00:03:19 · Ev Randle, Molly O'Shea
In many ways it feels like the risk of impairment or a company either going to zero is the worst case scenario, but even just being devalued from its last round in a significant way, it feels like the risk of that over time is actually sort of flat, or even like maybe there's like a weird positive correlated relationship with scale and risk, and it's a very disorienting thing because you're just very used to the fact that the bigger you are, the safer the company is, and it doesn't feel like that anymore, and there's a lot of reasons for that that we can go into, but I think that's the reason why folks in the market and investors and our peers and even us feel like something is, like the risk reward paradigm is very different these days. [00:04:05] S1: How do you evaluate that as an investor? We were kind of talking about this beforehand, but you say this is the death of spreadsheet investing, is that kind of what you mean? Is it categorical, or what's the premise there?
00:04:18 · Ev Randle
[00:04:18] S0: Yeah, you know, we used to have all of these golden rules, or like North Star metrics in investing, and there's some exceptions to this in, again, hard tech's always been different, or even some of the capital intensive consumer internet businesses of the past, but when you think about software, there was sort of five or six things that mattered. It was like high gross margins are better than low gross margins. So at least 70%, 80% is good, 90% is amazing. You wanna be selling typically pure software, which means you don't have a ton of services load and implementation load on your software because that limits your ability to scale, and it also brings down your gross margins. Typically, businesses were capital light. There wasn't a lot of CapEx, and you had a lot of operating leverage on your R and D spend. R and D was relatively efficient over time. You had a really high gross retention, which means that your customers, once they implemented your product, typically stayed around. And a lot of these software companies had 90% plus gross retention rates, which means that over a year long period, if you had 100 customers, less than 10 of them would leave a year. So there's a lot of things like that that basically when you took all of them, the output you got was a capital light business that essentially at maturity would spit out really high rates of free cash flow in a way that secularly grew at multiples of GDP. So it's like sort of a mouthful, but you can think about the whole reason why software companies traded at these high multiples of NTM revenue, and this is really important for an asset class whose foundation is paying really, really high multiples of ARR, The whole reason why we're sort of allowed to do that or have been allowed to do that is because eventually the companies are extremely profitable, they're very durable, like those profits are very durable, and the profits grow at a multiple of GDP for a very, very long time. So that was sort of the setup of software, and it was very legible and it was very spreadsheetable.
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.