The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:23:34 · Jason Lemkin, Rory O'Driscoll, Harry Stebbings
[00:23:34] S0: That's the thing. It's easy to shut off perplexity for Amazon. It's just... They're just a gnat. Amazon will have interesting choices when it's everybody. Like, you shut off everybody. It is technically possible. But at some point, it's gonna be complicated. Are we sure we wanna shut off everybody? Moving swiftly on. Anything else that I haven't touched with Muse? [00:23:52] S1: No. Because it's not really the story of the week. I mean, it's the story of last week. We're gonna do the story of the week, and Jason's gonna be ready as a hands on users to tell us about Jev. Right? That's the story of the week. How do we go? [00:24:03] S0: We can talk about JEV if you want. [00:24:05] S1: It's next one up, and it's the story. [00:24:06] S2: It's the next one up, and fine is Rory's story of the week. So for those that don't know, chat GBT and banter ships JEV. It's a model that decides instead of chats, and it becomes Vercel's fastest ever launch. Jason, I'm sure you've played with it. What did you think? Jason's review. New new segment. [00:24:24] S0: Well, first of all, listen. I'm not I I maybe... I might not be smart enough to get into semantics. I don't even think it's a model. I think it's a classifier backed by an LLM. I've used it. It's awesome. But I don't know that it'll be as disruptive as everyone on x talking about it that probably never used it said it is. But it is awesome. Okay? Jason, what do you [00:24:41] S2: use it for?
00:24:41 · Harry Stebbings, Jason Lemkin
[00:24:41] S2: use it for? [00:24:42] S0: What I use it for is deciding who in the SaaS community should meet each other. It's quite good at that. Rory is the CRO at GCI. Harry is the CRO at Perplexity. Should they meet? It's actually not that simple a question. They're at pretty different stages. They live in different places. Should they meet? I spent all of Saturday nothing worked on on Jeb because I had to redo the way I did prompts. Okay, so I thought it was a failure, right? Then what I learned is you've invested the time. It can answer that question in milliseconds for a hundredth the price of anthropic or judge, actually even less. It can just answer a question and it can answer a subset of questions. But I think as we've learned from data labeling and others, there's a lot of classification that needs to be done, a lot of data labeling that needs to be done, but it doesn't output text and it can't do anything particularly complicated or with reasoning. So it's like a big deal. And for this particular use case, I'm talking about who should meet who. It is disruptive, but it's maybe like 20% of all the LLM calls associated with this application. Right? So I love it. I think everyone will do some version of this. I think, you know, maybe even XA and Parallel should do some version. Maybe OpenAnch, everyone should do a version of this, but it's not going take over 100% of your stack and it's not going to replace ChatGPT because it just doesn't do those things. But it does remind us that man, we effing waste a lot of tokens on simple stuff that Astra shouldn't be doing.
00:26:07 · Rory O'Driscoll, Jason Lemkin
[00:26:07] S1: I pretty much a 100% agree with you. I mean, the zoom out comment is this. It's a new model. It's different than an LLM. An LLM returns text and is very computationally intensive, and in computing terms, slow and expensive. This just returns true or false or a ranking or a score, right? So there's three answer types, and it's super fast and super cheap. They don't even charge for output tokens because they're so meaninglessly few. So it's just a faster, more precise. That's why it's called... They refer to it in turn on the website as a system one, which is the Daniel Kahneman thinking fast, thinking slow. And this is the thinking fast part, quick, fast answers. But Jason's math is exactly right. But we're talking about this on Monday, the part of meeting is that there's roughly a $100,000,000,000 today being spent on LLM calls between Anthropic OpenAI and the open source models. Maybe that goes if we just agreed that OpenAI is gonna be doing 350, Amazon, 250. So it's probably gonna go to $500,000,000,000 five years from now. 20%. And, Jason, we had exactly the same number. Only 20% of them are relevant to this, where it's... Today, you're using a complex expensive model for something that really needs a much simpler solution. But 20% of a $100,000,000,000 is 20,000,000,000. [00:27:21] S0: Well, I don't think I don't think the dollar is gonna flow that way. I think the tokens will... Don't think the
00:27:21 · Jason Lemkin, Rory O'Driscoll
[00:27:21] S0: Well, I don't think I don't think the dollar is gonna flow that way. I think the tokens will... Don't think the [00:27:24] S1: dollar Let me finish, damn you. The next thing... Because you you got... You sound like the cynics of my group on Monday. We had this... I've already had this discussion once. That's why I can carry it off. So that's 20,000,000,000, but you're exactly right. As one of my partners said, but, dude, you just said the prices are going to down by five x. So that 20,000,000,000 is going to become 4,000,000,000. You're exactly right. It's just like open source. It's gonna take a slug of the total addressable LLM marketplace, attack it with a better, cheaper product at one fifth the price, net result at 20,000,000,000 becomes 4,000,000,000. But if you're a JEV or if you're a TypeSave, you're saying, hey, that 4,000,000,000 becomes mine. And that's the bet. Now, at the same time, going back to your naming concept, which I love, Jaya from Foundation did a nice post at Biggs. This is just a slug of the total 100,000,000,000 spend that was kind of automatically destined to go to OpenAI and Anthropic and is now kind of sluicing off into a cheaper low cost provider. That's what's happening here. You're right. It's not the end of foundation model. The clever shit is still gonna be done using foundation models. It's just a little maiming of 10, 20% of the revenue that's now gonna be done by someone else at one fifth the cost. Now we can talk about competition in a second because I think that's a real issue, but that's what's going on here.
00:27:24 · Rory O'Driscoll, Jason Lemkin
[00:27:24] S1: dollar Let me finish, damn you. The next thing... Because you you got... You sound like the cynics of my group on Monday. We had this... I've already had this discussion once. That's why I can carry it off. So that's 20,000,000,000, but you're exactly right. As one of my partners said, but, dude, you just said the prices are going to down by five x. So that 20,000,000,000 is going to become 4,000,000,000. You're exactly right. It's just like open source. It's gonna take a slug of the total addressable LLM marketplace, attack it with a better, cheaper product at one fifth the price, net result at 20,000,000,000 becomes 4,000,000,000. But if you're a JEV or if you're a TypeSave, you're saying, hey, that 4,000,000,000 becomes mine. And that's the bet. Now, at the same time, going back to your naming concept, which I love, Jaya from Foundation did a nice post at Biggs. This is just a slug of the total 100,000,000,000 spend that was kind of automatically destined to go to OpenAI and Anthropic and is now kind of sluicing off into a cheaper low cost provider. That's what's happening here. You're right. It's not the end of foundation model. The clever shit is still gonna be done using foundation models. It's just a little maiming of 10, 20% of the revenue that's now gonna be done by someone else at one fifth the cost. Now we can talk about competition in a second because I think that's a real issue, but that's what's going on here. [00:28:35] S0: I just think it's a hundredth the cost, but I agree with all
00:28:35 · Jason Lemkin, Rory O'Driscoll, Harry Stebbings
[00:28:35] S0: I just think it's a hundredth the cost, but I agree with all [00:28:37] S1: of You're right. A 100... Which which... In which case... Yeah. [00:28:40] S2: We're gonna see, like, the unbundling of ChatGPT, and we're have users go, oh, well, this one [00:28:44] S0: would be No. Good [00:28:45] S1: The the the problem is not not ChatGPT, the app, but the OpenAI API and the Entropic API. You're right. This is a developer product. You as an end user, I as an end user, don't use it. You can go on and try and use it, but it's full this morning, I tried. But it's really, I mean, if you listen to the launch announcement, it's very focused on developers. And the idea is, and this is an interesting thing, almost comes back to Muse, that I think you might see, I could be wrong on this, that the needs of humans, especially consumers for AI, are gonna continue to diverge from the needs from software developers for AI. And this is a core software developer thing. You'll never need it, Harry. But someone who's building a software app might realize that a significant portion of the core intelligence they want is system one intelligence. Just tell me if this is an a or a b, you know, animal, mineral, or vegetable. Is eggs better than y? Give me a quick answer. I don't want to blather and have you tell me that's a great question, Rory, like a sycophantic LLM. Just give me the damn answer. So it's not for you, the user, but it's unbundling at the developer level where it's more likely to happen. [00:29:50] S2: You're right. I require far more intellectual answers.