Top Line ARR vs. Churn in Enterprise AI

Jason, from businesses that are using its models. Here's the quote. >> Interesting. >> We've heard very loud and clear from businesses that this is important. They often have their own commitments that they have made to their customers. If you think about somebody who is serving other enterprises who are trusting them with incredibly sensitive data, that's Aaliyah House, OpenAI's head of product policy. >> We all know they're growing like weeds. What we actually need to know is what their churn is. Who's using it less, >> who is hitting the brakes and then why. And that data is not coming out and it's not even coming out in the IPO. They're never going to share the churn data. When companies become very robust, they kind of get enough institutional holdings that a company like Netflix or Verizon has to start reporting on churn. I think these guys are going to be able to Anthropics of the world, you know, um, Gros of the world, uh, Open AIS, Anthropics, they're not going to need to give us how many people are unsubscribing or how those trends are going for the major accounts, right? >> That's where I want to know the truth. The second place I want to know is how much does it cost to serve this stuff up? >> Yeah. Well, that's, >> you know, that funny Italian guy who was on the show with the bad teeth, laughing hysterically, >> laughing hysterically. Yeah, the meme. I saw you shared this meme. Yeah. [clears throat] >> And I shared this meme. You can pull it up. >> I'll I'll tell you. >> It's literally the guy doing the bit and he's laughing about he's paying, you know, 20 bucks a month or 200. He's paying 200 bucks a month for anthropics pro tier 2,400 a year. And you can just play it in the background here. Uh >> yeah, I'm pulling it up right now. >> It is. Here he is. I run Clark all day. Agents, the sub agents, the works. Okay. Uh end of the month, I check the meter. Check the meter. He slams a hand on the ding. $8,000 of [laughter] the audio is he's he's like d he's almost like expiring from laughing so hard. >> I'm so crazy 7800. [laughter] >> Sorry. It's just that's how he's screeching. >> Yeah. He's like >> and then he goes on and on. Yeah. >> I followed the money bottom and tropic. And then he goes out, I spent 71% cents of compute per dollar, $40 per dollar on me. And and then he goes back to like Microsoft and Nvidia. Microsoft. Yes. All all the way up. All of them paying one another. Yeah. >> That's when this whole thing is going to have to face the music, right? >> We're getting close to the face the music moment. I think because they know they have to face the music and explain profitability, they'll have a reasonable story. >> But there's a gap here between how much, you know, people are using and how much they're spending. >> Yeah. >> And then there's some people token maxing. >> But I believe people now are watching the meter. >> People have started watching the meter. So this is exactly what I saw up close and personal with Uber in the price wars between Lyft and Uber and then there were other competitors like Saigar and then Door Dash, GrubHub, Postmates, they were, you know, talking about this incredible growth. They were moving to every city. But then we knew internally, hey, um, across these companies, we're losing $5 a ride, >> right? >> And there was two ways to understand. We're paying drivers a $500 bonus if they hit a hundred rides a week. So if they'll do 100 rides, we'll give them 500 bucks, an extra $5 per ride. But that wasn't in the riding data. It was like marketing bonus. But then Lyft was giving $600. And then the drivers are smart. They're like, "Oh, you guys are idiots. I'm going to next week I'm doing Lift. The week after I'm doing Uber, I hit my incentive. I flip to the next person. Okay, yeah, yolo. I'll door dash." >> If it's possible to figure out a system with these gig apps, people are going to work the system for sure. One person figures it out and it's on Reddit and now everybody's working the system. Exactly. >> Bingo. So what happens is over time there's this famous Dear Drabosa moment and we should play it in a future episode whoever this week startups archivist is. >> Um where I said to her, Dearra, you asked me the question, let me answer it. And she's great and she's now doing her own uh spin-off and uh going to be a media entrepreneur. So congratulations to her. [sighs and gasps] She um is like but there are money losing. They're money losing. And I said, "Okay, let's look at the quarter. There was a billion rides. Incredible. They lost two billion. That's 1 billion rides divided into two billion is $2 per ride. It was like $6 per ride or $7 per ride that everybody was losing." Yeah. You pay seven bucks for a ride. That was like this should be $30 in a cab or, you know, $15 in a cab, whatever it was. They were losing that money to kill their competitors, build their base, attract investors, and essentially do marketing. Instead of giving the money to the network TV shows, >> the concept was, well, why don't we just give the money to the customers in the form of a discount, have them become addicted to this, right? >> And then we can slowly raise the price up to what it actually needs to be. We'll lose 20% of the people who will only use this service because of the discount. We'll keep the other 80%. >> Game over. Right? So venture capital funded it and instead of giving it to >> radio, TV, cable, magazines, newspaper ads, >> the internal discussion was yeah, just pass this, just give it directly to the customer. That's what's happening right now with these enterprise products. That's going to unwind. >> Yeah. I mean, when it does with Uber, that makes a lot of sense. Like we all did get addicted and got used to like if you go out night out, you don't want to drive drunk home, just uh you use Uber. all it got drilled into everybody's brains and now it's there permanently with AI compute though there are these alternatives models there are other things you can do >> highly competitive >> so it doesn't feel exactly the same like they're not anthropic not killing all of its competitors and embedding in like you got to use claw like I'm perfectly happy with grockbot if it works better you know like I don't think there >> and you will be perfectly h perfectly okay in all likelihood when Zuck comes out with something similar to rockbot the easy to use assistant that just abstracts everything. Let alone when new competitors come out and you can go on Amazon Web Services, Google Cloud and they will provision a bot >> and a harness and there'll be an open source harness like WordPress. It's just going to all come apart and it's going to drive >> the the margin out of this. Logs are an