The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:36:21 · Rory O'Driscoll, Harry Stebbings, Jason Lemkin
[00:36:21] S1: you wanna come back to [00:36:22] S2: the investment committee? We can do a deep mind. Damas the legend that is Damas Esarbus, raised at about $10,000,000,000, give or take, from Thrive a matter of months ago. Now Isomorphic Labs, his company, in talks to raise it between 40 to $50,000,000,000 price. We can do this? [00:36:41] S0: I mean, generational talent has generational prices today. As much as I believe in the JEV teams and the TypeSafe teams, he's gonna pull together a team like never been seen since the dawn of mankind in artificial intelligence. Maybe he fails. Maybe he's had enough of it. Maybe he's enjoyed life in London too much, too many posh clubs. But I think I've seen the fire. It is a scary bet. We have our liquidation preference, and no one will be able to assemble a set of talent like this. This is the next and last chance to cherry pick the best of the best of the best, and this is the job.
00:37:10 · Rory O'Driscoll
[00:37:10] S1: The interesting thing about this company will be that unlike the vast bulk of the revenues that come from the foundation models, where it's, you know, intelligence automation, where the value capture is replaced labor. Here, the value capture is not replaced labor. It's actually make science discoveries. And the way because you know, when you discover it, when you do a molecule, if you automate the 500,000,000 of science spend that you pay science wages to, that's not really going to cut it. What you really want to do is, know, when you create the $200,000,000,000 drug, you want to get 10% of that on a royalty basis. So it's kind of the as the models devour all the knowledge worker dollars that are available to them in terms of direct replacement, at some point, they're going to have to monetize via creating whole new things with their intelligence. And, you know, it's a lot more credible today just given the math, the recent work they did on math, which as one mathematician correctly pointed out, don't actually get paid a whole ton for solving Stokes, Navia, whatever it is. But if you can discover the next GLP drug, that's a winner, because those things do money. So it will be interesting to see I have in the past said it's hard to get the drug companies to extract meaningful royalties when they still got to do phase one, phase two, phase three. And many of these companies end up being drug companies down the line. It'll be interesting to see what happens here, but it's a big pile of money if you can extract the value, not just from a labor percentage.
00:38:34 · Jason Lemkin, Harry Stebbings
[00:38:34] S0: No, no, I think we're all going to be on 10 GLP-one like drugs in a few years. And I think, you know, we never really got the full story on Anthropix wet lab. I think the potential here is even greater than is let on. I think the potential for drug discovery is probably massive. And we're going from weirdos microdosing to finding out we're all going to have a set of drugs that dramatically extend longevity and quality of life. And they can radically accelerate it, own the IP, and have other people do the hard work, maybe AI really can reboot the entire industry fairly rapidly. Of course, not all of it, not the trials, not the rest, but man, if you could get a GLP-one out five years faster. [00:39:11] S2: Jason, do you actually buy that? I don't mean that cynically. [00:39:14] S0: The drug stuff? Yeah. Yeah. Only very recently did I buy it. I thought it was wackos and Brian Johnson's and people selling crap. [00:39:21] S2: What caused you to change your mindset?
00:39:21 · Harry Stebbings, Jason Lemkin, Rory O'Driscoll
[00:39:21] S2: What caused you to change your mindset? [00:39:22] S0: Because the the medical evidence for GLP ones is so... It's so profound. It's so for heart disease, for so many other things. And I think our ability to tailor drugs to extend longevity is the massive market. It's better than inference. You know, and if you talk to any just physicians at the cutting edge now, they're all saying to anybody like your hip or whatever, just don't hurt the other one. Like, just get there. Like in another couple of years, the amount of longevity we're gonna able to achieve is so much higher that I just think the wackos of the microdosers of SF and Brian Johnson made me skeptical, but just the overwhelming evidence of the benefits that almost everyone should be on the GLP-one, right? And the fact that we're going to get better and better at figuring all this stuff out. I mean, we're just walking sets of cancer and heart disease and other things that having material impacts will radically extend longevity, radically extend longevity. [00:40:14] S1: There's a whole sequence of steps before you even have adequately good molecule identification with some understanding of how it works, and then you still have all the different biophases. So as I say, I do believe it'll be harder to capture value than you think, but I'm really glad they're trying. Guys, I wanna talk
00:40:14 · Rory O'Driscoll, Harry Stebbings
[00:40:14] S1: There's a whole sequence of steps before you even have adequately good molecule identification with some understanding of how it works, and then you still have all the different biophases. So as I say, I do believe it'll be harder to capture value than you think, but I'm really glad they're trying. Guys, I wanna talk [00:40:29] S2: about one which may be a little bit earlier than usual, but we have Mecca, which is the MACRA or the surge for robotics data, physical world data that can be implemented in most often robotics. Raises 60,000,000 series b from Sequoia. The reason I wanted to bring it up is that, you know, we have micro AGI in Europe, which has posted race to a 100,000,000 very quickly. There's Macca, which is well over 100,000,000 in ARR. There's another provider that I'm forgetting the name of, XOFT or XO Soft or whatever it is, that's again over 100. And we just see the sheer volume of data providers over 100,000,000. What should we read, infer, analyze from this?
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.