Biotech investor D.A. Wallach: AI won't cure cancer overnight — China's rising pharma dominance is the bigger story
Nov 6, 2026 · Full transcript · This transcript is auto-generated and may contain errors.
Featuring D.A. Wallach
Speaker 2: Yeah. Great to see you. How you doing, man?
Speaker 3: The man.
Speaker 2: Welcome. Welcome.
Speaker 1: Guys, this feels so good.
Speaker 3: Yeah. I mean, Over here. Ultradome.
Speaker 2: Yeah. Ultradome.
Speaker 1: And we've got a socialist mayor in LA.
Speaker 2: Okay.
Speaker 1: But you guys are waging a capitalist revolution here in Hollywood. I just feel very
Speaker 2: lucky to be here in
Speaker 3: the midst. A capitalist revolution. Yes. No one talks about capitalist... Underdog now.
Speaker 2: So... Are.
Speaker 3: You guys are crushed. You gotta rise up. Rise up. Congratulations on it.
Speaker 2: We're stronghold.
Speaker 7: I'm gonna I'm gonna fight
Speaker 3: back by setting up a street food stand with just steaks. Caviar.
Speaker 2: Or or a private credit stand that's unpermitted and and outside of Dodger Stadium. Yeah. And when someone stumbles out, they can get a hard money loan against their property or something like that.
Speaker 3: Or there's... Finance data center.... Revenue.
Speaker 2: Yeah. Whatever they need to do. Whatever they need to do. Loans. Loans. Loans. Yeah. Finance. Yeah.
Speaker 3: Love it. Love it.
Speaker 1: It's actually finance.
Speaker 2: Finance. I think you guys
Speaker 3: know that. Heard you guys earlier. We try to say it Yeah. Properly.
Speaker 2: The fortress of finance. Yes. Yeah. Introduce yourself for the viewers.
Speaker 1: Well, I'm D. A. Wallach. I'm a big fan of TBPN. Okay. And that's my primary identity. Sure. Sure. But in terms of my First and foremost
Speaker 2: Yes.
Speaker 1: I've had two careers. Yeah. I was a I was a rock musician.
Speaker 2: Yeah.
Speaker 1: That's what brought me to LA. Yeah. And then over the past fifteen years, I've become a professional investor.
Speaker 2: Yeah.
Speaker 1: I run a biotech and healthcare focused firm.
Speaker 2: Yeah.
Speaker 1: And then I I love finance.
Speaker 2: Yeah.
Speaker 1: So we can we can talk
Speaker 2: Do everything.
Speaker 1: Anything. Whatever you guys wanna take...
Speaker 2: Maybe give a little brief overview of the investing milestones, the career. Like, at what point did you know, okay, it's working.
Speaker 3: Also, yeah. How did you how did how did how did you just stumble into... Like, what... At what point did you decide, you know, I'm I'm ready to become an allocator first...
Speaker 1: I'll you some deep history.
Speaker 2: Please.
Speaker 3: Yeah.
Speaker 1: So... And I'm gonna tie this into a recent guest here, Jimmy Iovine. Yeah? Yeah. So I was a college student.
Speaker 2: Yeah.
Speaker 1: I got a call on my cell phone from Kanye West.
Speaker 2: Okay.
Speaker 1: And he said, come out to LA this weekend. Okay. And I landed here and we we were... We spent the weekend not far from this studio, the Ultra Dome at the record plant. I you guys work out near this. Yeah. That's right. Right across from the record plant, there was a glory hole facility called The Zone.
Speaker 2: Okay.
Speaker 3: Didn't go there. I didn't
Speaker 2: go there.
Speaker 3: I went
Speaker 2: to the
Speaker 4: record plant. Okay. Good good and
Speaker 1: then we ultimately got signed by Jimmy Mhmm. To Interscope Records...
Speaker 3: Was Kanye, like, just told...
Speaker 1: We we ended up in a bidding war. Mhmm. My first experience of real finance. Here we go. Yeah. A bidding war between Kanye and Pharrell. And Pharrell had a partnership with Jimmy Iovine...
Speaker 3: You were
Speaker 1: a college kid at
Speaker 6: this point? I was a
Speaker 1: college kid. Was I was senior.
Speaker 3: You hadn't technically dropped out yet?
Speaker 1: No. I never dropped out. Wow. And we got so lucky with the timing because...
Speaker 3: Scott's fired it.
Speaker 2: Were you studying music, bio, No.
Speaker 1: My my degrees are in African American studies. Okay. And I have a minor in Kikuyu Okay. Which is a language spoke in in Central Kenya.
Speaker 2: Okay.
Speaker 1: It's a Bantu language.
Speaker 2: Okay.
Speaker 1: It's not not too distant from Swahili.
Speaker 2: Got it. Okay.
Speaker 3: And... Do you still know how to say one beer, please?
Speaker 1: I don't know how to say that. Can say,
Speaker 2: you know, Okay. Okay.
Speaker 1: Okay. These are
Speaker 2: some Yeah.
Speaker 3: The things I These say
Speaker 1: if I met a woman in in a strange place.
Speaker 2: Got it.
Speaker 1: So I'm out here. Mhmm. We get this record deal.
Speaker 5: Mhmm.
Speaker 1: And as a as a young artist...
Speaker 3: What does the first record deal look like when you're a college kid and you're in a bidding war?
Speaker 1: Yeah. For Allah. First off, you got to lawyer up.
Speaker 2: Mhmm.
Speaker 1: Yeah. First rule of business. Right?
Speaker 2: Yeah. Course.
Speaker 1: So we lawyered up.
Speaker 2: Mhmm. We had a
Speaker 1: great lawyer called Owen Sloan, Frank Zappa's old lawyer. Oh. Elton John's old lawyer. Wow.
Speaker 3: There you go.
Speaker 1: And we said, Owen, we need you to talk to these guys. Need you to get...
Speaker 3: Kind of sound like an industry plan.
Speaker 2: Yeah. Just rolled
Speaker 3: it out. Got the top lawyer. I'm getting a car.
Speaker 1: It was a soft landing. And what does the deal look like? Well, the record industry was in a transitional moment at that time. What what people had realized was you were decreasingly making money off of the music itself. Mhmm. All the income was coming from touring, merchandise, endorsement deals, that kind of thing. Mhmm. And so what the record label started to do was so called three sixty
Speaker 2: Three sixty deals. I remember these.
Speaker 1: We got one of the last non the whole fight with the label was to sign a non three deal
Speaker 2: Got it.
Speaker 1: At the moment that they were trying to default everyone into that. Yep. So we signed this deal.
Speaker 3: And and Yeah. The the complaint around the $3.60 deal is that an artist effectively ends up as like an indentured servant.
Speaker 1: You're just giving the man more of what you make. Right?...
Speaker 2: The album but then you go and do a movie and they take a look at that or something.
Speaker 3: And like there's literally no way to make money without the big man getting...
Speaker 2: Getting his cut.
Speaker 1: At that point. Yeah. Yeah. Because it's $3.60.
Speaker 2: I mean,
Speaker 1: they slice get of everything.
Speaker 2: Yep.
Speaker 1: So anyways, it's not unlike being a little company with a big venture fund in you. As an artist, you get signed to one of these big conglomerate record labels, and now you're one of a 100 artists. Yep. And you're vying for their attention
Speaker 5: Yep.
Speaker 1: Their investment, their promotional muscle. And so one of the only sources of leverage I had with Jimmy was I said, I can introduce you to Zuckerberg. Mhmm. Because I had gone to school with him. So I was like user two thousand and five Woah. On Facebook. I mean, I was literally on Facebook the night it came out. Yeah. My first investment lesson was failing to invest in Facebook.
Speaker 3: Of And,
Speaker 1: you know, I mean, in retrospect, I was so stupid. I was going and having burritos with Mark
Speaker 2: Yeah.
Speaker 1: And all I cared about was getting his permission to spam people to promote my band. You know,
Speaker 2: much... Instead of some Yeah. Yeah.
Speaker 1: So so anyways, I bring him up to Silicon Valley to meet with Zuckerberg. Mhmm. And in that meeting, I learn about Spotify. Wow. One of Mark's right hand guys at the time, Dave Morin
Speaker 2: Yeah.
Speaker 1: Also a Silicon Valley...
Speaker 2: Been on the show.
Speaker 1: Dave Morin tells me about Spotify and he says, I can get you an account. And at the time, it was only available in Sweden because it hadn't been legalized in the other markets.
Speaker 2: Yep.
Speaker 1: And I started using this product and I was just like, this... I know enough about the music industry, this is gonna work.
Speaker 2: Yeah.
Speaker 1: Yeah. And so that was my first investment. Mhmm. And then I became the artist in residence at Spotify. Wow.
Speaker 4: I very... Investment is crazy. You're like
Speaker 3: I should have invested in I should have invested in Facebook.
Speaker 2: Missed it.
Speaker 3: Said I was focused on spamming, but I got Spotify.
Speaker 5: It... It's
Speaker 1: really good. You know, Better Lucky than Smart.
Speaker 3: Yeah.
Speaker 1: And and then I thought, wow, investing's really easy. Mhmm. Because it's just like, you know, I'd read a Peter Lynch book.
Speaker 2: Yeah. One Up on Wall Street or something. Yeah.
Speaker 1: 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.
Speaker 2: Yeah. Buy it
Speaker 1: stock up. Yeah. Know, I... It's probably pretty smart.
Speaker 2: Yeah. Still good.
Speaker 1: 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.
Speaker 2: Sure. You know?
Speaker 1: Of course. If I knew which ones were gonna work,
Speaker 3: I only would've done those.
Speaker 1: We go. Nailed it. It's Skunk. Skunk was. Great success.
Speaker 3: So TBPN. Ultimate Skunk. Yeah. Now you've made it. Great. Now you've arrived. This is the moment. I
Speaker 2: was all in service
Speaker 3: 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...
Speaker 1: No, I'd say it was decidedly not hot. Yeah. And and... No, but it's
Speaker 3: gone through periods where it it hasn't over the last twenty years, it's gotten...
Speaker 1: Oh, goes up and down.
Speaker 3: It's very
Speaker 1: cyclical industry, very rate sensitive industry.
Speaker 2: Yeah.
Speaker 1: 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.
Speaker 2: Mhmm.
Speaker 1: If you just said computers and then Internet Yep. You probably do okay.
Speaker 3: Yeah.
Speaker 2: And
Speaker 1: 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.
Speaker 2: Now
Speaker 3: You were wrong.
Speaker 1: Few years into it, it's probably
Speaker 3: still computers.
Speaker 2: Yeah. Yeah. It's still computers.
Speaker 3: And then a few years later, software as a service. And then a few
Speaker 1: years later... This is... I mean, look, we...
Speaker 3: But now you look like a genius.
Speaker 1: It's... Well, don't know. It's it's it's happening, but it's happening slowly.
Speaker 2: Yeah. Yeah. What is actually happening?
Speaker 1: 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.
Speaker 2: Yep.
Speaker 1: The addressable market is not just sick people, it's all people.
Speaker 2: And GLP-one is probably the best
Speaker 1: example GLP-one of is the first blockbuster
Speaker 2: Preventative.
Speaker 1: 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.
Speaker 3: 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...
Speaker 2: Because they're off cycle and they're
Speaker 3: not on deep ball... Yeah.
Speaker 4: John is... Now I'm I'm Wolverine stink.
Speaker 2: Wolverine stink.
Speaker 1: Wolverine stink. Stink. Wolverine stink and cigarettes.
Speaker 2: Yeah. We're good. Yeah. Get. And a doctor.
Speaker 1: 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
Speaker 3: all of us.
Speaker 2: Of course.
Speaker 1: And in the past decade, a number of cancers are now curable diseases.
Speaker 2: So
Speaker 1: you can see it around the margins, but slowly but surely the industry is making progress in solving these really big problems.
Speaker 3: Yeah. So let's let's... You know, we're all thinking about it. AI. Sort of the...
Speaker 4: What is that?
Speaker 3: 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, Dennis and the work
Speaker 5: that he's
Speaker 3: 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
Speaker 2: Yeah.
Speaker 3: 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.
Speaker 1: Oh, that's right. I mean, that's that's the bottleneck is Yeah. The only way
Speaker 3: to find out if things are safe active is in a human. But like, was the bottleneck ideas?
Speaker 1: No. The bottleneck is not ideas. Yeah. The bottleneck is translation.
Speaker 3: 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...
Speaker 1: 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.
Speaker 2: 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.
Speaker 3: Or they were using it for this other thing. Realized it was actually
Speaker 2: having
Speaker 1: 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.
Speaker 2: Oh,
Speaker 1: yeah. Right? I mean, the story in big pharma has been all the new drugs come from M and A.
Speaker 2: Yep. Totally.
Speaker 1: But the GLPs are the biggest and They they were like a twenty year hard won project inside of big pharma.
Speaker 3: It came from the Gila monster. That's right. Exactly.
Speaker 1: 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
Speaker 2: Speed up data analysis, speed up capture of notes from the trial.
Speaker 1: 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.
Speaker 2: Yeah, it's
Speaker 1: 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?
Speaker 2: Yeah.
Speaker 1: Is it going to solve aging overnight? Yeah. Those are sales pitches.
Speaker 2: How are you set up to invest? Do you you trade in the public markets? Do you have a fund? Do you have restrictions? Like where where do you see opportunity because the GLP one trade again didn't really happen in the private markets. Of course, were a lot of private market beneficiaries. But how are you investing structurally?
Speaker 1: We do both. So we have a private fund and then we've got a hedge fund.
Speaker 2: Okay.
Speaker 1: And you know, the the... Where the risk return is more attractive Yeah. Changes over time.
Speaker 2: I can imagine.
Speaker 1: Again, this industry is very sensitive to rates.
Speaker 2: Yeah.
Speaker 1: So when you've... You know, when you're in a secularly rising rate environment in Yep. General it's tough for biotech. Yep. You think about it...
Speaker 2: Because the earnings are falling So we
Speaker 3: need to go back to ZERP for humanity.
Speaker 1: We need to go back to ZERP. For humanity. The bottom line. And I'm buying all the long dated treasuries I can but it's not moving the...
Speaker 4: Yeah. I feel like
Speaker 2: a Fed chair who came out and was like, we need to lower rates to save lives because we're going to cure cancer if rates are low. That's a really effective message.
Speaker 1: It's a good pitch. I think it's better than like, oh, need AI to add 2% real GDP growth a year, which to me is tall order.
Speaker 2: Was I was... Tall order.
Speaker 3: What's going on in the like biology and pharma data markets? Like is there is there That's a good question. Is there old companies that were dead and long... Effectively long gone that are being like, you know, that are suddenly worth something because they they had a bunch of, you know, they did a bunch of trials on something and that data is valuable and then I have a follow-up question.
Speaker 1: Okay. So when DNA sequencing first kind of hit the scene several years ago, there was this first wave of data is valuable as a market story. And I would say...
Speaker 3: It's new oil.
Speaker 1: Yeah. It's like the new... If we do a start up and all we do is we generate a boatload of data, maybe some big pharma's going to come and buy us for that data because they're going to mine new drugs out of it. There's really, to my knowledge, only one or two instances of that working. Amgen bought something called Decode, which is really interesting. Decode was a sequencing project for basically everyone in Iceland. Oh,
Speaker 2: I've heard of this, yeah.
Speaker 1: Interesting, quite genetically homogeneous population. Sure. That was good in some ways in terms of what insights you could derive from
Speaker 2: it. Yep.
Speaker 1: I believe it was from the decode data set that they actually figured out the mechanism for one anti cholesterol drug that's been very Wow. That still has benefits that will be harvested for years to come. Yeah. Now, the new thing that's happening is the AI labs are prospective buyers of biological data sets. And there's varying levels of sophistication inside of those companies. So Anthropic, a couple weeks ago, announced a number of partnerships with biotech companies. They're all trying to sell to the big pharmas in various ways. And as with all these questions of data sovereignty and so forth, the pharmas are a little bit in a difficult place.
Speaker 3: Apprehensive.
Speaker 1: They want to be innovative, they want to tell the street, they're doing everything on AI, but then they are a little guarded about their data. Mhmm. So I think it's going to be very interesting to see both how the big players decide to treat their own proprietary information and also how much the labs are willing to spend. Because, right, they've exhausted the Internet, they're paying Merkor and whoever else to go and generate expert style But this biological data, which really is intrinsically big data, I mean, you know, one experiment might create petabytes worth of information. You know, it it they think is an interesting place for them
Speaker 3: to try and suck up... How how is... How how do pharma executives view... Well, like what is their general view on the rhetoric within the tech industry around, you know, I think like this year everyone in AI was like, we really should cure cancer to help our popularity.
Speaker 1: When they're smoking cigs now.
Speaker 3: Yeah. Yeah. No sunscreen, all this stuff. Like what is the sort of pharma view on the rhetoric within tech? Do they laugh at it? Because they're sitting there being like, we've been curing cancer and helping... We're hated. And we're just hated. Yeah.
Speaker 1: Look, I think there's a mix of naivete and sophistication among the AI folks. So when they hear AI people say smart things, they go, cool. Yeah, let's run at
Speaker 2: it.
Speaker 1: Sure. Everyone respects Demis.
Speaker 2: Yeah,
Speaker 1: yeah. But when Demis says we're going cure all diseases in ten years, think people who have two PhDs and have been doing this for forty years ago, come on, man. One, you're giving a sort of false hope
Speaker 2: Sure.
Speaker 1: To a lot of people who are legitimately out there suffering or have a loved one who's on death's doorstep.
Speaker 2: Yeah.
Speaker 1: Like, that's a little messed up. Yeah. But on the other hand, they they also just think like it's not...
Speaker 2: Well, might be long tobacco So maybe he's pushing...
Speaker 1: I mean, I think it's an open question whether
Speaker 2: it's okay to just sort of
Speaker 1: sheep's clothing hedge fund manager. Probably.
Speaker 2: This this is a very
Speaker 1: long game that he's been playing.
Speaker 2: Exactly. Occam's razor. Occam's four d razor.
Speaker 1: I think it's legitimacy.
Speaker 3: What about robotics in a in a lab setting in an era like Kevin like Kevin Wheel has his his new company trying to sort of reassure stuff on the the R and D side. But but like that's been happening. Like people have said like we should automate labs.
Speaker 2: Every time someone picks a bottleneck goes and builds a business around it, the biotech community is like that actually wasn't the bottleneck. Like...
Speaker 3: Well, no no. But Kevin's Kevin's like strategy I think is more... It's honestly like a like a national security Totally.
Speaker 2: Think it's super valid. I I just think the biotech community is fantastic at getting something from the tech community and be like, yes, that is what that was a bottleneck but we have actually Right. 12 more bottlenecks for you
Speaker 6: to work Okay.
Speaker 1: I mean, this gives us a chance to talk a little bit about China.
Speaker 2: Please. You guys love China. Huge fans. We have Gong over there for China.
Speaker 3: Thank you.
Speaker 1: So the the... In in a way, a bigger a bigger shift than AI for the biopharma industry is the rise of China.
Speaker 2: Oh, yeah.
Speaker 1: This is real. This is already material. This is already changing the whole industry. So what's happened is over the past, let's call it five to ten years, China has both invested tremendously in early stage development, like discovery work. So this is the idea generation part. And then preclinical biotech outsourced labor. So before you take a drug into a clinical trial, you've got to run it through this whole battery of tests. You're testing it in mice, maybe non human primates, you're doing a lot of laboratory work. You're trying to characterize this thing as well as you can to minimize the risk to people. And then in The US, you take it to the FDA and you ask for permission to go and put it in people. In China, what they have tried to do is all of that work at a quarter of the price. And because the big pharma companies are multinationals, increasingly they've been comfortable doing that work in China. And just like any outsourcing, if it's faster and cheaper, why not send it there? Now it also turns out the quality of the work is outstanding. Because guess who's doing it? Lot of Chinese scientists who got their PhDs in The United States and then we kicked out because we're idiots.
Speaker 2: Yeah. Sure.
Speaker 1: So we've got all these... That's what I'm talking about.
Speaker 2: It's crazy. Mean, that's
Speaker 1: what it sounded like, right? Yeah. That's what it sounded like. So Stephen Miller out here kicking ass Yeah. Sure. Our new our new chief of staff. Congratulations. By the way, hear Susie Wiles is gonna be joining TBPN as a nice week. That's huge
Speaker 3: for you guys. Don't even know what's
Speaker 1: going on. China is ascendant And now Yeah. The lifeblood of big pharma, which is buying new drugs from little biotech companies Yeah. Now they are buying double digit percentages of their new drugs from China. That's what that's what I said when I heard this.
Speaker 2: Yeah.
Speaker 1: And it's it's a normal... Buy American. Yeah. Yeah. Course.
Speaker 4: The new balance.
Speaker 1: Of course. You know, buy American, make American, buy American.
Speaker 2: Yeah. So
Speaker 1: this is this is a big story and and like as with a lot of outsourcing, the potential to bring...
Speaker 3: Here's here's like Got all it. Where we we potentially... So automation is one thing. The other thing is like so much general excitement around AI Mhmm. Is gonna push it... Has already pushed a huge amount of capital Mhmm. Into bets that traditional biotech investors would be like, I've never... Right. I would never... I could never underwrite that.
Speaker 2: Right? Sure.
Speaker 3: Sure. Because all the... Because like we're dealing with like, you know, the singularity and super intelligence and curing all disease within ten years. When you just have sort of like big ideas about things and they're not like based around, you know, decades of investing in the category. And so I would say that that's like prop... Maybe the way that America saves itself by having a bunch of new investors that aren't like experts just flood the space with capital. Because even if we get if we get...
Speaker 2: It flows the right way.
Speaker 3: Yeah. We we overbuild or we get, you know, we we a bunch of a ton of zeros as always where we get four or five new companies that become like, you know, flagship, you know, public public companies that that solve some of these issues. Is that like...
Speaker 1: I think that'd be great. I will say the Chinese approach to AI generally and to biotech seems very different and I must admit I think it's more intelligent.
Speaker 2: Okay.
Speaker 1: Which is, you know, writ large, China's strategy is make AI free, cheap and good so that every business person in China can use it and out compete whoever else around the world wants to give a run at something. Interesting. Here Yeah. We're in a way, we're throwing crazy venture money at these like millennium problems.
Speaker 2: Right?
Speaker 1: Yeah. Insane like, oh, this would change everything. Now, I agree. If any of them works, it's worth it.
Speaker 2: Yeah.
Speaker 1: But it's kind of odd to me that we're not focusing AI on the low skilled labor first. Mhmm. I mean, call centers, everyone, that's the first example. Of course, it makes sense. Like, why wouldn't AI replace all the call centers? But part of the challenge with biotech is it's like, okay, this is supposed to replace white collar labor. So let's start with the most educated, most sophisticated form of human capital that exists. Yeah. Like big pharma, and I'll be the first to say, they're big companies. They're bloated, they're inefficient, they're bureaucratic, all of that.
Speaker 2: Yeah.
Speaker 1: They employ more PhDs Yeah. Than any companies in the world.
Speaker 2: Yeah. Yeah.
Speaker 1: Yeah. Okay? And they're they're not idiots. Yeah. So it's like the Silicon Valley is going to come and show the gray beards how it's done.
Speaker 2: It's sort of silly.
Speaker 1: It's it's going to fall.
Speaker 2: Do you think of the the electronic lab notebook, notebook like the dev tools play in Benchling. Benchling. I I was... A lot of people were pitching like, there'll be like a cursor for bio. And I was a little skeptical in the short term of that because Versus Code is open source and so Cursor was able to fork it. That doesn't really exist in the... I don't know what you're doing. Was just...
Speaker 3: Added a little Christmas lighting.
Speaker 2: Yeah. Oh, perfect. Perfect.
Speaker 1: That's nice.
Speaker 2: But but but there there isn't like an open source software stack for bio that I'm aware of. But at the same time, software development is so cheap now. Maybe you could bootstrap that quickly. Like is there is there a play for software for bio that you're excited by? Or is it a sign of just like a sideshow? Because it feels like an answer to your question of like how can you make things more efficient without needing to go after the no... You're not trying to replace the Nobel Prize winning PhD, you're just trying to make the average lab technician more efficient.
Speaker 1: Right. Look, I think in principle it's the right kind of thing. It's like go after the prosaic stuff. Yeah. So for example, big pharma's already seen a lot of yield increase when it comes to manufacturing process. Put cameras over the whole manufacturing line, you can do quality assurance much faster, that
Speaker 2: kind
Speaker 5: of thing.
Speaker 1: What you're talking about is the very early stages. Academics or researchers working in the lab.
Speaker 2: Historically,
Speaker 1: I'd say the bounty has been quite small for software and biotech.
Speaker 2: Yeah, yeah, yeah. They're really... IT spend is low relative to...
Speaker 1: It's low and it's all open source, right? Because it's
Speaker 2: Okay.
Speaker 1: It's it's a pipeline to analyze genetic Yeah.
Speaker 2: It might just be like a iPython notebook.
Speaker 1: It's a lot of notebook. Jupyter notebooks and stuff. Jupyter notebooks. It all comes out of some lab at MIT, some guy needs to do an experiment, he writes the code for it.
Speaker 2: Yeah. A little library for this.
Speaker 1: And I think there's also that kind of ethos, which isn't unlike software development where people just, you know, they're trying to cure disease. Yeah. There's kind of a bias against being proprietary.
Speaker 2: SaaS guy.
Speaker 1: Yeah. Like being SaaS guy.
Speaker 2: You're saying the industry needs a Mark
Speaker 1: We... Every industry needs
Speaker 3: a Mark Benioff.
Speaker 1: We need a dream force of of biotech. We need Tony Robbins. Need Metallica.
Speaker 2: On stage.
Speaker 1: We need all of
Speaker 2: it.
Speaker 1: Love it. For Tony.
Speaker 3: Ice... On the regulation on the regulation side Yeah. If you were running the show, which I'm sure you could get a job an admin. Don't know how long you'd last.
Speaker 2: Any any admin.
Speaker 5: Yeah. I'd lucky to...
Speaker 3: But what would you do? What would you do?
Speaker 1: If I were the regular, it's a good question.
Speaker 3: Bailouts for my whole portfolio.
Speaker 1: Listen. I
Speaker 3: think... We need to bail out my early stage book from my 2015 vintage.
Speaker 1: The the the United States FDA plays a really important function globally Globally. Yeah. Because everyone trusts us to be hard asses about drug safety and...
Speaker 2: And you'll see they just draft off
Speaker 1: of us. You know, go to a pharmacy in India. And they've got great scientists in India, but go into a pharmacy in India and roll the dice on whether the thing you're buying is what you think it is, whether the postal... All that. So the FDA I think does a really nice job keeping patients safe, making sure we trust the medicines we're getting. It's not perfect. The debate from the sort of tech libertarian wing, this goes back to Milton Friedman. Milton Friedman's view was the FDA should only clear drugs on safety and let the consumer decide whether efficacy is there. Right now, for the FDA to approve a drug, they've got to believe that the company has proven it is both safe and effective. And more effective typically than placebo. And then for the government to pay for that drug, they often want to know it's more effective than the drug it's competing with or replacing. So why spend more on something that doesn't work as well? So you know, we're in a place right now where this is being challenged by consumer health, wellness, peptides, and there are a lot of people out there buying peptides from compounding pharmacies that may not be safe, that may not be effective, probably aren't. Yeah.
Speaker 4: You know
Speaker 3: that horrible batch of DBAL.
Speaker 1: Yeah. What's been going on with you? How's rash?
Speaker 3: That's the old... No, know, but it's the OG.... Kind of sketchy pharmacy.
Speaker 1: We were short before you started these peptides.
Speaker 2: Yeah. In the leg lengthening surgery. There's been a couple things that have stacked up.
Speaker 1: So I wouldn't go that far. I wouldn't just let it free because the bottom line is this stuff is really complicated. Not unlike finance. Yeah. In finance everyone's trying to
Speaker 3: save you're saying almost peptides are like the web three of biology.
Speaker 1: Peptides are the hyper liquid of biotech. Right? And and it's like, I like that maybe, you know, maybe it's a good thing Sure. That perps on cal sheet
Speaker 2: Yeah.
Speaker 1: Can allow us to bet on whether you're going to uncross your legs in ten seconds.
Speaker 4: Mhmm.
Speaker 1: I know people watching at home would like to... Boom. There we go. I knew it.
Speaker 2: Just hit it big.
Speaker 1: So, you know, people can send me the cut Yeah. My PayPal. Yeah. We'll put... You'll put that up on the For screen. It's like, you know, I think it's I think it's kind of dangerous, right? And we have an FCC for a reason. Like we... Yeah. It is paternalistic.
Speaker 2: Yeah.
Speaker 1: The FDA is paternalistic. The conceit of it is Yeah. This stuff is complicated Yeah. And we don't want people to get swindled by snake oil salesmen.
Speaker 2: Yeah.
Speaker 1: And you know, peptides are, in many cases, snake oil.
Speaker 2: Yep. Interesting.
Speaker 3: So what's your stack?
Speaker 1: The Wolverines. We'll start with a couple Diet Cokes every morning,
Speaker 3: couple of fridge cigs and take it from there. Take it from there. Yeah.
Speaker 2: Thank you so
Speaker 3: much for coming on the
Speaker 2: show.
Speaker 3: This was amazing. I'm so glad we get to go to lunch right now to continue to continue.
Speaker 1: I'll be back next week and every week thereafter.
Speaker 3: You're you're always you're literally welcome any day.
Speaker 2: It was a real pleasure. Was fantastic.
Speaker 3: This was fantastic.
Speaker 2: Yeah. Let's close out the show. Leave us five stars on Apple Podcast and Spotify. Sign up for newsletter, tbpn.com and we will see you on Monday at the last...
Speaker 3: Have the best weekend of your life. Good job.