00:00:00 · Ev Randle, Molly O'Shea
[00:00:00] S0: We used to have golden rules or like North Star metrics in investing. When you think about software, there was like sort of five or six things that mattered. And it was very legible and it was very spreadsheetable. And now all of the golden rules of the past that defined spreadsheet investing era are all gone. In the new AI paradigm, you can have businesses that are well over $1,000,000,000 in revenue that haven't proven out their unit economics. They haven't proven out durable product differentiation. We have developers that are spending $3,000 per month themselves, like, each on Cloud Code. So it's like, wow. Okay. So that's $36,000 per developer. When you think about the Anthropic $380,000,000,000 round, if Anthropic was to go public and get liquid at a $1,500,000,000,000 valuation, I just don't know if people really understand and are ready for the impacts that this much liquidity could have on the ecosystem. [00:00:58] S1: Ev, welcome to Sorcery. [00:01:00] S0: Thank you, Molly. It's great to be here. [00:01:02] S1: I'm so happy. This is like the first interview I've ever done with someone from Benchmark. You're one of the newer partners. I did an interview with Jack before he joined. [00:01:11] S0: But [00:01:12] S1: now we have you. [00:01:13] S0: That's great. I'm excited to be the first. [00:01:15] S1: So what's going on? I know you guys are just coming off the heels of your AGM. You had a mass kind of collective reflection on the market today. How are the vibes?
00:01:24 · Ev Randle
[00:01:24] S0: Yeah, vibes, I mean, the vibes within Benchmark are definitely high. Think as a market, I think the whole venture growth ecosystem is going through this sort of phase of disorientation. Like everyone sort of feels like down is up, up is down. And I think that we were trying to as we have our annual meeting with all of our partners, oftentimes that's when we try to introspect and figure out how we've been feeling about the market and things we've been looking at. And the one visualization or the one framework that I came up with that I think explains a lot of it is that in the previous paradigm, especially in software, you had almost like this inverse relationship between scale and risk, or scale and risk of mega impairment or a company going out of business. And that's because historically, as you're scaling as a startup, you're sequentially de risking different parts of your business. And so the first thing you de risk typically is can you get product market fit? Do people want to buy your product? And then you de risk unit economics. Are you selling a product for what will end up being unit economic positive over time? And then you de risk things like the TAM of your market. Like are you able to grow not to just 10,000,000 of revenue but to 100 and maybe multiple billions of revenue, and then eventually market leadership? And if you don't de risk those things while you're on the journey, you stop scaling. You just stop growing, and so there's just like this nice clean relationship between those two variables where if you're continuing to scale it usually means you're becoming a less and less risky company. And I think that the thing that's changed massively in the new AI paradigm is that you can have businesses that are well over $1,000,000,000 in revenue that haven't proven out their unit economics, they haven't proven out durable product differentiation.
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.