00:00:47 · CJ Gustafson, Brian Neider
[00:00:47] S1: Is this thing on? Yesterday's price is not today's price. Brian Neider is a managing director at Lead Edge Capital and the author of one of my favorite pieces of financial writing, The Hierarchy of Metrics Bullshit. The premise is pretty simple. If your company generates cash profits, you'll probably tell your investors about your cash profits. If it doesn't, you'll find something else to brag about. And if you work your way far enough down the hierarchy, you eventually arrive at things like page views, downloads, and being named the third best place to work in Cincinnati. Brian has spent his career sorting through these numbers and figuring out which ones are actually worth paying for. We talked about the difference between a good company and a good investment, some particularly egregious EBITDA adjustments, and how AI is changing what makes a software business defensible. Where are the moats? And also the time Brian showed up to pitch an investment with a giant Happy Gilmore style check after being explicitly told not to bring a term sheet. I'm CJ Gustafson, and this is Run the Numbers. Brian, thank you so much for joining me on the podcast. [00:01:59] S0: My pleasure. Thanks for having me.
00:01:59 · Brian Neider, CJ Gustafson
[00:01:59] S0: My pleasure. Thanks for having me. [00:02:01] S1: I've been a fan of Lead Edge for a while, and before I ever interacted with your awesome colleagues in person, how I was actually introduced to you was, I don't wanna call it a meme because I know it was a memo, but like to me as a nerd, a financer, and it became a meme. And it's this like hierarchy of metric bullshit where it starts with, hey, on the top floor, you got cash profits, right? And then it works its way down on the metrics that people will disclose, then you get to revenue, then you get to GMV, and then at the bottom, like if you don't have anything to brag about, suddenly you're saying, hey, we're the seventeenth best place to work in Cincinnati. I gotta ask you, what was the origin of that?
00:02:39 · Brian Neider
[00:02:39] S0: I'll pat myself in the back because I came up with it and every so... Goes... Yeah. I I did. I wrote that whole thing myself. But every so often, like, goes viral on, you know, Twitter, Instagram, or something, and and each time I get, a ton of emails about it. Hey. This this thing came back around. We were sort of just getting off the ground as a firm. We were looking at a lot of different companies, and we noticed that there was a lot of sort of trickery in the numbers. A lot of the CFOs didn't wanna fully share everything about their business. There was some lumpiness that we would see in things that didn't add up. And it was just really more or less an opportunity for us to contextualize what we were seeing in the marketplace. And there's caveats to that infographic, I think. Certainly, there's earlier stage businesses to which, you know, being the best place to work in a particular locale, like, that's that's what they've got. And it's like that and the product and the vision, that's it. But a lot of the business we were looking at were later stage in terms of, you know, where they were supposed to be. And you're sort of, like, trying to match up the time frame by which a particular company was founded and has grown relative to kinda like the metrics that they're spouting out. And if you have a company that's ten years old and has $50,000,000 of revenue, but their key thing is that they're the best place to work in whatever city, and they can't talk to you even about their path to profitability.
00:04:02 · CJ Gustafson, Brian Neider
[00:04:02] S1: It's awesome. And I've often felt this as myself as an operator just trying to figure out which metrics I wanna share and which metrics are gonna be good characters in my story versus bad. But you can totally tell when you hear somebody pontificating on Twitter or or on LinkedIn about their metrics, and instead of talking about revenue or even, like, further down, instead of talking about monthly active users, instead of talking about subscribers, they're talking about downloads. So I think it just hit home for a lot of people. [00:04:29] S0: It goes back twenty years, by the way. You had all of this stuff around eyeballs, and that was like the key metric at the time, and it's completely changed. [00:04:38] S1: What do you think is the equivalent of page views today? [00:04:41] S0: I think in the AI world today, the page view sort of equivalent is probably this token maxing stuff that you might have heard about last year, which is all of these companies are being implored to figure out how are they gonna use AI? How are they gonna use it to be better on the cost side? How are they gonna embed AI into their products? And we're seeing a lot of companies who say, you know, we used x number of tokens. This is a great thing. And, actually, in some ways, it's sort of a a crazy metric because it really just sort of, like, induces spend without looking at efficiency. And for some period of time, that might be okay. But overall, we sort of think that that's a a metric that probably isn't exactly the right one to be pounding your chest about.
00:04:41 · Brian Neider, CJ Gustafson
[00:04:41] S0: I think in the AI world today, the page view sort of equivalent is probably this token maxing stuff that you might have heard about last year, which is all of these companies are being implored to figure out how are they gonna use AI? How are they gonna use it to be better on the cost side? How are they gonna embed AI into their products? And we're seeing a lot of companies who say, you know, we used x number of tokens. This is a great thing. And, actually, in some ways, it's sort of a a crazy metric because it really just sort of, like, induces spend without looking at efficiency. And for some period of time, that might be okay. But overall, we sort of think that that's a a metric that probably isn't exactly the right one to be pounding your chest about. [00:05:23] S1: Oh, man. I'm totally on the corner of this as a CFO. It's like, oh, we're bragging about activity for activity's sake. [00:05:28] S0: Right. Exactly. [00:05:29] S1: You wrote that if a CEO starts getting cagey about your questions, you're probably on the right track. And I have to admit, like as a human, when somebody starts to be a bit defensive, you also sometimes will will will kind of pull back. The right thing to do, I think you're indicating here, is actually to lean forward. What are CEOs most commonly cagey about today?
00:05:49 · Brian Neider
[00:05:49] S0: For a lot of the businesses that have been around for a long time, and we invest in software businesses and tech enabled service business, Internet businesses, they need to figure out how far in front they are from an AI standpoint and understand how AI can have some type of an impact on their business. And we see a lot of businesses that have operated for a really long time. They might be a really good business historically, but for whatever reason, they kind of wanna bury their head in the sand because dealing with the AI sort of threat and opportunity, by the way, ends up being a pretty difficult thing for them to do. And so a lot of times companies that we see that are slightly older in nature or have been around for a long time, the most cagey things that I see from them are the resistance to want to talk about how AI is impacting their business. So it's a little bit less so on kind of like the numbers. There's definitely that aspect, and I'll get to that in a second. It's about not wanting to talk about the elephant in the room, and I think that that's that's a difficult one. For a lot of the AI first businesses, so businesses that are already sort of using AI or they have AI related products, it's a totally different story. And that one, I would pin more on kind of like the numbers. So they'll have a super strong vision on where the world's headed and how they're gonna have impact in this, you know, brave new world. Oftentimes, we'll find that these companies are very late stage in terms of revenue, so they're more than willing to share their revenue. They're more than willing to share number of tokens that they're that they're using. But their unit economics are totally upside down, and so you have situations where you have really large cost structures, reliance on other LLMs to to run the businesses in the first place, and we see a lot of, like, normalization of the gross margins. So you'll have situations where costs are set aside almost like on a pro form a basis with the assumption in the future there might be, like, more use of open source models or costs from the existing LLMs coming down and the token cost decreasing, and everything's gonna be fine in the future. All that might be true, but oftentimes when you start poking around, the CEOs get a little bit cagey.