The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:41:10 · Rory O'Driscoll
[00:41:10] S1: You can infer from data in general that Intropic and OpenAI spend a large amount of money on training data, as do the other three wannabe foundation models, And there's an entire industry that suffers to take care of that. It's an interesting business because you only have five or six large competitors, and they all obviously want to go on and sell to enterprises. But the vast, vast bulk of the dollars come from a relatively small number of enterprises. Yeah, those companies are going very quickly because, you know, the financial model companies want want data. They want it now, and they're willing to spend big to get it. So they've been able to go from zero to hundreds, and in the case of some billions very quickly. And the interesting thing in the foundation model cases, there's clearly an end revenue source that's paying for that. In the case of robotics data, it all makes sense. It's the same analogy. There's a bunch of companies really interesting doing various forms of models for robotics. It's obviously early in this deployment life cycle. So you know, you've got the early burgeoning spend, you've got the 10,000,000 and the $100,000,000 deals, and you can get that on venture dollars from the robotics companies and fund... Yeah, and financial model companies too, because they want be in robotics. I think what's going to scale that to a billion or 2,000,000,000 is when the deployments happen, and that's still unproven in terms of timing. Because I mean, the humanoids, I saw a GXO, which is a logistics company that one of my companies deal with, they sent me a post, one of the guys there did it. It was the summary of the post was 42 POCs of humanoids, zero deployed. And then there was some thread about the BMW relationship with Figr, where, again, we talked a big game, but the humanoids aren't out there. So I do think there was a deployment issue in robotics, which will filter back upstream to a ultimately spend on data business until such time as the unlock happens at the deployment level. But for now, you know, definitely making hay while the sun is definitely shining.
00:42:58 · Harry Stebbings, Rory O'Driscoll
[00:42:58] S2: Are we doing Mecca from our [00:42:59] S1: investment committee? You you have to do the second half of the Sequoia round that the regulatory 20 x step up or whatever it is they deem to be appropriate. So... No.
00:42:59 · Rory O'Driscoll, Jason Lemkin
[00:42:59] S1: investment committee? You you have to do the second half of the Sequoia round that the regulatory 20 x step up or whatever it is they deem to be appropriate. So... No. [00:43:09] S0: Well, it's just... Listen. I'm not smart enough to really know. I do think it's it's an interesting play in the sense that, obviously, the current level of interest in robotics is beyond an all time high. Right? It's it's... And and they all need training data. Right? And you can be skeptical of some of these folks, but the demand for high quality training data, even if it's just paying people to wear sensors and do work, it's just off the charts. I met a founder at YC Demo Day. He wasn't in this class of YC. He was just there that I knew from saster stuff. And he tilted his company into doing just a very niche version of training data. And he did 30,000,000 in sixty days. And he said, the problem is they just don't want to do all the stuff that he's asked to do. He's like, we have 300,000,000 of demand. It just takes so many specialized humans in so many countries to do this niche product. We're turning away two seventy out of 30 because we don't want to do it, right? So if you could nail that two by two of being able to provide the data labeling, the humans and the everything, you have the right one. Maybe I'd rather do this than invest in a bunch of robots that are gonna throw Aunt Bessie out the door or smash their fist through my kitchen when they're trying to grab the pots. I'm certainly skeptical of the residential ones. Maybe this is the safest place to put our money.
00:43:09 · Jason Lemkin, Harry Stebbings
[00:43:09] S0: Well, it's just... Listen. I'm not smart enough to really know. I do think it's it's an interesting play in the sense that, obviously, the current level of interest in robotics is beyond an all time high. Right? It's it's... And and they all need training data. Right? And you can be skeptical of some of these folks, but the demand for high quality training data, even if it's just paying people to wear sensors and do work, it's just off the charts. I met a founder at YC Demo Day. He wasn't in this class of YC. He was just there that I knew from saster stuff. And he tilted his company into doing just a very niche version of training data. And he did 30,000,000 in sixty days. And he said, the problem is they just don't want to do all the stuff that he's asked to do. He's like, we have 300,000,000 of demand. It just takes so many specialized humans in so many countries to do this niche product. We're turning away two seventy out of 30 because we don't want to do it, right? So if you could nail that two by two of being able to provide the data labeling, the humans and the everything, you have the right one. Maybe I'd rather do this than invest in a bunch of robots that are gonna throw Aunt Bessie out the door or smash their fist through my kitchen when they're trying to grab the pots. I'm certainly skeptical of the residential ones. Maybe this is the safest place to put our money. [00:44:17] S2: Oh my god. I would way rather do this than any robotics company.
00:44:17 · Harry Stebbings, Rory O'Driscoll
[00:44:17] S2: Oh my god. I would way rather do this than any robotics company. [00:44:21] S1: But the absurdity of that is you can't... These things aren't going to exist unless the robotics companies are successful. So I disagree. [00:44:27] S2: Well, no. No. No. No. No. No. Because I... I'm saying I don't know which robotics company will be successful. I don't know what form factor. I don't know what vertical, but I believe that robotics will be. So I wanna be in the infrastructure that allows any of [00:44:38] S1: them to be But but pushing back on that, that's like saying I wanna do Scale AI, which is... Or Mercar, which are great companies, instead of Anthropic. If I had a choice, I'd do Anthropic. If you can pick the winner, pick the winner. Right? Because the model companies are, by definition, bigger than the companies that sell to the model companies. [00:44:53] S2: Sorry. I didn't quite understand that analogy. It's it's a bit like saying, I wanna be in scale, not legora, because I don't know who's gonna win the application layer, but I do know that models are gonna need to get better. That's a better comparable, which would be understandable.
00:44:53 · Harry Stebbings, Rory O'Driscoll
[00:44:53] S2: Sorry. I didn't quite understand that analogy. It's it's a bit like saying, I wanna be in scale, not legora, because I don't know who's gonna win the application layer, but I do know that models are gonna need to get better. That's a better comparable, which would be understandable. [00:45:10] S1: Personally What what you're what you're saying implicit in that is you believe there's a statement in that that each robotics company has its own foundation model and the companies like we're in the journalist pie and skilled, like if journalist pie and skilled become the dominant foundation models for robotics, then it is equivalent to my foundation model comment. But if every robotics company has to build its own specific model, then yes, then it becomes a very fragmented market at the model layer. And I don't know, it's definitely one of the areas where I have a fair number of robotics investment and the least amount of clarity. [00:45:43] S2: Okay, boys. There's a lot else. Guide me in terms of where you would like to go. We can talk about Anthropic cutting the price of Haiku by 90% as the price war with OpenAI further intensifies. Jason, I'm I'm relatively intrigued to hear your thoughts on that given how intensely you use them.