00:06:23 · Ev Randle, Molly O'Shea
All those things I said fit really, really nicely in a spreadsheet, and there was metrics like rule of 40, which was your growth rate plus your free cash flow margin that were, like sometimes you could literally abstract the quality of a software company into a single metric, and some investors would invest just on like a rule of 40 score in many times, and now, if you think about all the most popular companies, all the most popular categories, there's sort of like the inverse of every single one of those golden rules that I just said. It's like, you know, there was a tweet that said like, FDE is the new PLG. Like, it's the most popular distribution strategy, and [00:07:04] S1: Thank you to Paleteer.
00:07:05 · Ev Randle
[00:07:05] S0: That's right. Yeah. The, yeah, yeah, the Paleteerification of everything means that now you have all these folks that are, you know, sort of like sparkling implementation consultants that used to be used to be bad. Like you weren't supposed to have that. You were supposed to have selling pure software, but that draws down gross margins, and then now gross margins, if your gross margins are high, that's actually a bad thing, because AI inference costs a lot of money, and if you have an AI product with high gross margins, that means that no one's using your AI features. So there's this bizarro world where, and then now another popular thing right now is like, well, if you're gonna be safe from the labs, you need to have, you need to be training your own models, or at least be post training on the data that you're getting from your users, and so now it's like, okay, now you want people to set up their own research labs within a software app company in order to train their own models, that's unbelievably capital, you know, CapEx intensive or capital intensive relative to the old era of software where you didn't have any CapEx or any kind of capital outlays on GPUs or anything like that, and so it's like all these bizarro things where it's like all of the golden rules of the past that defined the spreadsheet investing era are all gone, and actually the most popular companies and categories are almost the inverse of all these golden rules, and I think the reason why that's so confusing for people is because these golden rules in a vacuum, like they are the first principles building blocks of a high quality company. Because like at the end of the day, you need to have a company that's producing a lot of free cash flow for a very long period of time in order for it to be valued highly, and when the most popular companies, you kind of look at them and you say, well from a first principles basis, they just seem less attractive than SaaS, you have to kind of rethink a lot of how you invest.
00:08:58 · Molly O'Shea
[00:08:58] S1: I have so many secondary questions to this. The first one is, who do you think has the best instinct on managing these AI scale and economics in the companies? Is it the founders, are you finding it within different firms or other kinds of researchers or economists? Is this new to everyone? How are you making sense of it, and who do you think has the best handle?
00:09:22 · Ev Randle
[00:09:22] S0: It's a great question. It's really interesting because these companies are also extremely different from each other. Yeah. Like, if you take, you know, a company in our portfolio like Fireworks, which, you know, we think is becoming sort of the the AI inference cloud, and you compare it to another incredible company that ostensibly is in the same category, like a Crusoe, they're actually completely different companies with completely different business models, with completely different economic models. What I mean by that is that Crusoe is actually going and constructing data centers. They're going and acquiring power, land, permits, and they're building data centers oftentimes for hyperscaler counterparties who become their customers, and then sometimes they also have their own cloud product where they can sell to inference startups or whatever, whereas Fireworks does not have their own data centers. They're actually leasing inference capacity and GPUs from partners themselves, and then the thing that they're getting paid for is both the inference but then also what their software is doing on top of the inference to make it more cost effective and and, you know, reduce latency and just, like, make it a better inference product altogether. And so from the outside in, could say, like, oh, it's just, two inference companies. But they have completely different business models. They have completely different capital intensities, they have completely different margin profiles, they're actually more different than they're alike, and I started my career at actually a software PE firm called Vista Equity Partners, and the CEO, Robert Smith, would always tell us software tastes like chicken, and that's why it's beautiful. Every software company, when you look at the balance sheet, at the P and L, they're all the same. They all have the same line items, they all essentially have the same, like the P and Ls look the same at maturity, They're more similar than they're alike, even if they're selling to completely different markets or to completely different products, and that is also not the case now. There's so many different business models that exist in AI.
00:11:24 · Ev Randle
If you're an AI app creator, if you're a foundation model lab, if you're an inference platform, if you're a data center construction company, they're just all over the place, and so it's sort of hard to tell. The answer to that would actually be on a case by case basis, depending on the category and the company, but I think that there's a definitive advantage to investors and founders that have gone through the thinking of what is the new taxonomy for an AI company, because what it comes down to in my mind is this idea of P times Q times M. This is like Econ 101, what's price times quantity times margin? And in Saasland, you had like price was your ACV, like what is the annual contract value of your contracts that you sell to customers. Your Q is like how many customers either do you have that are in your TAM. And then your M was again gross margin, was 70 to 90%. Well now in AI, if we take an AI app company, the Q is probably the same, like you're selling to the same people that would buy SaaS. The M is almost definitively lower for I think 99% of AI app companies, it's lower than 70%, but the P can be immensely high. Have these inference platforms that have 9 figure contracts with startups. There's very rare SaaS companies that have 9 figure contracts with anyone, much less a startup, and now you have a lot of these AI companies that have just these unbelievably large contracts, and I think that understanding that taxonomy and understanding what it means to the evolution in company quality and how these things will look when they mature, I think is a very important dynamic that's still being figured out by everyone on the field.
00:12:57 · Molly O'Shea
[00:12:57] S1: So are you saying it's disorienting for making net new investments, or also understanding current portfolio companies and how they're growing and scaling in different ways, marginally and business model wise?