AI Markets: Deep Dive with a16z's David George

[00:03] because this is the first time we've ever done this style piece. We produce so much work and so much analysis. It's like exhaust uh in, you know, inside of our team and we thought, you know, we have so many different thoughts and and

[00:19] paper and share them out with the world? So that was the genesis of this. My big takeaways from doing this one, you know, AI demand side is crazy. The actual

[00:32] uptake growth quality of companies in AI is extremely encouraging from our run themselves better. I'm going to show you some stats on that that, you know, there's been some sort of X buzz uh including this morning. You know, kind

[00:45] of debating what's going on there. But this crop of companies I would say is more impressive uh than than prior crops of companies partially because the demand for their products is so high. Um that's demand side. Supply side is

[00:58] healthy right now. Uh but we are starting to see some signs of things, bit. I'll I'll talk about what we see and what we're looking out for. We've been fortunate to be a part of a lot of these great companies. Um, and the most

[01:12] exciting action that is happening in the private markets, it's it's it's it's AI markets. Um, and we're going to show some slides about that. And then lastly, my big conclusion, what has me so excited about where we are now is just

[01:25] how early we are in this product cycle. Um, you know, product cycles drive our business. And, you know, these are 10, 15 year cycles and we're just at the very beginning of it right now. So, let's dive in. We invest across all

[01:39] just shows our activity. We're very busy. It's across all verticals. We on the growth side have been most active in AI and infer apps uh and then in in AD, but also very active in our other verticals um as well.

[01:55] And I'm going to zoom through some of these. I hate to do the A16Z commercial, I think we have the chance to work with infra companies. Uh obviously, >> I'm I'm going to do that on that side.

[02:09] [laughter] >> I do like that slide a lot. >> Soundboard effect here. I'm happy. >> We debated. Yeah, we debated how early to put that slide on the deck and and uh I said put it further back and I was

[02:21] I said put it further back and I was overruled. So thankfully um anyway, here's some data. So we collect tons and tons of data as a growth team because we're basically seeing every growth stage company in the market uh as a

[02:34] either portfolio company or as a prospect. And so we have a great data analysis team. We did some data analysis. I think this stuff is just super interesting. We geek out on it. To me, the big conclusion from this is 2025

[02:47] was a year for accelerated revenue growth. Um, you know, revenue obviously slowed, you know, in 2022, 23, 24 following the rate hikes and and the pullback in some of the tech stuff. But 2025 reversed that trend. Um and you

[03:00] know it accelerated across uh different you know types of companies as we rank them by decile and cortile. Um but especially among you know the outlier companies you know it really accelerated and you've probably seen us put this

[03:13] slide on a page before but the fastest growing AI companies are reaching 100 million bucks of revenue significantly faster than the fastest growing SAS companies in their era. And there's a really important thing I want to call

[03:27] out about why that is the case and that is because end customer demand is so strong and the products are so compelling. It's not because they spend more money on sales and marketing. It's actually the opposite. The the best AI

[03:42] are not the ones spending the most amount of money on sales and marketing and marketing than their SAS counterparts. And yet they're growing much much faster. So this was a slide showing just the growth of the AI

[03:56] companies versus the nonAI companies. Roughly speaking, the AI companies are growing two and a half times plus faster than the non-AI companies. And that shouldn't be a huge surprise. The best of the AI companies are growing very

[04:10] very fast. We had to triple check this data when we saw the, you know, the the AI top, you know, top performers growing 693% year-over-year. um but it matches up our experience you know and and anecdotes that we see from the portfolio

[04:23] companies. So that's growth. This is the margin profile uh that we're seeing in internal data sets that we have of portfolio companies and companies that we look at uh as potential investments. Gross margins are a little bit worse for

[04:38] AI companies. Um, you've probably heard us talk about this before, but in a way we feel like low gross margins for AI companies are sort of a badge of honor in the sense that we want to see if if if if low gross margins are a result of

[04:54] high inference costs, one that means people are using AI features and two, we have a belief that those inference costs over time are going to come down. Uh so in an odd way, if we see an AI pitch and the gross margins are super high, we're

[05:07] a little bit skeptical because that may mean that the AI features are not actually what is being bought uh or used by the customers. We're going to talk about AR per FTE, but this is a new thing that we've started focusing on and

[05:21] this is one of the things that got a lot of pickup and discussion uh on X in the of pickup and discussion uh on X in the last few days. ARR per FTE is sort of a measure of the efficiency of how you run your company in general. So it

[05:33] encapsulates all of your costs. Uh it encapsulates, you know, not just your efficiency measure that we've always kind of looked at when we do analysis in the past, but it also captures your overhead. It captures your R&D. Uh and

[05:46] so for the best AI companies, they're running at like 500,000 to a million dollars uh per per FTE. And the rule of thumb for previous software businesses in the SAS era was like $400,000 in the last generation. Again, I'm going to

[06:01] talk about this a little bit more, but the reason why this is the case is mostly because demand is very very strong for their products. Um, you know, and so they need a less resource to go take it to market.

[06:15] >> David, maybe a quick clarifying just before we we um go to this slide here. So, how do we how do you define AI companies? Is that defined as postjack companies? Is that defined as postjack GBT versus historical AI ML companies

[06:27] founded by a certain time period? >> Yeah. Yeah, it's sort of post postg like right around that time. We'd give a little bit of grace but but if they're their first product in market was an AI you know native product then that's how

[06:41] we define it. >> Got it. And then um maybe this is a good but like one of the questions I think a lot of folks uh are trying to understand is the magnitude of change and expected revenue and growth from companies from

[06:57] you've talked a little bit about the magnitude of revenue etc but what happens to those that are not AI native will they have a hard time competing against AI native companies are they all shifting uh will we see more fallout how

[07:11] should people be thinking about their historical portfolio. historical portfolio. >> Yeah. So, the way that we're approaching this with our portfolio is, you know, you you need to adapt to the AI era or

[07:24] die. Um, and so that's both on the front end and the back end. So on the front end, you need to think about how you can incorporate AI into your product natively and not just, you know, attach a chatbot app into your existing

[07:39] with AI and be aggressive about disrupting yourself and changing. Um, and then on the back end, you know, I I I shared some of the stats around the efficiency that the companies are running at. This is going to change too.

[07:53] And so you need to be fully rolled out with the latest coding models for all of your developers um and all of the latest tools across every different function inside your organization. Um the the biggest uptake has been in coding so far

[08:07] and that's where we've seen the biggest leaps. There have been major major changes like in the last two months on this like month and a half in this. Um you know Andre Carpathy has written about this. I was on a catchup with one

[08:21] of our, you know, sort of pre-AII companies. Uh, and this is a this is a founder who's very AI, like he's very AI deep and so he's adapting his company. We were talking this week and he told me that he was frustrated with one of their

[08:38] products and so he just took two engineers that are very deep in AI and assigned them to build it from scratch with cloud code and codeex and cursor

[08:50] and just they had unlimited budget on coding tools. Uh and he said he thinks it's going somewhere between 10 and 20x faster than progress that they had before. And the bills that they have

[09:04] associated with that is actually they're high enough that it will cause him to rethink what his entire organization will look like. The conclusion was will look like. The conclusion was basically I need my entire product and

[09:18] way and I think it's going to happen within the next 12 months. But what does that mean for what the team design actually is and and where does product start and where does start you know and even where does design start in that

[09:31] process. So it feels like December was sort of a turning point on code um and you know the next 12 months it's going to kind of hit it's it's either going to hit and take hold in companies or those companies I think are going to be moving

[09:46] much slower than their peers. Um, so you know, as it relates to the preAI companies, you know, adapt, we have we have another example of a company that have another example of a company that is a pre-ai software company and the CEO

[09:59] has gotten totally AI pill and he's like, we're going to become an AI know, your employees are now your AI agents. How many agents do you have? Like those are the things that he's talking about. Um you know we have

[10:12] another one that was very extreme about it and he said I now ask the question um for for every task that we now need to complete uh can I do it with electricity

[10:24] or do I need to do it with blood like this is like the extreme mindset shift that's happening you know with uh with our companies and and so I I'm I'm happy to see that our PAI companies are moving very fast and trying to adapt uh but

[10:39] they very much need to adapt to this new era both front end product wise and back end how they run their companies. >> Totally. Yeah. Maybe tactically almost every portfolio you have to go line by line on the company to understand where

[10:51] much they are implementing from the ground up and and you know what you said in terms of blowing up existing operations. That's also happening in post AI companies too and and increasingly people are just looking

[11:04] every six months. It's like the things we built six months ago could be vastly today. So that if that rate is continually happening, the preAI companies are needing to to increasingly 10x catch up to that point.

[11:18] >> Yeah. The good news for the prei companies is the business model evolution is still early days. So the most disruptive thing that can happen to you is a technology and product shift and also a business model shift at the

[11:32] and also a business model shift at the same time. There's really one I I think spectrum and you know and I'm talking about like enterprise like B2B just to keep it simple but the spectrum is basically licenses and this was like the

[11:45] pre-SAS you know license and maintenance business models then you had SAS and subscription and that was typically seatbased and that was a big innovation and it was very disruptive like the architecture and cloud delivery was

[11:57] disruptive but the business model change was very disruptive like just go look at what happened to Adobe as they went through that transition. Then you have this transition to consumption based so usage based and this is how the clouds

[12:10] charge and so many of the sort of volume based like taskbased type businesses that from you know seat based to consumption. Um and then the next

[12:22] iteration will be outcome based. So, you know, when you when you do a task, um, successfully complete a task, you get paid based on the successful completion of that task. The only area where that's really possible today to pull off is is

[12:38] probably customer support, customer success, because you can kind of objectively measure the resolution of of something. Um but we'll see what happens with the capabilities of the models to the extent that other functions besides

[12:52] customer support can measure those kinds of outcomes that would be a huge disruptive force uh for incumbents and and honestly seats to consumption might be a big disruption if the composition of companies changes as well. Uh but

[13:05] one >> for sure. Um speaking of blood versus >> for sure. Um speaking of blood versus electricity we should go to AR over FTE. >> Yeah. Yeah. Yeah. So the big the big debate that was going on on this one uh

[13:18] on the next slide was um like oh my gosh look at the AI efficiency gains that are happening in the market. Now there's a companies running themselves a little bit differently and you know you take

[13:34] the two engineers who are rebuilding the product like sure I would say my observation from our companies even the AI native ones is they run leaner

[13:47] partially because they've just grown so quickly and the demand is so strong. I I wouldn't say yet we're at the point where companies have fully reimagined the way they run themselves. I think this is a little bit the result of our

[14:01] companies and demand signals for those being extremely high. Uh so they you know they have less resources to serve that demand and frankly you know efficient general efficiency gains that have happened in the technology market

[14:15] have happened in the technology market you know out of the kind of 2021 most you know most bloated era. Um, so we're starting to see some early signs of that efficiency, but the wholesale run your company totally differently. I think,

[14:29] that in that journey. I'd say the coolest one that I've seen is um in the in the public markets that anyone can go read about is probably Shopify where they, you know, Toby's awesome. Like he's a CEO that's that's close. He's in

[14:43] a bunch of our groups and stuff. Um, and he does a great job and he, you know, he fully embraced this a couple years ago. And then um there one of our staff writers uh actually wrote this whole big deep dive on how Shopify AI itself you

[14:58] know in terms of you know employee direction process etc. Um and that's what's going to happen over the next 5 years. A good seg to the next section on what are these companies actually doing in

[15:13] our favorite topic which is lawyers have only increased in this new world of uh AI's meeting lawyers um not the opposite uh I I love the tweet I don't know if you saw it earlier this week that uh a corporate lawyer was quoted saying LLM

[15:25] because every client thinks they're a lawyer now it's a good seg to Harvey which is the next slide [laughter] >> that's that's very good that's very good real test for me because you know I love talking about our portfolio companies

[15:40] section quickly because uh you know I think people people know these companies uh hopefully um the takeaway on this one you know one of the big things that we look for and um one of the questions I think that came in was how do you know

[15:55] that revenue is going to be sustainable like these companies they all grew really really fast but is it fleeting and the big thing that we push ourselves to do is make sure we go super super deep on revenue venue retention,

[16:09] renewals, uh, and product engagement, actually time spent, how often are they're in the platform, what does their activity look like? And what you see on this page is with the onset of much better product that they've built over

[16:24] the last couple of years, plus the improvement of reasoning models, it turns out lawyering and reasoning uh go go hand in hand. um users are spending about double the amount uh in the product as they had before. So it turns

[16:40] out that AI is is really good at lawyering. Um again there's not fewer lawyers. Uh but I think you know AI is very very good at this and I think efficient. The most important thing as it relates to Harvey is they're just

[16:53] and getting a lot of value out of it which is great. >> Let's go to uh a bridge. Oh unless you want to keep talking about lawyer. Oh, I the seven years that I've known you, I wouldn't have ever uh discerned that

[17:07] you're from Kentucky other than this moment now. By the way, you say lawyer. >> That was a tell. >> My uh there's a there's a couple of those words in my vocabulary. I can't I

[17:20] always jokes. She's like, you know, you then you you talk like you probably did when it came to lawyers.

[17:32] >> It's it's it's 10:25 a.m. I have not had any bourbons today. So, um >> It's important distinctions. Yes, exactly. So, uh a bridge a bridge is another one that's super super exciting. I mean, this is like the doctors rave

[17:45] I mean, this is like the doctors rave about um getting to to have access to a bridge and how much time it saves them uh and how much, you know, better it makes their lives. Um, so you know, one of the customers that we talked to

[17:57] described it like a trusted deputy. The chart on the right shows something we look for, which is the blue line shows the growth in users and the green line shows the engagement of those users. And so as they have massively grown the

[18:13] number of users, you'd be a little worried if engagement of those incremental users that they were adding was going down. but instead they have extremely high usage among the people who use the product and that has

[18:27] actually held steady and grown a little bit even as they've added tons and tons of more users. So the these are just examples of the kind of data that we look for to make sure that we feel confident that the revenue these

[18:40] and again these companies are growing predecessor companies but but it's very sustainable. It's, you know, it's high engagement, it's high retention. Uh, and that's critically important for us. Same

[18:53] thing with 11 Labs. Voice is the centerpiece of so many of the new AI tools. You know, I talked about customer support on the B2B side. Um, but, you support on the B2B side. Um, but, you know, so much um, you know, other

[19:05] personal tools, business tools, you know, start start with voice. Um, the usage growth is the thing that I love to look at on this chart. It's just staggering. Uh and this company is growing very fast and is a great example

[19:17] of one of these companies that runs extremely efficiently. Um so 11 Labs is extremely efficiently. Um so 11 Labs is is really is really a great one. Non is the next one. So this is another this is a different example. So this is actually

[19:30] a good example of what I was describing earlier. So they were early to this, you earlier. So they were early to this, you know, AI shift and uh and and they spent a lot of effort making sure that they could take the most of the AI

[19:42] capabilities and make their business better. And so the biggest way you can better. And so the biggest way you can see it in their business today is in uh part of what they have is, you know, agents that have to handle travel

[19:55] bookings or travel changes. AI is now handling 50% of those user interactions. And this is hard stuff like this is travel bookings. This is changes to travel. Uh so this is not you know complex like tell me the balance of my

[20:10] bank. Uh you know this is like complex workflow that that AI is now able to handle. The way you see that in the business is a 20 percentage point expansion of gross margins over the last 3 years. And that's just exceptional

[20:25] impact. And so you know you need to adapt or die. Well their competitors are not adapting. They're very old school and while you know they've been sitting still and and doing things the old way, Non now has 20 percentage point higher

[20:40] gross margins than those incumbents. And then you know Flock Flock is doing absolutely incredible work. I've talked about them so much. It's it's the most compelling customer value proposition that we see in our

[20:54] portfolio because what their ROI is is solving crime. Um the 10% stat we've covered before. Each year Flock is solving 700,000 crimes. Um the the the

[21:08] data point on the right also is a data point that just shows per officer that where there's flock they're clearing almost 10% um you know more crimes. So they have a great you know they have a great business and financial model that

[21:23] impact uh on their product or from their product is is exceptional. >> Uh by the way, I don't know if you see the chat lighting up of people saying that they're three bourbons deep. Uh >> oh, I didn't see it.

[21:37] >> For what it's worth, uh there is one question about um how do you think about the the benchmark? Like if you were to think about traditional industries like finance for example and using JP Morgan as a benchmark, what would you calibrate

[21:49] the Fortune 500 in terms of AI adoption? And then maybe I'll overlay that that question that that Xavier mentioned as well with you know there was that study about enterprise adoption from MIT at the early outset of last year and they

[22:03] were measuring all sorts of wonky things. Uh maybe say a little bit more about how and what you're hearing from Fortune 500 CEOs. >> Yeah. Um what we're hearing from Fortune 500 CEOs I would say is and maybe this

[22:18] two points. What we're hearing from Fortune 500 CEOs is we have to adapt. We're dying to understand what AI tools we need. Um, you know, we're ready to change. We, you know, our businesses are going to fully roll things out and, you

[22:34] know, we're we're ready. We're going to become AI companies. That's quite different than what is actually happening. And I think the biggest disconnect of sort of, you know, that mindset compared to actual change

[22:50] in the businesses is just change management is hard. Um, you know, it's it's hard enough to get people to just use an AI assistant to help them do their jobs better. Um, you know, coding is probably the easiest one to get

[23:05] people's minds wrapped around. Customer support. It's such a better, faster, cheaper, obvious thing. But in terms of actually, you know, general management of businesses, changing business processes, change management, it's

[23:20] processes, change management, it's extremely hard to do. And so I'm not surprised that there are anecdotes out there that suggest, oh, you know, things are moving slower than expected, but for the best companies that are fully

[23:33] embracing it and actually know what to do, it has tremendous business impact already. Uh so you know I think there's going to be a sort of reckoning over the next five years of who can actually embrace change push through change

[23:46] management you know adopt all the best products um and those that don't and I productivity you know we have some charts later in the slides you know expectations around productivity

[24:00] enhancements and you know and growth and all that stuff um you know the expectations are high and I think a bunch of companies will achieve those and the ones that don't are going to be at a huge disadvantage. Chime said they

[24:12] at a huge disadvantage. Chime said they reduced their support costs by 60%. Um, reduced their support costs by 60%. Um, Rocket Mortgage said that they saved 1.1 million hours in underwriting, up 6x year-over-year, and that was 40 million

[24:24] year-over-year, and that was 40 million bucks of run rate annual savings. So, bucks of run rate annual savings. So, you we're seeing pockets of it in nonAI be a really interesting year to watch over the next 12 months. I think you're

[24:37] there will be companies that can figure it out and there are going to be companies that don't. >> Totally. And also, they've a lot of these corporations have had to orient their business to be ready for AI as

[24:49] well. Like there's one version of just like using a chatbot, right? And how gets you? Probably not a lot, right? But if you have to actually completely upend your systems information and backend to be ready for AI, a lot of that is

[25:04] probably latent and and being built up now into actually seeing the outcomes >> AI winners are driving the public markets. They account for almost 80% of the S&P 500's return. So this is sort of the major thing driving the economy and

[25:19] the stock market. Public markets are doing very well. Um but the fundamentals are sound. So the prices are going up or you know there's some blips like the last couple of days but they're generally doing well. Um but the

[25:33] would say the evidence of froth is minimal. So recent performance is driven by EPS growth. Um multiples have contracted slightly maybe more than slightly uh if you're a SAS company over the last few days or a couple weeks. Um

[25:49] but I would say the market is priced on in general uh earnings earnings and earnings growth. So the earnings multiples are higher than average but nowhere near the dot. And so you can just look at the charts and see where we

[26:04] are and you know that that gives me some comfort. And again the earnings of the companies that are the biggest drivers of the market in general I feel like are So, you know, the the health of these companies, I would say, is pretty good

[26:18] and and the valuations are higher than average in the past, but they don't feel super alarming. I often say the leading tech companies that I was uh I was just talking about are the best businesses in the history of the world. Um, if you

[26:32] just look over a long period of time, they have shown margin improvement that that's, you know, that's on the left side of the page. So, investors are paying for profits, not lossmaking

[26:44] growth. Um, and that's a big contrast from 2122 era, sort of 21 era. Um, and obviously a big contrast from a dot adjusted for margins. Um, multiples are

[26:57] are not that high. And so again, I like summarize, you know, five slides worth of materials. The market's higher than it has been in the past, but I think, you know, there's high expectations for a reason. and and we're optimistic about

[27:10] earnings, you know, overall in the public markets in the coming years. Uh, and maybe I'd focus your attention on the right side, which is um, you know, if you just took a fourbox of like low growth, high growth, low margin, high

[27:24] margin, and paired up those types of companies. This is a chart that shows how they trade. There's a premium for the best companies. And what you see on the the two columns on the right is high growth, high margin companies and then

[27:37] high growth and low margin companies. Your bad box is obviously low growth, shouldn't be rewarded. They they they should trade low. Uh and they do. But the companies that are high growth and high margin um and you know the high

[27:51] have good unit economics and they're scaling into their margins, they should be rewarded. And so I think this is good. Um, if you're not high growth, even if you're high margin, it's tough out there. And that's not surprising.

[28:04] Again, I've talked about this in the past in many different forms. But ultimately, growth is the biggest thing that drives returns over 5 to 10 years. And so, it's nice for me to see high growth is rewarded more than low growth.

[28:18] margin, you're one of those great businesses, it's being very rewarded. This is just like we're going to talk about supply side of the capex buildout. So the buildout's massive, the size and the concentration uh of the investment

[28:33] is inherently risky just given how big it is. Um while it has some bubbly features, the underlying fundamentals I would say bear little resemblance to previous bubbles. Um the investment is financed primarily by historically

[28:47] profitable companies that I had talked about. Um debt has started to enter the picture. um cycle times have accelerated which is good but you know model we're closely monitoring the sort of cost of training and the economics of that whole

[29:02] equation right now it seems pretty good the paybacks for the big model companies that spend money on training models is pretty good uh but we're monitoring that closely most importantly we think that AI is going to be you know the biggest

[29:15] model buster that I've seen in my career certainly um I've written about model on them but they're companies that grow faster and longer than anyone would have would have modeled in any scenario. Like iPhone is the classic case of this. You

[29:29] know, if you if you take consensus models uh from pre iPhone to 5 years later, four years later, consensus models were off for Apple's performance models were off for Apple's performance by a factor of 3x over four years. And

[29:43] this is like the most covered company in the world uh at the time. So, you know, I think that the same thing is going to happen in many pockets of AI where the performance just massively uh exceeds, you know, what any expectations in a

[29:55] you know, what any expectations in a spreadsheet would would show you. So, tech in general is itself a model buster, but since 2010, tech has delivered high margin revenue at unprecedented speed and scale. So it

[30:09] often looks expensive early, but repeatedly surprises to the upside, I would say, um, and creates value, I would say, far in excess of the capital have no reason to think it'll be different, you know, this time around.

[30:24] So relative to the dot, capex is actually supported by cash flows, and actually supported by cash flows, and capex as a percentage of revenue is considerably lower. So that's simple headline. We can zoom to the next slide

[30:37] but you know I feel much better about this capex um you know dynamic than than this capex um you know dynamic than than than do obviously hyperscalers are the ones who are bearing the biggest brunt of the capex and this is a very good

[30:51] thing you know for our portfolio companies this is great like I am all for it get you know get as much capacity in the ground get as much supply as you training and inference this is a very good thing and Again, the companies that

[31:06] are bearing most of the brunt of this are the best businesses of all time that I had talked about before. So, one thing that we're starting to monitor is the introduction of debt into the equation. So, you can't finance all of the

[31:19] forecast capex that's to come with cash flow, and we're starting to see some debt. So, we're following this closely. Um, we're generally not invested heavily in companies with exposure to debt. Um, do I feel comfortable with a bunch of

[31:32] the companies on the page financing with cash flow, continuing to produce cash flow, and using debt even, you know, Meta, Microsoft, AWS, Nvidia as great about that. I mentioned the ones I

[31:46] feel great about. I don't feel great about all of them. So, not all counterparties are the same. You know, we're starting to see Private Credit get a little bit more involved in the data center buildout. And you know again the

[31:59] company that's very well covered uh that is kind of making a bet the company move into becoming a cloud is is Oracle and they've you know they've been profitable forever and reducing their shares forever. Um but the amount of capital

[32:13] that they are committing um is very large. It's a big bet. They're going to come. Um, and you know, if you follow cost of their credit default swaps has gone up um, you know, to like 2% uh,

[32:28] over the last three months. And so, we're watching stuff like this. Again, our portfolio companies, but we want to make sure that the market overall is healthy as well. So, this is just a slide that shows the magnitude of the

[32:42] pace of change of AI. So, comparing AI buildout and AI revenue to what happened with Azure. So the AI revenue is coming along relative to the cloud. It took Azure 7 years to reach one year of AI revenue. Um so this this is just

[32:58] Microsoft reporting data which I think is a a cool way to to frame how quickly build's taken a very long time. Again this this AI buildout is happening much faster. Um but it took 10 years for Azure revenue to surpass their capex. Um

[33:12] ratio or equation is going to happen much faster with AI. topics that gets a lot of buzz in finance circles, you know, just what are

[33:28] your assumptions around depreciation of chips in particular. Um, I would say the chips in particular. Um, I would say the pricing for older GPUs is very solid. Um, early users stick with models a bit longer, but later users quickly switch

[33:44] side. That's like kind of the model side. On the chip side, um 7 to 8y old TPUs, Google actually disclosed this, 7 to 8y old TPUs actually have 100%

[33:56] utilization. Um and we very closely monitor the price of chips in the secondary market and the price to rent A100s and H100s um has actually held up very very well. So older generations of chips are still still getting fully

[34:10] utilized. So this is not something I worry about uh yet but it gets a lot of buzz and you know sort of alarmists uh who like to to talk about risk in the who like to to talk about risk in the system.

[34:25] the big thing that we talk about all the time uh is is is this paradox right like as tokens get cheaper consumption goes up. All the hyperscalers report demand is well in excess of supply. I believe them when they say that. Um, you know, I

[34:41] interviewed uh Gavin Baker, friend of mine on our at our AI summit, and he was comparing the buildout of uh the internet and and laying all the fiber to the buildout of data centers here. And, you know, his his big line was there is,

[34:55] you know, there is no dark GPU. There are no dark GPUs. There was a dark fiber. You had to lay fiber and then, you know, it laid there dark and it wasn't used. If you put a GPU in the system in a data center, it gets fully

[35:09] utilized immediately. And so that's a very good sign, you know, in terms of, you know, demand meeting supply uh immediately. I mentioned this earlier, earnest growth should come for these companies like this is our expectation.

[35:21] Um, and if it doesn't, then they will probably be disrupted uh if they can't change. So change management again is the biggest reason why we see things um you know that that haven't sort of dramatically shifted yet. Um it's

[35:35] honestly to to me it's not the readiness of the technology itself. It's probably you know product buildout that needs to get built around the technologies. Uh and then change management and and putting it in production. So revenue

[35:49] growth has scaled at a staggering clip relative to other categories. Uh so this is just it shows how quickly generative AI uh in app revenue has grown uh from 23 where it was basically you know you can barely even see it on the page to to

[36:04] now and this is a slide that we've showed before but basically this compares the clouds um public software companies uh and then how much net new

[36:16] companies uh and then how much net new revenue gets added in 2025. So the far right is what I like to look at, which is public software companies added $46 is public software companies added $46 billion of revenue in 2025. If you just

[36:29] add up OpenAI and Anthropic on their on a run rate basis, they added almost half of that. And I think if you were to do that same comparison for 2026, all of the entire public software industry, I mean SAP, this is not just SAS, like

[36:42] including SAP, um, and older software companies. I think the AI companies, the model companies will be something like 75 to 80% as much. So, it's just staggering how quickly that has happened. These are pretty detailed

[36:56] slides, these next couple ones. Um, these are sort of slides showing what is implicitly expected in AI performance based on where stock prices are today um in analyst models. So, Goldman Sachs estimates 9 trillion of revenue flowing

[37:11] from the buildout of AI. So if you assume 20% margins and a 22 times PE assume 20% margins and a 22 times PE that translates into 35 trillion of new that translates into 35 trillion of new market cap um there's been about 24

[37:24] pulled forward. Now we could debate if that's attributable all to AI or otherwise you know large tech performance. Um but there's still a lot of of sort of market cap to go get um where you could have upside if you know

[37:39] if those assumptions are right. So this is another sort of cut or or few cuts on trying to address this sort of AI AI payback question. So current estimates payback question. So current estimates put cumulative hyperscaler capex at a

[37:53] little less than 5 trillion by 2030. So if you do napkin math on that to achieve if you do napkin math on that to achieve a 10% hurdle rate on that 4.8 trillion annual AI revenue would have to hit about a trillion dollars by 2030. So to

[38:08] put that into context, a trillion dollars, that would be about 1% of global GDP to generate a 10% return. It's possible that happens. It's also that. But I think it's limiting just to look to 2030. I think the the payback of

[38:23] this probably happens, you know, over a longer period of time, like, you know, between 2030 and 2040 as well. Um, but you know, framing it up, that's about, you know, 1%, you know, 1% GDP to get to to get to the payback number of a 10%

[38:37] hurdle rate. All right. Heard it on the street. What we've started to do is we've sort of built software to track what all of the AI or companies discuss in their earnings

[38:50] calls and mentions of AI, how relevant it is to our business at the early stage then we package it all up and we share it out to our CEOs. Um, so you know they

[39:03] can kind of have a simple digestible format of like what do I need to know about AI as it relates to public technology companies you know how does And so we shared a bunch of the you know the stuff that we that we track in here.

[39:17] before we moved to the private section which a lot of folks of course on this call care about in this transition here. Um, but before we get to that, so where are we calibrating to your trillion dollar in AI revenue, you know,

[39:31] thereabouts in in 2030? Where are we today relative to your guesstimate of AI enabled revenue? And and um how far off are we to that trillion dollar number? [snorts] We're probably

[39:46] We're probably in the >> Just add it all up. And there's no perfect way to do it. I mean, I know I >> Yeah. >> Uh to it. The harder stuff to track is

[40:01] honestly the the big tech companies like how much real AI revenue do they have? the cl the clouds can kind of they will from time to time give percentage uplift from AI but I think depending on how they want to paint the picture they can

[40:14] play games with that a little bit so you know I think it's I think it's I that's a that's a rough swag but like you know trillion we're probably at 50 but it's growing you know way way way faster than 100% year-over-year.

[40:28] >> Yep. And then arguably that revenue I mean chat GPT launched three years ago but substantially most of this traction happened in the last year and a halfish or so if we're being really generous too. Is that a fair characterization?

[40:41] >> And look, you know, it's not just CH GPT now on the consumer side. You know, business. >> Um, and then, you know, on the B2B side, >> you know, not only do the big model companies all have large API businesses,

[40:55] lot of the, you know, a lot of the sales that are model sales are also flowing >> Yep. Yep. Yep. Yep. Okay, cool. We have, uh, some questions on on the private up for it. >> Well, you I I'm happy to go into

[41:10] this a lot of the stuff that we've talked about, you know, the big themes for me on the private market side, um, you know, companies are obviously staying private longer, but this is such a real asset class now. Over the last 20

[41:24] years, the number of public companies has been cut in half. Um, you know, the the vast majority of companies that are hundred million dollar plus revenue companies are private, something like 86%. Um, so, you know, that's that's a

[41:39] major shift. Um, we could go you can skip a couple slides forward. Uh basically I'll talk a little bit about power laws because that's I think that's that we haven't talked about as much but value very much concentrates in the

[41:53] outlier companies. So the collective valuation of North American and European valuation of North American and European unicorns is about $5.5 trillion. The 10 largest ones if you just take those um comprise almost 40% of the entire value.

[42:08] So and that's actually doubled since 2020. So sort of value you know sort of value is being uh concentrated in the biggest and best winners. I'm trying to biggest and best winners. I'm trying to count real time we have four five six

[42:23] seven of the 10 our portfolio companies of that 10 um so you know we we've got a a reasonable amount of coverage on that. Power laws are happening in the public markets too. So large cap has tripled since 2019. So what what constitutes a

[42:40] large cap company has actually tripled since 2019. And I think this the chart on the right side is super interesting. This was new data analysis that we had This was new data analysis that we had done. Um if you look at the lifespan of

[42:52] an average company on the S&P 500, that's what that chart shows. That's what the numbers represent. The like once a company is on the S&P 500, how long is it on there? This is on average is actually if you look over the last 50

[43:05] is actually if you look over the last 50 years that has declined by 40%. The amount of time it stays as part of the S&P 500. So disruption to companies happens faster and faster and faster which I think is a very interesting

[43:17] we're seeing you know just in terms of like speed of change in in the markets driven by technology. So we always like to talk about power laws in our business too. I didn't choose the title of this slide. Uh, [laughter] I recognize all of

[43:31] the, you know, questions and concerns about it. Um, so the the volatility laundering thing is is a is a big debate in our circles too. Um, mostly around founders who are trying to debate the merits of the private markets and the

[43:46] public markets. And you know, the Collison's did an interview where I think maybe it was John uh did an interview where he talked about, you know, managing your stock price and avoiding volatility and you can kind of

[43:59] orderly fashion, bring your stock price up over time and that makes it easier to retain employees, hire employees, manage morale, uh, etc., etc. Um, and so I get

[44:11] are really really strong merits of being a public company as well. I think we're interesting 18 months where we're going to have some of the big kind of private

[44:23] for a very long time companies that go public. Um and that's a good thing in my opinion too. Um some of the stuff that we show in this chart is just volatility and the observation that over time volatility has gotten a little bit more

[44:37] extreme in the markets. To me this is a little bit cycle driven too. I know it's short short duration is sort of what we're measuring. Um but there's merits to both companies can get much larger in the private side. We have embraced that

[44:50] new reality. I think it's it's been a big benefit to our business in terms of getting getting to continue to invest in these companies over time. Uh but obviously you know there's there's a path of of being a public company and

[45:02] getting liquidity which we care a lot about too. >> Awesome. that note um there were uh two questions uh I will queue up for you here uh one on data bricks can you talk about their transition uh from being a

[45:14] preAI company now to a fully embedded AI company and what that's been like >> yeah um first of all I think you need to you know I mentioned Toby like the reason Shopify has embraced it is because Toby has led from the top and he

[45:29] runs the business you know with AI at the center and and he he sort of performance manages everyone uh to you to make sure that they do that. Ali is the same. Ali is this unique blend of um sort of commercial kind of terminator. I

[45:43] technical terminator. You need to have a commercial instinct and understand the importance of the value creation opportunity and AI and then you need to actually be deep enough in the technology to know what to build. And so

[45:55] technology to know what to build. And so it just so happens that their um their sort of cloud data warehouse or they call it the data lake um is actually a great way to have your data in a place to run AI workloads on top of it. So you

[46:10] know that was sort of a good place to be for them and then they've very aggressively iterated on new AI products. They have this uh new product super excited about. we think is going to be really big and transformative for

[46:24] them. So um I would say that's a piece of it and then they have the big AI native companies all as customers and so you know they have the technology they have the lowcost technology um and so you know a big thing that we look for

[46:38] when we're making investments in companies is who are their customers and companies is who are their customers and I would far prefer the customers of our portfolio companies to be the modern thinking ones you know the Door Dashes

[46:50] of the world um you know the Instacarts of the world the Ubers of the world than the very very old school stodgy companies because that means that their technology is evaluated by smart technologists and they pick it and so

[47:03] the cutting edge AI companies are all building on top of data bricks uh and so with them as they scale uh but it's also a really good you know validator that they have the right technology >> we'll close out here thank you David for

[47:16] >> we'll close out here thank you David for taking us through that