This Week in Startups
00:16:55 · S0, D.A. Wallach
[00:16:55] S0: It's gotta be double SaaS. [00:16:56] S2: So is is it gonna start by eating their SaaS budget and then eat eat more, or is it gonna be incremental? I mean, I think, like, you know, you have this other major uncertainty here, which is that everyone involved here is doing the rational thing. So Anthropic is doing the rational thing to preorder all that compute, even though it might put him in a tough place. Every corporate CEO is doing the rational thing to do a bunch of pilots and spend a bunch on tokens and figure out what this can do. The question is gonna be, are these rational actors who are both ramping up their AI activities going to be doing so at at rates that lead them to meet at the same level? Right? I mean, ultimately, what matters here [00:17:41] S0: is balance the demand. Supply and demand. I got it. [00:17:43] S2: Yeah. And and right now, everybody says, oh, that we can't meet the the demand. The demand's too high, but then I go again. Well, they're only spending point 1%. The the presumption in that comment is typically if the price were lower, they'd be spending more. In other words, when you say this is this is supply constrained, what it's suggesting is that the supply constraint is leading
00:17:43 · D.A. Wallach, S0
[00:17:43] S2: Yeah. And and right now, everybody says, oh, that we can't meet the the demand. The demand's too high, but then I go again. Well, they're only spending point 1%. The the presumption in that comment is typically if the price were lower, they'd be spending more. In other words, when you say this is this is supply constrained, what it's suggesting is that the supply constraint is leading [00:18:06] S0: to price. I think it's meat. It's it's a meat sack constrained D. A. Is the people, the meat sacks at organizations are not able to deploy it. It's too heady for the average employee. The meat sacks are blocking this. And then if you look at a company like, say, Uber, which adopts the technology pretty fast, they burned all their tokens in the first quarter famously, and then we're like, oh, what do we do now? They just put all of their employees, I think it was 30,000 on OpenAI at once. So I think that this is the key issue. When are the meat sacks able, which organizations will put 100% of meat sacks on this? And will they be able to not just be the meat bridge, the compiler between the output and the input of the AI, and actually orchestrate it? When could they be sophisticated enough to orchestrate this towards a business result? And I think Microsoft and their 60,000 person buyout, one in four employees that are being offered a buyout, is a roadmap. I think another organization like them, they see it, the writing's on the wall. Dave, you've been wanting... Oh, go ahead, Lon and then Dave.
00:18:06 · S0, Lon Harris
[00:18:06] S0: to price. I think it's meat. It's it's a meat sack constrained D. A. Is the people, the meat sacks at organizations are not able to deploy it. It's too heady for the average employee. The meat sacks are blocking this. And then if you look at a company like, say, Uber, which adopts the technology pretty fast, they burned all their tokens in the first quarter famously, and then we're like, oh, what do we do now? They just put all of their employees, I think it was 30,000 on OpenAI at once. So I think that this is the key issue. When are the meat sacks able, which organizations will put 100% of meat sacks on this? And will they be able to not just be the meat bridge, the compiler between the output and the input of the AI, and actually orchestrate it? When could they be sophisticated enough to orchestrate this towards a business result? And I think Microsoft and their 60,000 person buyout, one in four employees that are being offered a buyout, is a roadmap. I think another organization like them, they see it, the writing's on the wall. Dave, you've been wanting... Oh, go ahead, Lon and then Dave. [00:19:12] S1: Oh, I was just going say, on This Week in AI this week, Andy Berman of Run Layer addressed this exactly. That's what he was saying, it's just the companies have yet to attach the AI to the data and the systems that would actually cause it to become more valuable. And so that's... We're just waiting for them to sort of make that switch. [00:19:29] S0: And the why? Why are they not doing that?
00:19:29 · S0, Lon Harris, Dave McClure
[00:19:29] S0: And the why? Why are they not doing that? [00:19:31] S1: Exactly the reason that you said that it's just like people being hesitant or not understanding your security concerns or the meat proxy. [00:19:38] S0: Cyber concerns. Yes. Think it's both. The meat sack doesn't know what to do. They're like, what do I do? I already summarized this article. How many more... I don't have any more articles to summarize. Like, but that's not what this technology is for. That was like the demo four years ago. And cyber has made everybody in these big corporations play on their heels. Go ahead, Dave. [00:19:58] S3: I think we're going to get there. I mean, this is early innings. I liken this to teenagers in high school talking about sex. They they they wanna talk about, like, everybody's doing it, but not everybody's doing it. And eventually, they do get there. So, like, as we gotta move up the stack, I think you're gonna see AI productivity gains hit not just tech companies, but normal companies. We're still in the early stages and, you know, a lot of people talk about AI, but most people are not really using, you know, the full potential of AI. Most people are still using it like, you know, Google prompts. A few people are starting to use it, you know, for agents, but we're we're pretty early on.
00:20:36 · S0
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00:22:47 · Dave McClure, Ben Black, S0
[00:22:47] S3: What if they started? [00:22:49] S4: This is the Yeah. That's my point. Number that... The one number that I just couldn't get over was the 900% net revenue retention. [00:22:56] S0: Well, like, unpack that and explain it, Ben, for somebody listening. What does it mean to have 900% retention? [00:23:03] S4: Basically, they took the same customer cohort from a from a three month period a year ago and looked at how much they were spending, that was their spending, and then looked at... And basically said that same three months this year same customers three for the three month 909 times more. [00:23:21] S3: Nine times