a16z Podcast - The Hidden Economics Powering AI

Episode Date: January 26, 2026

In this episode, Jen Kha, Head of Investor Relations, and David George, General Partner, discuss how late-stage private markets are evolving as AI reshapes scale, capital intensity, and growth timelines. They explain why AI-driven companies are staying private longer, how infrastructure spending is changing return profiles, and what this moment means for durability, value creation, and long-term outcomes in private markets.Timecodes:0:00 — Introduction04:21 — The Market Opportunity for AI26:48 — Pricing, Monetization, and Cash Burn43:15 — Companies Staying Private Longer51:30 — Portfolio Composition and Construction57:18 — Team Culture and Collaboration Resources:Follow Jen Kha on X: https://x.com/jkhamehlFollow David George on X: https://x.com/DavidGeorge83 Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergNot an offer or solicitation. None of the information herein should be taken as investment advice; Some of the companies mentioned are portfolio companies of a16z. Please see https://a16z.com/disclosures/ for more information.  A list of investments made by a16z is available at https://a16z.com/portfolio. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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, that Taiwan has quietly broken. Software companies stayed private longer, market capitalization concentrated. Today, the most valuable companies in the world are U.S. technology firms built on infrastructure that barely existed the 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% all 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 the infrastructure underneath it all. This creates a paradox. The buildout is larger than anything we've seen before, yet 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 Jen Ka, head of investor relations, sits down with David George, general partner, to examine how lake stage markets are evolving, how AI is changing scale and timing, and what this moment means for returns, durability, and value creation in private. market. 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 U.S.-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 10 are U.S.-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 Databricks 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 is 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 10 or so years as we look to make new investments. So let's jump in to the detail. 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 capax from the latest quarter, I think it's like $400 billion of annual capax. 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 buildout. And so you've probably all seen the

charts of CAPEX spend as a percentage of their overall sales. It turns out they're the best companies probably ever created, companies like Google, Facebook, Amazon, and Microsoft, and they can bear potential capacity overbuild 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 big. bigger than this. So the build-out's 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 bodes really well for building new stuff and new capabilities on top of AI. I think our house view now is that AI is going to end up like electricity or Wi-Fi. If you're access to you know, 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, U.S. software spend is like 1% of GDP. U.S. white collar payroll is like 20% of 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 give are like, what is your iPhone cost? I don't know, the latest give or take a thousand bucks. If gun to your head, what would you pay for an iPhone? If you're on the higher end income spectrum, like probably far greater than $1,000. And the difference between that and the $1,000 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, end use. whether it's businesses or consumers, and a massive amount of new market cap goes to companies that are capturing that opportunity.

There was a great meme the other day that someone said. Imagine if Google had known that users were willing to pay, you know, 100, 200 bucks like they are with ChatGPT. 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 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 buildab and then the glut, we actually, of course, get the beneficiary of that today. But 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. And the biggest funder of private capital is actually banks or private debt. So, banks are the ones that are funding the private debt companies, and increasingly they all have insurance companies. So maybe there's insurance companies that are kind of backdoor funding this buildout. That's a really good sign for the stability of the buildout. 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 demand 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. So it's 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 buildout will be utilized, maybe in a more predictable way than, you know, broadband in the early internet buildout 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 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 U.S., 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 freemium 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 the way you see, like one way to see it. in the product is if you haven't done this already, go into 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, opening eye or grok or whatever it may be.

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, you know, 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 AI 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. probably $2 billion using it. So like, you know, and again, Facebook and Google monetize their properties at like, you know,

for U.S. users call it between $150 and $200 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 are ready 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. But 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 the infrastructure buildout. 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 have a really good view of demand signals that 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 convince 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 AI companies that we're investors in. Totally. Pulling up the website of ChatGAPT and doing it, being able to do it accessibly 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 is very different than we, in the prior cycle where we literally had to wait for the infrastructure to be built out and 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 spent time on a public side, you know, one of the things that has been significantly observable is the number of public

companies who have reported a decline 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 life, we're hearing a lot about from folks in the Fortune 500, just given, you know, obviously this reorienter business on how do they engage with the consumer when you could actually do a really detailed search,

as David described with his son's baseball bat, 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. I scoured the end of the Internet for you, buddy. and it took into all all dads in their desire to have endless amounts of research

on, you know, kind of arbitrary things to say the least. Here, I'll send that, there was a good little snippet on X about some of the Google search traffic stuff. It was like IAC and Target Roos. Yeah, I'll send it. Oh, you understand it.

Yeah, that running's called that Martin dropped the other day, just so folks can see, like, again, it's the folks usually what's expect, right? like folks like Groupon, for example, IAC, who are seeing the impact 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's 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, as of right now, through our current means of energy production, yeah, I mean, energy's 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 island's 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 build 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 sources, I think is the short answer. And, you know, we're probably most optimistic about nuclear. for sure. 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 XAI did is they stood up the biggest data center at 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, you know, multi-state region bought out and, you know, by 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 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, we always do, if then the bottleneck just shifts to another. The big component that I think most folks have not yet realized your zone in on 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. already how to actually cool all this stuff down without boiling our oceans and making the world meltdown. And making the chips meltdown. And making the chips meltdown. Yes, that's right. There's one question here, David, if I can interject, because you are the business model snob. 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 companies should be more scrutinized, i.e. particularly, you know, kind of there's a lot of turmoil around, for example, the relationship between cursor and anthropic

and whether 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 an output on the other side. Yeah, I love this topic. And I would just say that, like, the reason that this industry and 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. But I'm born born born yet, too. I know. There's some that weren't born yet. Yeah.

So some young folks who are very AI-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 your SaaS app. 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 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, a greater degree of variance 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 model? So one, sort of value proposition to the customers is the number one thing that we care about. Is there a customer love of your product? And is that love enduring? So, you know, 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 always get to look at net retention rates too. But for gross retention rates, it's sort of like, are 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 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 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. If you recall, you know, 100x decline in the input cost. over the course of two years, the hope would be that that continues.

And 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 in this area and sort of assessing the 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...

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 Anthropic. 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. 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 for 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 so much 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'd 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. For sure. Yeah, I flip back to this because I've seen this personally myself like a dozen plus times, 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 they 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 subscription

or a Netflix subscription, where as soon as, you know, obviously pressures come 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' question. Yeah, this is from Thomas. So it seems like there's downward pressure on the likes of Open AI on consumer pricing, yet the cashburn of OpenAI ramping up to levels not seen before previously, reports of a billion dollars plus per month. How does a cashburn moderate in the future relative to what you think in your opinion? Yeah. 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 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. 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've gotten so big 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 in the upside on the P on the price. 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 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. years is they've like 8xed their own monetization of their users. And so I suspect that if they, you know, 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, the actual, like most of the burn comes from research like research like,

R&D, you know, and so future investments. And, you know, 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 AI, 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 chat, GPT, like if there's some better slightly better model that comes out. 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. You know, there used to be like a thing which was, you know, enterprise companies are stickier than consumer companies. In this case, their developer,

like these are like developers, buying these things on the B-to-B 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. So 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, you know, 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, 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. And 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, Anthropic XAI. 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. Um, 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. Can you explain 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, and stuff like that. And, you know, sort of enterprise capability.

So, like, I think that. Customer, for example, the rules to go down the sequence. of how to troubleshoot are so embedded that you probably wouldn't experiment a ton once you've got a workflow 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, 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. So 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, we'll 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 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 the minute. I want to make sure we get through as many as possible. So, Mberto asked, given the speed of go-to-market for AI companies, do you think 100 million ARR is the right milestone to measure against? Or are you starting to move the goalpost on what success is there? Yeah, it's so funny.

Like, you know, it used to be, I can't remember one of the VCs coined the, you know, like triple, triple, double, double, or something like that. Like that looks like very modest. Yeah. It's something like that. Yeah. And that time compression has like massively gone down. So I got to look up the stat.

We ran the analysis on it recently, but it was like, you know, the top companies that we've seen, you know, 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. And so I don't have an absolute answer for you, but if you were to sit there and look at,

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 A Bridge and 11 labs, you know, and not like Shopify and DocuSign in, 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.

For sure. For AI companies, what are you? This is a good question from a deep gap. What do you think about seat-based pricing versus research-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, 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 with AI. And, like, 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. And 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.

Then 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 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. and customers want to buy stuff on seat and consumption-based pricing.

And you kind of got to meet the market where it is. So 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. 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. 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 can 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, 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 it was priced at some appropriate competitive level

with a return on capital while still 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, and what areas are they building. And our early stage team is doing a great job of, you know, doing the most exciting early stage deal. So that to me just bodes really well for the next 12 to 24 months of deal activity for us, you know, looking out.

All right. Growth partner. So this is a, 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, you know, within, 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, you know, You know, 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 private markets valued above a billion dollars, you know, we could argue are some of them overvalued, undervalued.

But that whole value in aggregate is like $3.5 trillion. And that's like 11%. Three and a half trillion is like 11, 12% of the NASDAQ, 10% of the NASDAQ, 10% 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 or 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 Snowfield, 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, 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. 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, 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, you know, 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, you know, there are 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 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. One is the companies where they're like flying off the page, you know, like undeniable momentum. So, you know, companies like Curser and Decagon and 11 and a bridge 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

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, 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 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. We talk about timelines to exits because on one dimension, you know,

know, folks 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 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? And 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 on DPI too. Like, you know, that's, that's how we, that's how we compensate ourselves and our teams. And so, you know, we care, 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, 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 championed 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. And, 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. 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 we've fortunately been very active in some of those situations. So things like tender offers in the private markets, a big thing that is hard for private companies is competing for talent. And part of the reason why it's so hard is because public companies grant RSUs that typically vest on a quarterly basis. And so they hit your account. They're already taxed 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, I just don't see that being the case. For 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, open AIs of the world

and also data bricks that feel like, you know, they're doing more of these tender offers, they're doing more of these, you know, kind of SPVs. How do you counter that portfolio composition of names like that versus names like an under roll 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 eye investment that we made, you know, we were able to write exactly the check that we wanted to write, 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 SBVs 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 recent one in Data Bricks is very attractive. But, you know, we don't want an entire portfolio of things that we think are like pretty safe two Xs, you know, where I think the question is, can we make 5X on them? But like, we feel pretty confident we can make three to four X.

and 2X is pretty safe. Awesome. Speaking of public companies, we had a question from page 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 remote 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 of 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. 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, 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 bar is very high.

And the opportunity cost is probably something that may be even higher growth in the private markets. Yeah, 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, you know, UI, UX. So if to the extent that we get a complete reimagination of UIUX, you know, that'll be really exciting.

like 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. The promise of this stuff, you know, agents is like a way overused term. But 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 UIUX, which is proactive, which is like, 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 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. Like Salesforce, you know, 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 data bricks, for example, 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 an sort of like novel way of shifting the business model in a disruptive form against like seat-based pricing salesforce.com,

you know, it would be, it would have a chance to win. So 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 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 of minutes,

we'll fast forward here 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 A16-Z team, and then 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.

They're very, very smart. They've come together in an awesome way. We have a really strong sort of team subculture. You know, we are very, very, very intertwined with the early stage teams. You know, if you think about like where we get alpha in our business, you know, there's access, which I've talked about already, you know, in terms of like, you know, being already involved in the companies. And 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 preexisting relationship. So, 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, upsized, you know, returns in our business is market. product insights. I mean, 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. 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, 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, you know, this is a good herometer for team subculture, no doubt.

And something we always do around the firm at your end, which is very company-like, but hopefully gives you a sense of what we're measuring, you know, ourselves across as well in terms of team culture. But 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 would 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, ours sort of follows that. So I think the largest amount of opportunities, will continue to be AI, infra AI apps. Next, American Dynamism, you know, our early stage team is killing it in that area. You know, beyond just AI, there's obviously a pressing, you know, sort of market and 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. 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. 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, 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, you know, 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. 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 takeoff in that market, it could be more. I 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, you know, 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 when, you know, where do we have access,

right to win, et cetera. Great. And then less than 60 seconds, do you want to take Annie'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? How many more researchers do you think are actually out there, you know, 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, you know, we have some, you know, absolute champion companies that 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 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. I 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 from us in those kinds of companies. Awesome. And with that, I'm going to close that here lastly. Thank you, David. Appreciate it. Great to hang as always. Thanks for listening to this episode of the A16D 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 at A6 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.

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