Transcript

Well, every week we hear of new companies, new AI companies hitting big revenue milestones. Companies can grow from no revenue to $100 billion in annual recurring revenue seemingly overnight. But that doesn't always mean that those companies have enduring businesses. And to talk about that, I want to bring on Jam and Ball, a partner at Altimeter Capital who has done some great writing on exactly this topic. Jam, and welcome back to the show. It's great to see you again. Thanks for having me excited to excited to chat today. Yeah, let's let's nerd out about all these metrics that are, you know, going in all different directions these days. Look, we had all these companies growing quickly. And yet the gist that I got from the post that you wrote was that you are kind of nervous right now. You're, you know, you mentioned that you said, you know, you're a little bit anxious looking at some of these companies. I mean, things are so fun right now. Why is German ball nervous right now? Yeah, look, I mean, I, I think it's important, it's important to call out first, right, that what we are seeing is some of the most historic growth trajectories that we've ever seen. And, and any private software company and it's for a few reasons. It's it's because with a lot of these AI products, whether it's an AI marketing agent and I sales agent and I finance agent, they're able to demonstrate ROI very quickly. You have a buyer who is very interested. In applying AI and efficiencies to their own business and a world that's just moving so fast that you can't risk to wait and stay on the sidelines. You have to go procure these things. Otherwise your competition well and you'll be left behind. Wrap on top of that, these are Greenfield markets. A lot of these AI solutions aren't replacing anything. They're they're net new. Sometimes they may be augmenting or replacing some form of Labor, but it's Greenfield. Now with that being said, you know the, the, the topic of the post. Wrote last week was called easy come easy go. I've, I've maybe written a prior iteration of that, which is kind of AR versus ER kind of the AR being annual recurring revenue, very sticky and ER experimental runway revenue. But yes, I think there's, and we can chat all about it. There's, there's definitely pros and cons to these historic ramps and growth rates. Well, so one of the things you basically talked about is basically, you know, some of the reasons why this initial surge in revenue for. These companies may not be as sticky as other revenue that may have taken, you know, maybe a couple years to generate. And so talk about some of the reasons we talked about implement, implementation time, sales cycles. Yeah. What are some of the biggest concerns that you have or, or, or flags that should be raised? Yeah. And look, I would say it's it's a beauty and a curse. And there's things that are very good, but may also have a negative, right. And there's really kind of like 3 I came up with, right. One was the implementation cycles. Watch them even start before that. The sales cycles are extremely short for a lot of these AI product purchases and that's generally because of the few things I listed earlier. The time to value is very clear and obvious. You have a buyer who's very motivated, but in general you have very fast sale cycles. On top of that, you have very fast implementation cycles. A lot of these products can be PLG and you have a champion that is making a very fast decision. Again, I think it's very easy to hear me say those things and say, well, that sounds. Amazing. Isn't every company trying to lower their sale cycles. It is in every company trying to lower the time it takes to go live on the implementation. And why are those bad things? And, and here's here's what I would say that they're not necessarily bad, but I think they can be taken for granted, right when you have a very fast sales cycle. Well, what does that probably mean? It probably means that the buyer didn't evaluate you against the four or five other solutions. I moved really quick. It might mean that your champion. Really wasn't fully bought in. They made an emotional decision because their boss was telling them we have to move fast and they weren't as bought into the solution. You were just a solution to a immediate hair on fire problem they had right now. And I mean, you mentioned, I mean, in some cases you mentioned, you know, they could have been the only vendor that was evaluated, which, which is crazy to me that that's not the way software is typically worked, right? It, it, it hasn't. But I would say there's never been the pressure from higher ups from execs to move quickly. And adopt AI right and when you have kind of like the pressure coming down from the top when you have an employee base who's excited to adopt A lot of these solutions and tools and automate away a lot of the mundane parts of of their day-to-day, you do have this perfect storm of just moving really quickly. Umm and then there's also generally very fast implementation cycles at the end of the day, you can't go zero to 100 and in a year or two years without, you know some level of fast implementation cycles if it took a long time you. Just probably couldn't grow that quickly, but that also probably means you're not necessarily, well, why are implementation cycles long? Because you have to integrate with a lot of solutions. You have to integrate with a lot of other workflows. You have to get different roles and access controls and all of all of that stuff, which again, it's people want to bring that time down. But the beauty of longer implementations, more integrations, kind of more workflows is it's what I call like the Salesforce effect or like the lasting effect. Like these are products that you don't really find a lot of people. Saying I love Salesforce like I love Atlassian, but they can't RIP them out because they just have their tentacles in 100 different workflows and removing one solution. It actually means rewriting the 100 workflows that touched it. And so that is the beauty of a long implementation cycle is you're going to really push your tentacles into workflows and all different other parts of the org and so absent that. It might be easier to RIP out and replace. And so again, it's kind of like the, the, the beauty and the cursor of shorter implementation cycles and shorter sales cycles. Let me ask you about, you know, what the right metric is here because you talked about AR versus what you sort of call experimental revenue run weight. I thought experimental experimental run rate revenue, revenue, right? So I mean, you've got the revenue metrics, right? The other metric we've heard so much about is, is net dollar retention, of course, which is a way to sort of measure this. Over time and see how many customers are actually sticking around or even upgrading in some cases. The thought that I've had and I'm, I'm asking you this, you know, one of your thoughts here is, I mean, our metrics sort of becoming less important, important for many of these companies, even if they have revenue, you know, are the qualitative factors really around, hey, who is the customer? You know, what is the profile of the customer? Is that really what you have to focus on in this climate is leaning on your qualitative judgment of who these, these companies are selling to? I think, yeah, look, I think that's certainly part of it. I think the other metrics that I will learn a lot more on these days are kind of like gross retention and looking at retention cohorts because that does tell a good story on how these customers are trending. Because again, I'm not saying that all of these customers are kind of like easy come, easy go. There will certainly be a number of companies that, you know, we see grow to be multi billion dollar, 10s of billion dollar large public companies that started off maybe with the profile that could have left easy come easy go. But just continued their liftoff. The execution rate we're seeing of a lot of these companies is faster than ever. But I'll, I'll look at other metrics. But yes, I think it's almost like counterparty risk, it's customer risk. It's hey, let's go talk to that customer. And what you might hear sometimes is, hey, we built, we bought this product because we want to do build this feature. We don't know what the exact ROI of that feature will be, but we have a hypothesis. We think it will drive higher conversions. We think it will drive a higher ASP. We think it will lead to better customer satisfaction, right? But at the end of the day, if it doesn't, maybe they'll just turn it off and stop using it. But they can't afford not to try it. And so they're trying a lot, they're experimenting a lot. And again, a lot of those experiments will be shut off, but also a lot of those experiments will be double, triple, quadruple down on. And we'll see kind of a new crop of historic companies built. Well, I mean, that was a big point that came up in our in our AI agenda live conference this week was, you know, companies saying, hey. The pilots are what they are. Don't stop or start spending based on the pilot. Just, you know, keep keep chugging along and seeing how they go. Jim, and thank you so much for coming on the show and and explaining it to us. It's a great newsletter. Again, he is the author of the Clouded Judgment newsletter that is Gem and Ball from Altimeter Capital.