01:46:59 · D.A. Wallach, John Coogan, Jordi Hays
[01:46:59] S0: Yeah. You know, it's just... He talks about it in that book. You just go into the grocery store and you see Legg's hosiery is sold out. You buy it. If the container's empty, this thing must be working. [01:47:08] S1: Yeah. Buy it [01:47:09] S0: stock up. Yeah. Know, I... It's probably pretty smart. [01:47:13] S1: Yeah. Still good. [01:47:14] S0: That was how I thought about it. Yeah. And then it was a it was sort of a golden era, as you guys know, of startup formation. So I met Elon really early, invested in SpaceX in 2013 or something like that. And I found Ripple really early. But I also missed some good ones. [01:47:29] S1: Sure. You know? [01:47:30] S0: Of course. If I knew which ones were gonna work, [01:47:32] S2: I only would've done those. [01:47:33] S0: We go. Nailed it. It's Skunk. Skunk was. Great success. [01:47:38] S2: So TBPN. Ultimate Skunk. Yeah. Now you've made it. Great. Now you've arrived. This is the moment. I [01:47:44] S1: was all in service [01:47:45] S2: I've of the story one on one, but you're you know showbiz so you really just perfect delivery. But then how how did you end up... You you you back all these incredible companies and then you're and then you're like I want to go into biotech. Was like... You got to be honest was like was biotech investing like the hottest thing? Was it like the web three or the AI of the time and you were like chasing the hot thing or did was there... [01:48:10] S0: No, I'd say it was decidedly not hot. Yeah. And and... No, but it's
01:48:10 · D.A. Wallach, Jordi Hays, John Coogan
[01:48:10] S0: No, I'd say it was decidedly not hot. Yeah. And and... No, but it's [01:48:13] S2: gone through periods where it it hasn't over the last twenty years, it's gotten... [01:48:18] S0: Oh, goes up and down. [01:48:19] S2: It's very [01:48:19] S0: cyclical industry, very rate sensitive industry. [01:48:22] S1: Yeah. [01:48:23] S0: Yeah. And when I got into it, you know, I I was just kinda trying to get the big directional bet right. You know, I thought if you were starting an adventure in the seventies or the eighties, basically all you had to do right was call computers. [01:48:36] S1: Mhmm. [01:48:36] S0: If you just said computers and then Internet Yep. You probably do okay. [01:48:41] S2: Yeah. [01:48:41] S1: And [01:48:41] S0: so I thought, well, what's going to happen over the rest of my career? And it seemed like maybe life sciences would be the most dramatic area. Yeah. [01:48:50] S1: Now [01:48:51] S2: You were wrong. [01:48:51] S0: Few years into it, it's probably [01:48:52] S2: still computers. [01:48:53] S1: Yeah. Yeah. It's still computers. [01:48:54] S2: And then a few years later, software as a service. And then a few [01:48:58] S0: years later... This is... I mean, look, we... [01:49:01] S2: But now you look like a genius. [01:49:03] S0: It's... Well, don't know. It's it's it's happening, but it's happening slowly. [01:49:07] S1: Yeah. Yeah. What is actually happening?
01:49:07 · John Coogan, D.A. Wallach, Jordi Hays, Keller Cliffton
[01:49:07] S1: Yeah. Yeah. What is actually happening? [01:49:09] S0: Well, what's happening is I think the fulfillment of a lot of promises that people saw fifteen, twenty years ago conceptually, which were like medicine's going to become preventive. Sure. Right? It's not just going to be an industry that's focused on people once they're sick. [01:49:25] S1: Yep. [01:49:25] S0: The addressable market is not just sick people, it's all people. [01:49:28] S1: And GLP-one is probably the best [01:49:30] S0: example GLP-one of is the first blockbuster [01:49:33] S1: Preventative. [01:49:33] S0: Preventive medicine. You could call this maybe the first longevity drug in some regard because it's going to reduce all the other risk factors for the other diseases that get us. And there's going be a lot more of that to come. So I think the industry is increasingly consumer focused. You see that in the strategies, say, of Eli Lilly, which is the leader in the GLPs. Their strategy is they want to have a whole pharmacy shelf of different profile products that fit the needs of different customers. Mhmm. [01:50:03] S2: Yeah. It's interesting like you can imagine a lot of people they go in, you know, they're in a medical setting and they're like, you currently taking any medications? And like if you're under 40, I think a lot of people are hoping, or 50, they're like, no. You know, hopefully the answer is no. Otherwise, you already have like... [01:50:20] S1: Because they're off cycle and they're [01:50:21] S2: not on deep ball... Yeah. [01:50:23] S3: John is... Now I'm I'm Wolverine stink.
01:50:23 · Keller Cliffton, John Coogan, D.A. Wallach, Jordi Hays, Praveer Melwani
[01:50:23] S3: John is... Now I'm I'm Wolverine stink. [01:50:24] S1: Wolverine stink. [01:50:25] S0: Wolverine stink. Stink. Wolverine stink and cigarettes. [01:50:28] S1: Yeah. We're good. Yeah. Get. And a doctor. [01:50:30] S0: It's changing. So basically the not sick are now part of the addressable market of pharma. So that's a big shift. And and then, you know, the the slower but really important work that never ends is trying to chip away at these horrible diseases that kill [01:50:44] S2: all of us. [01:50:44] S1: Of course. [01:50:45] S0: And in the past decade, a number of cancers are now curable diseases. [01:50:50] S1: So [01:50:51] S0: you can see it around the margins, but slowly but surely the industry is making progress in solving these really big problems. [01:50:58] S2: Yeah. So let's let's... You know, we're all thinking about it. AI. Sort of the... [01:51:04] S3: What is that? [01:51:05] S2: This is sort of the elephant in the room. Yeah. I ask you about it. Yeah. No. But but I wanna get right into it which is like people are talking about where the next leg up comes in AI revenues. Simple answer is just like continued diffusion Mhmm. Through a bunch of different businesses and consumers, whatever. But obviously, people think life sciences and biology is gonna be pretty big. See that signaled from the labs, you see that signaled from, you know, Demis and the work [01:51:35] S4: that he's
01:51:35 · Praveer Melwani, Jordi Hays, John Coogan
[01:51:35] S4: that he's [01:51:35] S2: doing. And now I saw for the first time this week somebody like say, global r and d spend is some number of trillions of dollars. Like AI can easily capture 2030% of that. And that was the same exact like logic of like looking at like white collar work which was like, oh [01:51:55] S1: Yeah. [01:51:56] S2: This much spending. So of course AI... Because AI can do some percentage of that. Mhmm. And the question I I keep coming back to that I'm curious to get your thoughts on is, say I am a drug... You know, I'm in the drug discovery business or I'm a pharma company and I come up, like, I can use the models to come up with like a thousand new ideas. Mhmm. There... We are so far, it seems, from being able to simulate the human body perfectly enough that you can like close the loop where it's like, I have a thousand new ideas for drugs and now I'm gonna simulate the trials for them and tomorrow I'll know if we have a a hit drug. Right? I assume that eventually we would be able to like simulate human biology well enough that we don't need to run a five year human trial to figure out if our idea is valid. Right? But like the problem is right now, if if I'm Eli Lilly and I'm using, you know, the latest models, great. I came up with a thousand new ideas. That's like potentially, that's like many many billions of dollars of new money that I need to then go spend on figuring out if they're valid. And so you're back to this idea of like, well, we can only take so many shots, you know, we can only actually run with so many of our ideas. Mhmm.
01:51:56 · Jordi Hays, D.A. Wallach
[01:51:56] S2: This much spending. So of course AI... Because AI can do some percentage of that. Mhmm. And the question I I keep coming back to that I'm curious to get your thoughts on is, say I am a drug... You know, I'm in the drug discovery business or I'm a pharma company and I come up, like, I can use the models to come up with like a thousand new ideas. Mhmm. There... We are so far, it seems, from being able to simulate the human body perfectly enough that you can like close the loop where it's like, I have a thousand new ideas for drugs and now I'm gonna simulate the trials for them and tomorrow I'll know if we have a a hit drug. Right? I assume that eventually we would be able to like simulate human biology well enough that we don't need to run a five year human trial to figure out if our idea is valid. Right? But like the problem is right now, if if I'm Eli Lilly and I'm using, you know, the latest models, great. I came up with a thousand new ideas. That's like potentially, that's like many many billions of dollars of new money that I need to then go spend on figuring out if they're valid. And so you're back to this idea of like, well, we can only take so many shots, you know, we can only actually run with so many of our ideas. Mhmm. [01:53:16] S0: Oh, that's right. I mean, that's that's the bottleneck is Yeah. The only way [01:53:20] S2: to find out if things are safe active is in a human. But like, was the bottleneck ideas? [01:53:25] S0: No. The bottleneck is not ideas. Yeah. The bottleneck is translation.