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.
01:53:25 · D.A. Wallach, Jordi Hays, John Coogan
[01:53:25] S0: No. The bottleneck is not ideas. Yeah. The bottleneck is translation. [01:53:28] S2: But but like the potentially exciting thing is that as you see like things like Navier Stokes fall, maybe there's just things that humans have been missing and like we'll get a bunch more new ideas. And again, we're not bottlenecked on ideas, but maybe we'll just get some like better ideas. Is that... [01:53:46] S0: Well, we almost certainly will get better ideas. And as with all the other AI opportunities, it's both going to come from the AI generating ideas and from the AI giving leverage to really smart scientists who were trying to come up with ideas before but now are going to be supercharged in their effort to do that. I think the sort of challenge right now, even before AI, was that we have a super abundance of good ideas for new drugs. And you can go look at any university in America or now in China and Europe. There's great science everywhere. [01:54:20] S1: It happens every time a new blockbuster drug comes out. I do the research on where it came from. It's like, well this guy invented it in the eighties and then it was used for some really small thing in the nineties and then they finally got it to scale in the twenty ten's. [01:54:33] S2: Or they were using it for this other thing. Realized it was actually [01:54:36] S1: having [01:54:36] S0: Yeah. Another The GLPs are one of those stories. Yeah. And it's kind of an indictment of the startup ecosystem, the biotech world. That didn't come from a startup. [01:54:46] S1: Oh,
01:54:46 · John Coogan, D.A. Wallach, Jordi Hays
[01:54:46] S1: Oh, [01:54:46] S0: yeah. Right? I mean, the story in big pharma has been all the new drugs come from M and A. [01:54:50] S1: Yep. Totally. [01:54:51] S0: But the GLPs are the biggest and They they were like a twenty year hard won project inside of big pharma. [01:54:59] S2: It came from the Gila monster. That's right. Exactly. [01:55:05] S0: I mean, look, there are a ton of good ideas out The challenge is how do you prove whether they're safe and effective in humans, which is, to your point, it's very time consuming, it's very costly. And AI, from the standpoint of a big pharma company, is going to chip away in incremental ways at a million different things [01:55:27] S1: Speed up data analysis, speed up capture of notes from the trial. [01:55:32] S0: It's everything. FDA filings, It's legal going to be incremental and you're going to add up a lot of tiny efficiencies to accumulate and it's going to look more like, oh, maybe a drug's development time goes from twelve years to eleven years and then maybe to ten years. Now, the average blockbuster drug is selling, I think, 3,000,000,000 a year. So more or less every day you can accelerate its time to market is worth millions of dollars Sure. For a sponsor. [01:56:03] S1: Yeah, it's [01:56:03] S0: valuable. So anything you can do with AI is going to be a great thing for the industry. But is it going to cure cancer overnight? [01:56:11] S1: Yeah. [01:56:11] S0: Is it going to solve aging overnight? Yeah. Those are sales pitches.