The Metrics Brothers
00:19:55 · Ray Rike, Dave Kellogg
[00:19:55] S1: Well, they did something very good on page 38. On the X axis it says 0.25¢, 1,000,000 ARR. They told us the starting point. [00:20:03] S0: Good point. Cause I was giving them a dig for switching to quarters from years or months, but they did something highly commendable, which is they told you the starting point, which nobody ever does. They just go months to 100,000,000 and like from when? From when the company was founded, from when they sold their first license, or from when they had their first trial. So good for them. There's a nice chart there. We've talked about the crazy growth rates of AI. On slide 40, they talk about some what I call hockey stick companies, which companies were growth decelerated and then took off. They talk about Palantir, which is obviously a super hot and well followed company right now. My favorite example is Bolt, which I think had existed for I don't know, five years, and then they hit the hockey stick and went in less than a year. I think I can't remember the exact number, but so it wasn't from inception because it would have been like sixty or eighty months. But from hockey stick, they did it in like twelve months from effectively, you know, 1 to 100,000,000 in ARR. [00:21:05] S1: Hey Dave, I can't believe it. We've only got a couple minutes left. What else do you want to cover here?
00:21:11 · Dave Kellogg
[00:21:11] S0: Yeah, I think we could wrap this section up. I want to do a couple other comments. They do some comparisons between AI native and non AI companies. And as Ray pointed out at the beginning, we're a little worried about their definition of non AI because they're saying that 60% of the companies have no AI in them and that's just so not consistent with anything we read or see. So you should view these comparisons with two grains of salt. One, that we're a little worried about how they define that. And two, they don't actually show any material differences and maybe this is why. I don't see a lot going, Wow, when I look at slide 43. They do an interesting idea, which is looking at how headcount is distributed. But once again, I don't see any major wow. Maybe the major wow might have to do with the fact that there's something wrong in the taxonomy. Not sure. So they looked at funnel. They, Oh, the last thing we've got to talk about is here. They talked about forward deployed engineers, and this was great. So they looked for how many people were hiring them. And forward deployed engineers is, in short, a super hot hiring trend. And at first glance, it appears to be the same thing as a new name for post sales consulting, but it's not. And we may have to do an episode on this, Ray, to talk about what's a forward deployed engineer versus what's a normal post sales consultant. And think I, and I'm pretty cynical or skeptical, think there is one. But this is just data. Was saying in February 24, the number of vacancies were 30 for FTEs, forward deployed engineers, and now it's up to three seventy five. So this trend has caught fire. Thoughts on this one, Ray?
00:22:51 · Ray Rike
[00:22:51] S1: No, just on the same chart, they say that the forward deployed engineers should be able to realize at least a five to 10X return on that investment. Number one, I'd really love to know how they measure that because to me, the reason you do forward deployed engineers is to make sure that you get them up and running quickly, that you get the maximum adoption, and you have the most revenue over twelve, twenty four months. So I just want to know how they're going to measure that.
00:23:18 · Dave Kellogg, Ray Rike
[00:23:18] S0: Got it. So I think we'll wrap up, Ray. They do a slide on burn multiple, which we won't talk about again. The short answer is it looks like it's around two is the right answer. The only interesting thing on the chart is they say that for AI native companies, it does drop a lot above $100,000,000 which it never really did for some of the non AI companies in this sample. It was pretty consistent between below and above. So that was interesting. The headcount split doesn't make that big a difference. So I'll wrap up by saying, look, they have what they call the Iconiq Enterprise Five, which I think are five great metrics. And they show them split in significant detail here. And those metrics are ARR growth, net dollar retention, rule of 40, net magic number, and ARR per FTE. And if I were on an executive team of a startup, I would definitely be looking at these five metrics and the report then goes through and shows you a pretty detailed segmentation for each of them. So this is just useful benchmark data in my opinion. And then the report racks up. Any overall thoughts on this, Ray, or thoughts on the tail of the report? [00:24:28] S1: No, I would just say, you know, there's enterprise five starting on page 52 are really good and I like it. What's good, I love what Iconiq does, but at the same time, every time I read the report, I guess I have such high expectations. I want even more from them. So, but good on them for doing it.
00:24:28 · Ray Rike, Dave Kellogg
[00:24:28] S1: No, I would just say, you know, there's enterprise five starting on page 52 are really good and I like it. What's good, I love what Iconiq does, but at the same time, every time I read the report, I guess I have such high expectations. I want even more from them. So, but good on them for doing it. [00:24:43] S0: Yeah. And I think, look, the only thing I'd say on this is I love their research too. I think they are by far the most rigorous of the kind of VCs who publish research. My net is always, this is like VCs wanting the best thing for their children. Always setting a really high bar, right? So we've got a very tough set of companies that we're comparing to. And then for example, on slide 52, what are we looking at? Are we looking at the median? No, we're looking at the top quartile of the kids who got into Harvard, not just the median kid that got into Harvard. And that's always the thing you need to bear in mind with Iconiq research is I do think you're going to feel bad when you look at it and you need to look at it and go, wait a minute. First, if you're trying to go to Harvard, you're trying to go public, this is the bar you got to hit. Very useful for that. But if you have a different aspiration for your company, this research can be depressing because you're looking at literally the top of the top when you look at these numbers. [00:25:44] S1: Hey Brother Dave, thank you for this one. It was another good and fun one.
00:25:44 · Ray Rike, Dave Kellogg
[00:25:44] S1: Hey Brother Dave, thank you for this one. It was another good and fun one. [00:25:47] S0: Thanks Ray. The Metrics Brothers AI, SaaS, and more is a production of The Metrics Brothers Growth and CAC and a member of the Bench Market Podcast Network. By accessing this podcast, you acknowledge that The Metrics Brothers make no warranty, guarantee, or representation as the accuracy or sufficiency of the information provided or the humor content of the jokes told. Information opinions and recommendations presented are according to our spouses probably wrong and provided for information purposes only. This podcast should not be considered professional or for that matter unprofessional advice. We disclaim any and all liability for any direct, indirect, indirect, misdirect, incidental, special, ordinary, consequential, inconsequential, or other damages arising out of any use of or god help you reliance upon the information presented here. Ray Rike is based in New York City and available on x at Ray Rike. Dave Kellogg is based in Silicon Valley and available on x at Kellogg. Schenectady, which is French for unspellable, is not our actual production location. Thanks for the listing.