B (24:44)
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 the stock market. Public markets are doing very well, 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. But the fundamentals are very sound and I would say the evidence of froth is minimal. So recent performance is driven by UPS growth. Multiples have contracted slightly, maybe more than slightly if you're a SaaS company over the last few days or a couple weeks. But I would say the market is priced on in general earnings earnings and earnings growth. So the earnings multiples are higher than average but nowhere near the dot com and so you can just look at the charts and see where we 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 pretty sound. The companies are good. So you know the, the health of these companies I would say is pretty good. 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, I was just talking about are the best businesses in the history of the world. Um, if you just look over a long period of time they have shown margin improvement. That suggests that is probably true. And that's, you know, that's on the left side of the page. So investors are paying for profits, not loss making growth. And that's a big contrast from 21, 22 era, sort of 21 era and obviously a big contrast from a dot com adjusted for margins multiples are, 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 the impact of AI flowing through to earnings, you know, overall in the public markets in the coming years. And maybe I'd focus your attention on the right side which is you know, if you just took a four box of like low growth, high growth, low margin, high margin and, 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 high growth and low margin companies. Your bad box is obviously low growth, low margin. And those companies shouldn't be rewarded. They, they, they should trade low and they do. But the companies that are high growth and high margin and you know, the high growth and low margin, as long as they have good unit economics and they're scaling into their margins, they should be rewarded. And so I think this is good if you're not high growth, even if you're high margin, it's tough out there and that's not surprising. Again I've talked about this in the past in many different forms but ultimately growth is the biggest thing that drives returns over five to 10 years. And so it's nice for me to see high growth is rewarded more than low growth. But if you have high growth and high 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 build out. So the buildout's massive. The size and the concentration of the investment is inherently risky just given how big it is. While it has some bubbly features, the underlying fundamentals, I would say bear little resemblance to previous bubbles. The investment is financed primarily by historically profitable companies, like very profitable companies that I had talked about. Debt has started to enter the picture cycle times have accelerated, which is good but you know, model we're, we're closely monitoring the sort of cost of training and the economics of that whole equation. Right now it seems pretty good. The paybacks for the big model companies that spend money on training models is pretty good, but we're monitoring that closely. Most importantly, we think that AI is going to be, you know, the biggest model buster that I've seen in my career. Certainly I've written about model busters so I won't spend too much time 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 know, if you take consensus models from Pre iPhone to five years later, four years later, consensus models were off for Apple's performance by a factor of 3x over four years. And this is like the most covered company in the world 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 exceeds what any expectations in a spreadsheet 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 often looks expensive early but repeatedly surprised to the upside I would say and creates value, I would say far in excess of the capital that's required to grow. And I have no reason to think it'll be different, you know, this time around. So relative to the.com, capex is 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. But you know, I feel much Better about this capex, you know, dynamic than than than dot com. Obviously hyperscalers are the ones who are bearing the biggest brunt of the capex. And this is a very good 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 as you possibly can on the ground for training and inference. This is a very good thing. And again, the companies that 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 forecast capex that's to come with cash flow and we're starting to see some debt. So we're following this closely. We're generally not invested heavily in companies with exposure to debt. Do I feel comfortable with a bunch of 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 counterparties, of course I feel great about that. I mentioned the ones I 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 build out and you know, again the company that's very well covered, 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 but the amount of capital that they are committing is very large. It's a big bet they're going to go cash flow negative for many years to come. And you know, if you follow some of the buzz around it, like the cost of their credit default swaps has gone up, you know, to like 2% over the last three months. And so we're watching stuff like this again. This is all generally good stuff for 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 pace of change of AI. So comparing AI buildout and AI revenue to what happened with Azure. So the, the AI revenue is coming along relative to the cloud. It took Azure seven years to reach one year of AI revenue. So this is just Microsoft reporting data which I think is a cool way to frame how quickly this has happened. The build's taken a very long time. Again this AI build out is happening much faster. But it took 10 years for Azure revenue to surpass their capex. And I think that sort of ratio or equation is going to happen much faster with AI. We don't need to geek out too much on depreciation but this is one of the topics that gets a lot of buzz in finance circles. You know, just what are your assumptions around depreciation of chips in particular? I would say the Pricing for older GPUs is very solid. Early users stick with models a bit longer, but later users quickly switch to the new thing. So that's the right side, that's like kind of the model side on the chip side. 7 to 8 year old TPUs, Google actually disclosed this. 7 to 8 year old TSUs actually have 100% utilization and we very closely monitor the price of chips in the secondary market. And the price to rent a 1/ hundreds and H1 hundreds has actually held up very, very well. So older generations of chips are still, still getting fully utilized. So this is not something I worry about yet, but it gets a lot of buzz and you know, sort of alarmists who like to, to talk about risk in the system. All right, some positive stuff. So the, the big thing that we talk about all the time 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. You know I interviewed Gavin Baker, friend of mine on our, at our AI summit and he was comparing the build out of the Internet and laying all the fiber to the build out of data centers here. And you know his, his big line was there is, 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, laid there dark and it wasn't used. It. You put a GPU in the system, in a data center, it gets fully utilized immediately. And so that's a very good sign, you know, in terms of you know, demand meeting supply immediately. I mentioned this earlier. Earnings growth should come for these companies like this is our expectation. And if it doesn't then they will probably be disrupted if they can't change. So change management again is the biggest reason why we see things, you know, that, that haven't sort of dramatically shifted yet. Honestly to me it's not the readiness of the technology itself. It's probably, you know, product build out that needs to get built around the technologies and then change management and putting it in production. So revenue growth is scaled at A staggering clip relative to other categories. So this is just, it shows how quickly generative AI in app revenue has grown from 23 where it was basically, you know, you can barely even see it on the page to now. And this is a slide that we've showed before. But basically this compares the clouds public software companies 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 billion of revenue in 2025. If you just 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 SaaS, like including SAP 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 slides, these next couple ones, these are sort of slides showing what is implicitly expected in AI performance based on where stock prices are today in analyst models. So Goldman Sachs estimates 9 trillion of revenue flowing from the build out of AI. So if you assume 20% margins in a 22 times PE, that translates into 35 trillion of new market cap. There's been about 24 trillion of new market cap that's been pulled forward. Now we could debate if that's attributable all to AI or otherwise large tech performance. But there's still a lot of sort of market cap to go get where you could have upside if those assumptions are right. So this is another sort of cut or few cuts on trying to address this sort of AI AI payback question. So current estimates put cumulative hyperscaler capex at a little less than 5 trillion by 2030. So if you do napkin math on that, to achieve a 10% hurdle rate on that 4.8 trillion or almost 5 trillion of investment, annual AI revenue would have to hit about a trillion dollars by 2030. So to 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 possible we could fall a little short of that. But I think it's limiting just to look to 2030. I think the, the payback of this probably happens, you know, over a longer period of time, like you know, between 2030 and 2040 as well. 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% 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 what all of the tech, public technology companies discuss in their earnings calls and mentions of AI, how relevant it is to our business at the early stage and you know, the growth stage. And we package it all up and we share it out to our CEOs so you know, they 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 it, how does it impact my business, et cetera. And so we shared a bunch of the, you know, the stuff that we, that we track in here.
