instead of right up front which is like. We get paid on on DPI to like you know that's that's that's how we that's how we compensate ourselves in our teams so you know we care we care a lot about it I would say we're in a fortunate position. Where are portfolios is pretty good and we've had a number of companies that have acts have acted to the public markets are 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 getting a lot of coverage i think each kind of has idiosyncratic reasons why they wanted to stay private and you know i think i'm confident that they will go public so. So I think most of them will probably end up going public it just will take a longer amount of time you know 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 you know we've fortunately been very active in in some of the situation 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 rs use that typically vest on a quarterly basis and so they hit your account they're already tax with held 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 the bigger better ones have done more regular tenders and we've been very active in shaping those. With the companies 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 will help enable that. But we do recognize that obviously you know our goal is to to monetize great investments we're not there yet where we feel like there's a need to do unnatural things i just don't see that being the case. For sure and I do want to call it as well i'm going to brag on behalf of you David because I do think dpi generally is 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 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 of names for example like. You know the chat open a eyes of the world and also data bricks that feel like you know they're doing more of these tender offers are doing more of these you know kind of s pb's how do you counter that portfolio composition of names like that versus names like an and roll or flock where it's probably very very difficult to get any. Access and how do you think about the configuration of a portfolio as a part of that. Yeah I mean look. I mean the opening investment that we made. You know we. We were able to write exactly the check that we wanted to write um you know in terms of sort of portfolio construction so. To me it doesn't seem you know i'm sure some folks have had access because other managers are trying to do spv's and stuff but i think that's increasingly hard. You know it's also sort of our decision on how much to wait these opportunities and build a portfolio so you know i happen to think that the return profile of that reason one and data bricks is very attractive. But you know we don't want an entire portfolio of things that we think are like pretty safe to access you know where i think the question is can we make five X on them but like we feel pretty confident we can make three to four X and two X is pretty safe awesome speaking of public companies we had a question from. From page here on um actually it's it's two related questions um what publicly traded software companies do you think won't be disrupted by a AI and have real notes and any obvious ones that you think it will be and related to that i think we had some questions earlier on around how much if at all any public companies you do in the fund. There are 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 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 bars are extraordinarily high you know for things in the public markets and we've done a couple which have worked out i would say but you know the bars very high and the opportunity cost is probably something that maybe even higher growth. 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 all i would offer this framework which is which is maybe there's three interesting ingredients to consider when you think about the safety or durability of the of the public software companies. One would be you know UI UX so if to the extent that we get a complete reimagination of UI UX um you know that'll be really exciting like what is Salesforce.com Salesforce.com is like a set of really uninteresting um you know checklists and forms that people fill out for the most part on the front end. The promise of this stuff you know agents is like a way of reuse 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. Um and so a total reimagination of the workflow um would be one ingredient for a startup to win versus incumbent the second would be access to data um so you know an entirely new form of data that gets sucked in to take the actions on your behalf. Like Salesforce you know I mentioned the form thing in 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 um you know and and query it or access it through that um that would be another interesting sort of opportunity for a startup to attack. Um 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 an you know sort of like novel way of shifting the business model and a disruptive form. Uh against like seat based pricing sales force dot com uh you know it would be uh it would have a chance to win so I think for startups to win I think you need all three. Um 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 um 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 sales force dot com yet I hope we find it so I know that's an unsatisfactory answer but at least it's like a for that's the framework I would use to 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 so maybe in the last couple minutes we'll fast forward here to talk about the team I don't know why it took us over in 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 a 16 Z team and I'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 there uh they're very very smart uh they've come together in an awesome way we have a really strong sort of team sub culture um you know we are very very very intertwined with the early stage teams um you know if you think about like where we get alpha in our business um you know there's access which I've talked about already um you know in terms of like you know being already involved in the companies and in 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 um you know access is a big piece of it insights is the other piece like I think the way you get really you know like outsized up size you know returns in our business is market and product insights. I mean I think you 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. Um 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 and you know 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 uh small amount of team members given how much how much ground we cover. I will also take the opportunity to brag on your team and my team because I think both our teams got a 91% on the employee engagement score so not that we're competing across teams but you know this is a good parameter for team subculture and no doubt and something we always do around the firm at your range which is very company life but hopefully give you a sense of what we're measuring you know ourselves across as well in terms of team culture. But I want to spend as 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 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 be helpful and then I saw a question around how much we would expect around crypto investments and particularly around token crypto investments that 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 are sort of follows that so I think the largest amount of opportunity will continue to be. AI in for AI apps next American dynamism you know early stage team is killing in that area you know beyond just AI there's obviously oppressing 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. So you have this side of space X and 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 J and I 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 so we've done a couple. So we'll continue to see us active there but it's probably a little bit less than the AI in front of an app side and then crypto the way we're doing that is working. You know hand in hand with Chris and the crypto team for their high conviction bets where they're at a stage that they fit you know the growth fund and and you know I beg on them I think they're the best crypto investors in the world and we sort of are happy to attach ourselves to that. So certainly depends on the opportunity set like we're seeing really exciting stuff in the enablement of stable coins and so you know to the extent that we see like a massive take off in the market it could be more I always say it would be great of 100% of our investment activity in the growth fund was follow on because that would mean that our early stage team is absolutely killing it and they're getting a lot of market share. You know I think there'll 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 verse follow on it's it's really like best idea is when you know where we have access right to win et cetera. And then less than 60 seconds you want to take any questions 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 how many more researchers do you think are actually out there. You know beyond the ones that you've already backed that that you will get more exposure to and there are extremely high end researchers at some of the big labs that we we early stage team is tracking and I should have mentioned this when we do these we do them with the AI and for fund so we would not you know we 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 you know we have some you know absolute champion companies that we we think actually have like a lot of room still to run. Where on the downside we think we would if things really go poorly like we'd still probably make two times our money and on the upside you know there may not be the opportunity for 10 X over five years we think there will be the opportunity for five X 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 area 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 I think the only shift that will 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 from us in those kinds of companies. Awesome and with that I'm going to close that year lastly thank you David appreciate it great to hang as always. Thanks for listening to this episode of the a 16 Z podcast if you like 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 podcast and Spotify follow us on X a 16 Z and subscribe to our sub stack at a 16 Z dot sub stack dot call 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 a 16 Z fund. Please note that a 16 Z 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 a 16 Z dot com forward slash disclosures.