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Speaker 1: Hey, they're aud Loots listeners. It is that time of

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Speaker 1: the year again. We are going to be doing a

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Speaker 1: call in show on the podcast. You can ask us

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Speaker 1: any of your burning questions.

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Speaker 2: That's right. You want to ask us about finance, markets

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Speaker 2: and economics, go for it. You want to ask us

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Speaker 2: about the year in podcasting, go for it. You want

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Speaker 2: to ask about where Tracy is with growing chicken or

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Speaker 2: raising chicken, growing chickens, raising chickens on her versioning firm

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Speaker 2: in Connecticut, go for it. This is your chance to

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Speaker 2: ask us anything.

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Speaker 3: Joe's favorite cut of steak.

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Speaker 2: Right, my favorite cut of steak, my favorite Chinese restaurant

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Speaker 2: in the East Village. It's all fair game, all right.

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Speaker 1: All you have to do is send a voice memo

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Speaker 1: with your question, your name, your age, and your location

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Speaker 1: to od Loots at Bloomberg dot net.

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Speaker 2: Deadline to submit is December seventeenth, So get the men,

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Speaker 2: assume as you can.

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Speaker 1: We're looking forward to hearing what you have to ask

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Speaker 1: and yeah, that's coming up.

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Speaker 4: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 1: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 3: I'm Tracy Alloway.

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Speaker 2: And I'm Joe Why isn't they Joe.

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Speaker 3: We were doing a Q and A this morning.

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Speaker 2: That's right.

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Speaker 3: It's a lot of fun go on a live Q

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Speaker 3: and A.

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Speaker 1: And someone asked a question about whether or not we're

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Speaker 1: going to do more healthcare episodes, and no, we don't,

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Speaker 1: and there's a reason for that. I personally am incredibly

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Speaker 1: intimidated by the US healthcare system. I do not understand

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Speaker 1: it at all. It is just a complete mystery to me.

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Speaker 1: But I was very happy to say in response to

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Speaker 1: that question that this same day, that's right, we're actually

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Speaker 1: recording a healthcare episode with someone that we've wanted to

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Speaker 1: speak to for a long time.

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Speaker 2: I'm the same way in the sense that, first of all, yes,

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Speaker 2: I'm the same way in the sense that I really

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Speaker 2: do not know much about how the healthcare system works.

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Speaker 2: I don't even know where to begin asking the right questions.

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Speaker 2: There is athing you do. You have to just start

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Speaker 2: a random episode, and that gives you the germs of

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Speaker 2: the next question, the next episode, the next episode. But

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Speaker 2: they're so it seems so big and sprawling, etc. That

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Speaker 2: what is the first question to ask? So we just

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Speaker 2: have to plunge right in and just pick one which

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Speaker 2: we're doing now, and then maybe that will lead to

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Speaker 2: the string of healthcare episodes, which we should have done

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Speaker 2: a long time.

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Speaker 1: Again, that's exactly right. There's also a lot of new

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Speaker 1: stuff happening in healthcare at the moment, and we recorded

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Speaker 1: an episode on Chinese biotechs a little while ago that

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Speaker 1: was incredibly fascinating. Definitely, I'm very curious to see what's

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Speaker 1: going on on the US side of biotech and nesting,

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Speaker 1: and we do have another episode plan that's sort of

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Speaker 1: tangentially related to that. But clearly there's a lot to

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Speaker 1: talk about. The other very odd lotsy thing with this

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Speaker 1: guest is we like people who have interest in career histories, right.

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Speaker 2: That's right. How how they got to where they are

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Speaker 2: today is often a very interesting question. You know, I

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Speaker 2: have nothing against people who just took the normal path,

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Speaker 2: people who just sort of, you know, went to college

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Speaker 2: and they got their NBA.

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Speaker 3: And okay, you forgive them, I forgive them.

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Speaker 2: That's totally fine. But it's also interesting to hear about

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Speaker 2: the people who maybe walked in through the side door,

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Speaker 2: so to speak.

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Speaker 1: Absolutely so, we do, in fact have the perfect guest.

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Speaker 1: We have someone who has a lot of thoughts on

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Speaker 1: US healthcare and who is also a biotech investor and

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Speaker 1: also formerly the lead singer of Chester French. So Da Wallack,

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Speaker 1: Welcome to the show. Thanks so much for coming on.

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Speaker 5: Thanks for having me, guys. I'm I'm a odd lots junkie.

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Speaker 5: So this is like going to the Grammys.

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Speaker 2: Amazing, Thank you ever Grammy. No, don't sorry, sorry, sorry,

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Speaker 2: I shouldn't have I shouldn't have it.

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Speaker 5: Not yet is the right answer.

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Speaker 3: Not yet, not yet, not yet.

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Speaker 1: I guess my first question should be, can you talk

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Speaker 1: to us about the through line between being musician and

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Speaker 1: healthcare and how and biotech and how you got into

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Speaker 1: the space, because I think you know, it's not a

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Speaker 1: natural transition, to say the least.

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Speaker 5: Yeah, well, I'll tell you how I ended up doing this,

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Speaker 5: and then I'll try to connect them theoretically in some way.

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Speaker 5: It might be a little tenuous. I've basically had three

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Speaker 5: careers so far in my limited adult life. I was

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Speaker 5: a professional rock musician with the band that you mentioned

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Speaker 5: for several years, and then I kind of slipped into

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Speaker 5: the venture capital world when I invested in Spotify about

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Speaker 5: thirteen years ago, and that was pretty much the only

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Speaker 5: company in the private markets I was well positioned to

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Speaker 5: understand as a musician, and through the success of that,

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Speaker 5: I got turned on to how exciting venture capital was.

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Speaker 5: Started doing other types of investments across different industries. Was

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Speaker 5: involved in SpaceX and Ripple and a bunch of interesting

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Speaker 5: other startups, and then ultimately a guy I knew started

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Speaker 5: an early stage a healthcare company. It was a telemedicine

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Speaker 5: startup called doctor on Demand, and telemedicine at the time

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Speaker 5: was not a hot topic because this is pre COVID,

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Speaker 5: so we still primarily went to the doctor in person.

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Speaker 5: And when I made that investment, I started to learn

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Speaker 5: more and more about our healthcare system and was just

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Speaker 5: blown away by how screwed up and stupid it was.

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Speaker 5: And then that eventually evolved into learning more about biotechnology

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Speaker 5: and the other sub sectors of healthcare that are critical

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Speaker 5: to medicine, and it's ended up being what I do.

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Speaker 5: In terms of the connection between music and any of

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Speaker 5: this stuff, there are a couple of ways I can

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Speaker 5: think about it. One is, I tell people, now my

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Speaker 5: job is like being a record producer for scientists, so

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Speaker 5: there's a little bit of a parallel there. But the

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Speaker 5: other is that I think there's a unique challenge in

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Speaker 5: music to combining art and commerce, And in healthcare there's

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Speaker 5: a similar parallel challenge, which is how do you combine

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Speaker 5: medicine and capitalism, which don't naturally go together very well?

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Speaker 2: Producer, analogy makes a ton of sense. And you know,

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Speaker 2: there are probably a lot of musicians who are really brilliant,

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Speaker 2: they're really great musicians, but for whatever reason, the lightning

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Speaker 2: doesn't strike where they are or doesn't strike nearby, and

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Speaker 2: they don't take off. Probably many brilliant scientists, et cetera,

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Speaker 2: but the path from brilliant science to commercial blockbuster can often,

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Speaker 2: I assume, be tricky or dispiriting in many ways, et cetera.

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Speaker 2: Biotech specifically, of all the things in investing, biotech strikes

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Speaker 2: me is this whole different world than the rest of

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Speaker 2: like investing. You know, when I think of like a

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Speaker 2: software company, it's like, oh, okay, well they've accumulated these

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Speaker 2: clients and their churn is low, et cetera. Yeah, this

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Speaker 2: seems like a company that has traction is going to grow.

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Speaker 2: When it comes to biotech, it's like, okay, here's some

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Speaker 2: patent on a sequence and maybe ten years from now

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Speaker 2: it'll get approved to something that'll be a therapy. It

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Speaker 2: seems so much harder to figure out, like what are

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Speaker 2: the heuristics that one would use to establish this is

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Speaker 2: a likely this science is likely going to turn into

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Speaker 2: a business.

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Speaker 5: Oh, that's absolutely true. It's like a completely different paradigm.

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Speaker 5: As an investor, I think the typical biotech company is

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Speaker 5: like a bag of options, and each one of the

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Speaker 5: drugs that the company is working on in success could

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Speaker 5: be worth billions of dollars, but that's ten years away

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Speaker 5: often minimum, and so you're trying to price things based

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Speaker 5: on their ultimate potential scale times their probability of succeeding,

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Speaker 5: and unfortunately, the base rates in terms of probability of

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Speaker 5: success are very low. So if you take small molecules,

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Speaker 5: which is one major area of drugs, the base case

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Speaker 5: is like a five percent probability of success from the

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Speaker 5: original idea to an FDA approval and a marketed drug.

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Speaker 5: Now you get to a higher sort of prior probability

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Speaker 5: with antibodies or so called biologics other classes of drugs

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Speaker 5: that are intrinsically more likely to work than small molecules,

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Speaker 5: but still in every case you're dealing with very low

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Speaker 5: probabilities of success, and the entire challenge as a biotech

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Speaker 5: investor is how do you manage those low probability events

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Speaker 5: and build portfolios that are still likely to make money

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Speaker 5: despite the fact that each individual project is relatively unlikely

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Speaker 5: to work. I'd say, in tech, there's this well described

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Speaker 5: kind of power law distribution of winners and losers, which

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Speaker 5: is to say, a very small number of companies make

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Speaker 5: all the money and pay for the huge number of losers.

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Speaker 5: In biotech, that's still true to a degree, but the

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Speaker 5: magnitudes of the winners are lower, and so a really

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Speaker 5: good biotech investor probably has a lower, sorry a higher

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Speaker 5: batting average than the typical tech investor, but the wins

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Speaker 5: are not as big.

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Speaker 1: So one thing I'm really curious about is how you

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Speaker 1: source potential investments and how you find you use the

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Speaker 1: analogy of the record, how you find talent in the space,

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Speaker 1: or how the talent kind of finds you, and whether

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Speaker 1: or not it's different from again, the sort of software

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Speaker 1: or tech space that we usually talk about when it

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Speaker 1: comes to venture capital.

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Speaker 5: You know, when I started doing venture investing, it was,

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Speaker 5: like I said, twelve thirteen years ago, it was obviously

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Speaker 5: a well established part of the capital markets. But you know,

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Speaker 5: I cold emailed Brian Armstrong from coinbase and was meeting

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Speaker 5: with them two days later. And it's hard to overstate

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Speaker 5: how much money has rushed in over the past decades.

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Speaker 5: So what went from being an established but still kind

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Speaker 5: of marginal part of the capital markets is now all

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Speaker 5: anyone thinks or talks about. And so in biotech, what

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Speaker 5: I found getting into this area was that it was

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Speaker 5: more like that venture market I had encountered. There was

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Speaker 5: a scarcity of capital relative to the to the caliber

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Speaker 5: of ideas that were out there, and so I'd say

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Speaker 5: deal sourcing is much easier in a sense because there's

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Speaker 5: less money chasing a huge number of good ideas, and

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Speaker 5: those ideas, by and large do come out of our

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Speaker 5: university and research infrastructure here in America. The same is

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Speaker 5: also true in other parts of the world in Europe, China, India,

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Speaker 5: and so forth. But it's really the translation of those

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Speaker 5: academic concepts into products that could make money that is

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Speaker 5: the challenge. That's the so called valley of death that

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Speaker 5: people sometimes talk about in our industry. There are just

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Speaker 5: an immense number of cool ideas. If you go into

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Speaker 5: any university in our country, but such a small number

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Speaker 5: of them is ever going to cross that chasm. And

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Speaker 5: part of that is that the expertise and the personnel

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Speaker 5: required to do that translational work is not the same

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Speaker 5: expertise that is required to do the inventing in the

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Speaker 5: first place. So that is really what the large pharmaceutical

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Speaker 5: companies have a specialized expertise and they train people in

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Speaker 5: this translational work. How do you go from early science

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Speaker 5: to real products?

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Speaker 2: When I go to a typical venture capitalist website or

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Speaker 2: I see their Twitter bio or something like that, it'll

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Speaker 2: say like, we bet great founders, and I'm like, thanks,

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Speaker 2: that's very helpful because that distinguishes you from the venture

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Speaker 2: capitalists who back crappy founders. So I'm glad I'm gonna

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Speaker 2: invest with you. And said, what's the biotech equivalent? What's

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Speaker 2: the cliche in your industry that every VC says that

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Speaker 2: ostensibly distinguishes them from all the others.

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Speaker 5: Well, I'm not sure what the vcs say. I mean,

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Speaker 5: they are kind of commoditized in the sense that most

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Speaker 5: of the firms look pretty similar. They employ thirty PhDs

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Speaker 5: and physicians, and the value of those people is that

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Speaker 5: they can make sense of the information that you have

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Speaker 5: to process to invest intelligently in this space. In terms

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Speaker 5: of what distinguishes the founders that they like to look at,

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Speaker 5: I'd say again, it's kind of the inverse of what

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Speaker 5: you find in tech. There's a real premium on quote

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Speaker 5: gray hair in the biotech industry because the only way

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Speaker 5: to learn this stuff is to do it over and

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Speaker 5: over again and to have had a lot of failures.

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Speaker 5: And if you think about a software company, the tropes

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Speaker 5: you are familiar with are you know, fail fast, pivot right.

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Speaker 5: You know, like, you launch something, it doesn't work, you

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Speaker 5: tweak the product design, you go into a different market.

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Speaker 5: You can adapt very readily to the market. In biotech,

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Speaker 5: if you choose to embark upon a clinical program, you're

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Speaker 5: in for thirty or forty million bucks, an easy door

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Speaker 5: to walk back out of. And so there's a real

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Speaker 5: premium on people with experience who have done it multiple times.

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Speaker 5: That is a little bit at odds in recent years

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Speaker 5: with a movement that people have I think awkwardly dubbed

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Speaker 5: tech bio instead of biotech. And really these are Silicon

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Speaker 5: Valley tech investors, not totally unlike myself, who have gotten

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Speaker 5: into biotech, and they think that what's about to change

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Speaker 5: is it's going to go the way of the tech

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Speaker 5: industry and the next big companies are going to be

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Speaker 5: started by really clever twenty one year olds coming out

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Speaker 5: of Stanford and that hypothesis people have been testing now

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Speaker 5: for a few years. I'd say it's a little too

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Speaker 5: early to issue a verdict, but that's never really been

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Speaker 5: our theory.

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Speaker 1: Is that hypothesis just predicated on AI coming in and

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Speaker 1: making you know, drug development easier.

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Speaker 3: Is that all it is.

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Speaker 5: There's a lot of that. I'd say there are two

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Speaker 5: parts of it. One of it is maybe more substantive

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Speaker 5: than that, and this is a little nuanced, but I

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Speaker 5: know odd lots of people like Nuance one of the

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Speaker 5: big transformations that really gave rise to the biotech industry.

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Speaker 5: And when I use that term biotech, I'm distinguishing it

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Speaker 5: from big pharma. So biotech really just means small drug companies,

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Speaker 5: many of them are public. What really gave rise to

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Speaker 5: that industry was the big pharmas at the behest of

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Speaker 5: Wall Street deprioritized early stage research because Wall Street said,

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Speaker 5: you're wasting a lot of money on this really risky

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Speaker 5: early stage discovery work. What we would rather you did

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Speaker 5: was just let all these crazy guys like da finance

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Speaker 5: startups and once they work, just buy them. You know,

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Speaker 5: you're going to pay a higher price, but you won't

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Speaker 5: be burning all this money on early stuff. What that

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Speaker 5: led to was an exodus a very specialized technical experts

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Speaker 5: from the pharma companies, and it created the so called

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Speaker 5: cro or contract research organization ecosystem. So you now, as

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Speaker 5: a consequence of that, for the past twenty years, have

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Speaker 5: had a very proficient environment full of contract organizations that

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Speaker 5: you can hire as a little company to outsource a

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Speaker 5: lot of work that you couldn't in the past. So

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Speaker 5: the best analogy to to tech would be sort of

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Speaker 5: like virtual servers or cloud infrastructure, Like you know, to

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Speaker 5: have a startup, you used to have all these servers

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Speaker 5: in your office, and then at some point you didn't

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Speaker 5: need that, so the cost of new company formation went

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Speaker 5: way down. So part of the argument for younger, more

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Speaker 5: agile founders has been, look, we got this whole new

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Speaker 5: kind of infrastructure through which they can build companies in

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Speaker 5: a really agile way. The other argument, you know exactly

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Speaker 5: your question, is around AI, and that is basically, look

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Speaker 5: these old people don't understand AI. Let's get some young

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Speaker 5: Silicon Valley computer science he types to do this, and

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Speaker 5: they're gonna show them how it's done.

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Speaker 2: I feel like that's probably a phenomenon that goes beyond biotech,

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Speaker 2: where there's this fantasy, and maybe in some cases it's

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Speaker 2: even correct, but there is this fantasy that every industry

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Speaker 2: out there must be dominated by old dinosaurs who don't

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Speaker 2: know how to use tech and who have been doing

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Speaker 2: something the same way forever. And so you're twenty twenty five,

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Speaker 2: it must be out of date by now and they

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Speaker 2: haven't figured this out.

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Speaker 3: And if we could just cough cough, journalism.

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Speaker 2: Yeah right, if we could just hire wiz kids, then

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Speaker 2: we could reinvent the industry from first principles and just

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Speaker 2: do a much better job than the legacy of things.

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Speaker 2: And I think, whether it's healthcare or whether it's industrial

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Speaker 2: stuff that we see Silicon Valley getting excited about right now,

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Speaker 2: it just feels like the default assumption must be that

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Speaker 2: the veterans are doing something wrong, and with pure brain power,

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Speaker 2: we could figure out what that thing is.

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Speaker 5: I think that is a reasonable characterization or what people

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Speaker 5: say today in a lot of different places, and I

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Speaker 5: don't think it's true in my sector. But as with

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Speaker 5: every conversation about AI, the challenge is balancing two ideas

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Speaker 5: that can be true at the same time but seem contradictory.

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Speaker 5: And one is that this stuff is amazing, and it is,

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Speaker 5: particularly in life sciences, responsible for some true breakthroughs, like

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Speaker 5: the breakthrough that won Demisisabus at deep Mind the Nobel

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Speaker 5: Prize last year with alpha fold, which was this amazing

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Speaker 5: discovery they made that using machine learning models you could

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Speaker 5: solve a problem that had gone unsolved for decades, which

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Speaker 5: was can you predict from the sequence of a protein's

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Speaker 5: amino acids what three dimensional shape a protein is going

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Speaker 5: to take in a physical environment. And I just threw

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Speaker 5: around a bunch of terms of art. But this is

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Speaker 5: fundamental to drug development and drug discovery. So it's like,

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Speaker 5: on the one hand, you can't deny these breakthroughs that

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Speaker 5: we're experiencing. You can't deny that when you talk to Gemini,

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Speaker 5: it's staggering what this thing can do. I mean, I'm

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Speaker 5: sitting there all day having it teach me about asset

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Speaker 5: pricing models or whatever else I'm interested in. But at

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Speaker 5: the same time, the religious movement that is powering all

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Speaker 5: of the investment and a lot of the entrepreneurship here

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Speaker 5: across industries is full of hot air and is making

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Speaker 5: claims that are preposterous unless you are a zealot.

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Speaker 2: Just real quickly, if we had been having this conversation

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Speaker 2: in a month ago, would you have said Gemini or

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Speaker 2: would you have said CHADJPT Because I switched from chad

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Speaker 2: JPT to Gemini in the last month, and I'm just

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Speaker 2: curious whether you're what you would have said a month ago.

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Speaker 5: A month ago, I was using all of them. Now

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Speaker 5: I'm only using Gemen.

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Speaker 2: It's interesting, all right, good data plant.

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Speaker 1: Okay, talk to us about the choke points when it

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Speaker 1: comes to new drug development, because I imagine, okay, maybe

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Speaker 1: AI machine learning can speed up some of the research

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Speaker 1: or discovery process, but even after that, you have to

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Speaker 1: go through these really long clinical trials that in some

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Speaker 1: cases take decades. What are the major I guess, like

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Speaker 1: stumbling blocks to getting something to the market.

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Speaker 5: Your question held the answer. The process of taking a

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Speaker 5: drug from idea to the market. You can think of

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Speaker 5: as a funnel. To just use a visual analogy and

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Speaker 5: into the top of the funnel, go all the millions

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Speaker 5: of ideas that people have, and then as you go

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Speaker 5: down the funnel, you are spending progressively more and more

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Speaker 5: and more money to prove two things. The first is

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Speaker 5: that the drug is safe and won't harm or kill people,

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Speaker 5: and the second is that the drug works and actually

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Speaker 5: modifies the disease that you're trying to treat. And the

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Speaker 5: tragedy of our moment is that the only way to

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Speaker 5: figure out if drugs are safe and effective is to

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Speaker 5: try them in human beings, living, breathing human beings, and

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Speaker 5: that is extraordinarily time consuming and incredibly expensive financially. So

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Speaker 5: I wish for the day when AI is able to

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Speaker 5: fully simulate an accurate human in the computer and we

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Speaker 5: don't need to do clinical trials on real people. But

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Speaker 5: until that moment, the vast majority of the cost and

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Speaker 5: expense and time that is involved in drug discovery remains

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Speaker 5: with us. So most of the AI technologies that people

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Speaker 5: are excited about really would have the effect of putting

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Speaker 5: more good ideas into the top of the funnel, But

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Speaker 5: unfortunately that doesn't solve a problem that we have. We

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Speaker 5: already are drowning in good ideas, and the issue is

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Speaker 5: exactly the choke point or bottleneck that you're referring to.

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Speaker 2: This is really there's actually two questions. First of all,

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Speaker 2: is there low hanging fruit from a regulatory side to

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Speaker 2: accelerate that process. People like to fathom, oh, the FDA

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Speaker 2: must be super There's another area people will say, oh,

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Speaker 2: the FDA must be super slow and do things one

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Speaker 2: way we could expede this up. I don't know. Is

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Speaker 2: there somewhere along the process where like from a regulatory

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Speaker 2: standpoint or some other thing, that the either the cost

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Speaker 2: of the timelines could shrink or is it mostly still

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Speaker 2: just the reality of we have to test these things

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Speaker 2: on humans and that's costly going, it takes time.

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Speaker 5: Well, we don't need to do anything. We could have

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Speaker 5: no FDA and anyone who has a good drug idea

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Speaker 5: just launches it commercially and if some people die from

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Speaker 5: that and it doesn't do anything, that's fine. By the way.

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Speaker 5: That's kind of like the supplement yeah, time and the

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Speaker 5: way we deal with it. Milton Friedman famously thought that

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Speaker 5: the FDA should only assess the safety of drugs, and

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Speaker 5: if a drug was proven safe, put it on the

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Speaker 5: market and let the market dictate whether people determine they

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Speaker 5: should pay for it based on their lived experience with

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Speaker 5: whether it works or not. Now, I just personally prefer

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Speaker 5: to live in a world where if I've got something

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Speaker 5: that's going wrong, I can more or less trust that

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Speaker 5: the product my doctor gives me has been proven safe

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Speaker 5: and effective. And that reflects that we have today a

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Speaker 5: pretty high bar for approving drugs. But we could certainly

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Speaker 5: lower that bar. We could change the type of data

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Speaker 5: that the FDA requires, And that's what's happening in China.

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Speaker 5: By the way, I know you mentioned this other episode

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Speaker 5: you did with my friend Tim. In China, the regulatory

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Speaker 5: environment has been moving pretty rapidly, and they've done that

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Speaker 5: deliberately because they want to be more productive. They want

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Speaker 5: to approve more drugs, and they're trying to strike that

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Speaker 5: balance between being prolific and holding things to a high

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Speaker 5: standard at the same time. So you know, we'll see.

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Speaker 2: And I just want to up and one other thing

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Speaker 2: you said, because I think it seems important someone like

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Speaker 2: Sam Altman, when he talks about the promise of AI,

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Speaker 2: a lot of it is like, Oh, we could find

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Speaker 2: the next drug that cures cancer. In the meantime, we're

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Speaker 2: going to make this sort of slot machine that makes

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Speaker 2: weird videos, et cetera. But really we're trying to find

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Speaker 2: these wonder drugs in long term. But for what it

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Speaker 2: sounds like you said, candidates are not where the shortages like.

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Speaker 2: The issue is not that we lack sufficiently a number

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Speaker 2: of sufficiently promising molecule combinations. The scarcity is not on

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Speaker 2: that at that point.

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Speaker 5: That's my view. I mean, I'll steal me in the

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Speaker 5: other argument. The other argument would be, well, look, Dea,

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Speaker 5: you said ten minutes ago that these drugs have a

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Speaker 5: five percent probability of working from the outset. You know,

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Speaker 5: if we had better predictive models that told us certain

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Speaker 5: candidates were much more likely to work than others, wouldn't

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Speaker 5: that be great? And my rejoinder to that is yes,

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Speaker 5: but how would we know that we've done that? Meaning

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Speaker 5: if the three of us tomorrow invented a black box

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Speaker 5: that produced drug candidate concepts, and we were certain that

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Speaker 5: our model doubled the prior probability from five percent to

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Speaker 5: ten percent, that would be a truly revolutionary innovation on

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Speaker 5: our part. But how many candidates from that model would

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Speaker 5: we need to take all the way to an approval

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Speaker 5: before we had statistically demonstrated that we in fact increased

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Speaker 5: the rate of success. So people may have already cracked

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Speaker 5: that code. You know, Google may have already cracked that code.

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Speaker 5: Sam Waltman may have cracked that code. But someone's going

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Speaker 5: to need to spend thirty billion dollars developing the drug

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Speaker 5: ideas he has before we know whether he's done that,

437
00:24:46,880 --> 00:24:51,240
Speaker 5: And until that money is spent, it's pure conjecture and salesmanship.

438
00:25:06,560 --> 00:25:10,719
Speaker 1: How are you actually evaluating opportunities in the US against

439
00:25:11,160 --> 00:25:14,320
Speaker 1: China competition, Because you know, if clinical trials are the

440
00:25:14,320 --> 00:25:17,399
Speaker 1: major choke point, and if China seems to be trying

441
00:25:17,480 --> 00:25:21,240
Speaker 1: to make that process as efficient as possible, it seems

442
00:25:21,320 --> 00:25:23,800
Speaker 1: like maybe they have an advantage.

443
00:25:23,960 --> 00:25:27,119
Speaker 5: I mean, they definitely have an advantage. And if I

444
00:25:27,240 --> 00:25:29,560
Speaker 5: had to make a bet today on our sector, it

445
00:25:29,600 --> 00:25:31,520
Speaker 5: would be that China is going to be the big

446
00:25:31,560 --> 00:25:34,160
Speaker 5: story over the next decade or two. I think it's

447
00:25:34,200 --> 00:25:39,400
Speaker 5: a fundamental structural shift in the global biotechnology market. And

448
00:25:39,880 --> 00:25:43,240
Speaker 5: their advantages are multiple. I mean, their advantages are regulatory,

449
00:25:43,840 --> 00:25:47,440
Speaker 5: they relate to the personnel. We have lost an amazing

450
00:25:47,640 --> 00:25:52,160
Speaker 5: amount of talent who was educated here in our graduate

451
00:25:52,160 --> 00:25:56,280
Speaker 5: schools and now has gone back to China. And furthermore,

452
00:25:56,840 --> 00:26:01,240
Speaker 5: they are able to develop things in the clinic, which

453
00:26:01,280 --> 00:26:04,320
Speaker 5: is to say, do clinical trials a lot faster and

454
00:26:04,359 --> 00:26:08,520
Speaker 5: at a much higher volume than our infrastructure can handle.

455
00:26:08,880 --> 00:26:11,480
Speaker 5: So they've got big advantages. Now, how do I think

456
00:26:11,480 --> 00:26:14,159
Speaker 5: about investing in the US versus China. I don't that

457
00:26:14,280 --> 00:26:17,119
Speaker 5: much because I don't speak Mandarin, and I think it

458
00:26:17,119 --> 00:26:19,800
Speaker 5: would be really difficult for me to invest in China today.

459
00:26:20,400 --> 00:26:24,680
Speaker 5: But increasingly companies in the US are starting to outsource

460
00:26:25,160 --> 00:26:28,240
Speaker 5: certain parts of the research process to Chinese companies, and

461
00:26:28,320 --> 00:26:31,880
Speaker 5: increasingly they're going to outsource parts of the clinical development process,

462
00:26:32,080 --> 00:26:34,399
Speaker 5: the clinical trials to China. That's going to make a

463
00:26:34,480 --> 00:26:35,320
Speaker 5: huge impact on the AUA.

464
00:26:35,400 --> 00:26:38,240
Speaker 1: Yeah, this was actually my next question. I guess how

465
00:26:38,280 --> 00:26:42,760
Speaker 1: translatable is a successful clinical trial in China to a

466
00:26:42,880 --> 00:26:44,080
Speaker 1: market like the US.

467
00:26:44,800 --> 00:26:49,159
Speaker 5: Three or four years ago, what both investors and regulators

468
00:26:49,200 --> 00:26:51,560
Speaker 5: in the US would have told you was that it's

469
00:26:51,560 --> 00:26:54,520
Speaker 5: not that translatable because they're liars and they make up

470
00:26:54,560 --> 00:26:57,880
Speaker 5: all the data, and it's rampant with fraud. And there

471
00:26:57,880 --> 00:26:59,400
Speaker 5: may have been some truth to that, but I think

472
00:26:59,400 --> 00:27:02,600
Speaker 5: there was also a good amount of racism and what

473
00:27:03,400 --> 00:27:05,600
Speaker 5: sort of woke everyone up in the past couple of

474
00:27:05,680 --> 00:27:10,120
Speaker 5: years was that some very significant clinical trials were done

475
00:27:10,160 --> 00:27:13,840
Speaker 5: in China. People were suspicious of the data. Then they

476
00:27:14,640 --> 00:27:17,359
Speaker 5: replicated those trials in Europe or the United States and

477
00:27:17,400 --> 00:27:21,280
Speaker 5: got very similar data, and folks thought, WHOA, maybe they're

478
00:27:21,320 --> 00:27:25,880
Speaker 5: not so bad at this. So I think decreasingly people

479
00:27:26,200 --> 00:27:31,680
Speaker 5: are skeptical, and which said less awkwardly, people are trusting

480
00:27:31,760 --> 00:27:34,800
Speaker 5: more and more what's coming out of China. And it's

481
00:27:34,800 --> 00:27:37,240
Speaker 5: incumbent upon the Chinese to the extent that they want

482
00:27:37,240 --> 00:27:42,120
Speaker 5: this to be a major strategy to continue enhancing people's

483
00:27:42,160 --> 00:27:44,800
Speaker 5: trust in the quality of their work and their data.

484
00:27:45,800 --> 00:27:48,760
Speaker 5: If they can do that. I think it's a global industry.

485
00:27:48,760 --> 00:27:50,800
Speaker 5: A lot of the companies are multinationals. They don't care

486
00:27:50,800 --> 00:27:52,760
Speaker 5: if the drug comes out of the US or comes

487
00:27:52,760 --> 00:27:54,240
Speaker 5: out of China.

488
00:27:54,280 --> 00:27:57,120
Speaker 2: This is a really good question about private or VC

489
00:27:57,280 --> 00:28:01,080
Speaker 2: stage investing per se, but about biotech more broadly. You know,

490
00:28:01,119 --> 00:28:03,200
Speaker 2: I've covered the stock market for a long time in

491
00:28:03,280 --> 00:28:05,840
Speaker 2: various ways. I've never spent any time really getting to

492
00:28:05,880 --> 00:28:09,520
Speaker 2: know a publicly traded biotech doc is, are you insane

493
00:28:09,720 --> 00:28:13,800
Speaker 2: to try to invest in biotech if you don't have PhD?

494
00:28:14,160 --> 00:28:17,800
Speaker 2: Level understanding of biology, Like, can anyone have alpha in

495
00:28:17,840 --> 00:28:21,280
Speaker 2: this industry if they don't actually know science.

496
00:28:21,720 --> 00:28:22,600
Speaker 5: I think it's tough.

497
00:28:23,400 --> 00:28:25,000
Speaker 2: Yeah, it seems very tough to you me.

498
00:28:25,200 --> 00:28:29,359
Speaker 5: Look, yeah, I mean, here's the thing. What's really interesting

499
00:28:29,400 --> 00:28:34,639
Speaker 5: about biotech in the public markets is it's abundantly clear

500
00:28:34,840 --> 00:28:39,360
Speaker 5: that active investors can have alpha in biotech, whereas as

501
00:28:39,400 --> 00:28:42,360
Speaker 5: you guys know, that is not clear in the rest

502
00:28:42,400 --> 00:28:46,560
Speaker 5: of the public equity landscape. And so whereas there is

503
00:28:46,840 --> 00:28:52,280
Speaker 5: very little, if not negative persistence of performance among active

504
00:28:52,400 --> 00:28:56,640
Speaker 5: equity managers broadly, in biotech, you have a small number

505
00:28:56,640 --> 00:29:00,000
Speaker 5: of firms that have been doing great for sometimes decades,

506
00:29:00,680 --> 00:29:01,320
Speaker 5: and it.

507
00:29:01,120 --> 00:29:03,680
Speaker 2: Is and they all have real science expertise on Stowe

508
00:29:03,760 --> 00:29:04,080
Speaker 2: they do.

509
00:29:04,480 --> 00:29:08,480
Speaker 5: And you know, the dynamic between them and the generalists,

510
00:29:08,520 --> 00:29:11,520
Speaker 5: so to speak, is that they do a lot of

511
00:29:11,600 --> 00:29:14,320
Speaker 5: very detailed work to make sense of the information you

512
00:29:14,400 --> 00:29:17,880
Speaker 5: need to process to value these companies and to assess

513
00:29:17,920 --> 00:29:21,640
Speaker 5: their probability of success. And then the generalists often follow

514
00:29:22,000 --> 00:29:25,880
Speaker 5: those specialists into these names and the fortunes of the

515
00:29:25,960 --> 00:29:29,680
Speaker 5: industry in these cycles, like we're coming out of a

516
00:29:29,760 --> 00:29:32,560
Speaker 5: four year great depression for biotech, I should just mention

517
00:29:33,600 --> 00:29:37,520
Speaker 5: a lot of those fortunes ride on the sector rotations

518
00:29:37,560 --> 00:29:41,120
Speaker 5: of the generalists. So the specialists have to stick with

519
00:29:41,160 --> 00:29:43,840
Speaker 5: biotech because that's what they do. But whether or not

520
00:29:43,960 --> 00:29:47,840
Speaker 5: companies can IPO, whether or not companies can fund their

521
00:29:47,840 --> 00:29:51,120
Speaker 5: next clinical trial, is largely a function of whether the

522
00:29:51,320 --> 00:29:54,160
Speaker 5: generalists are in the sector at that moment or not.

523
00:29:54,720 --> 00:29:58,360
Speaker 5: And we're just in the midst of the early rotation

524
00:29:58,520 --> 00:29:59,280
Speaker 5: of generalists.

525
00:29:59,320 --> 00:30:02,960
Speaker 1: Back into wait, the biotech investing downturn, was that just

526
00:30:03,000 --> 00:30:05,360
Speaker 1: a function of higher interest rates or was something else

527
00:30:05,400 --> 00:30:05,760
Speaker 1: going on?

528
00:30:06,880 --> 00:30:10,040
Speaker 5: It was a confluence of everything that could go wrong

529
00:30:10,040 --> 00:30:12,920
Speaker 5: at the same time. It was higher interest rates, which

530
00:30:13,400 --> 00:30:16,440
Speaker 5: really punished these biotech stocks relative to other companies because

531
00:30:16,520 --> 00:30:18,960
Speaker 5: you know, no cash flows for ten years and then

532
00:30:19,000 --> 00:30:21,480
Speaker 5: a big bowl of some money. So these companies are

533
00:30:21,600 --> 00:30:26,440
Speaker 5: very sensitive to discount rates. Add to that this dynamic

534
00:30:26,480 --> 00:30:29,680
Speaker 5: where the generalists had gotten out of the sector, that

535
00:30:30,320 --> 00:30:35,479
Speaker 5: ultimately is fatal. And then consider the fact that we

536
00:30:35,560 --> 00:30:38,760
Speaker 5: had such a come down after the sugar high of COVID.

537
00:30:38,880 --> 00:30:42,240
Speaker 5: So obviously during COVID there was this moment of clarity

538
00:30:42,600 --> 00:30:48,479
Speaker 5: where everyone for a second recognized that this sector is

539
00:30:48,920 --> 00:30:51,040
Speaker 5: for each of us at some point in our lives.

540
00:30:51,160 --> 00:30:53,960
Speaker 5: The most important thing that happens in the global economy.

541
00:30:54,960 --> 00:30:59,200
Speaker 5: Like without the biotech industry, you know, we're all in trouble.

542
00:30:59,640 --> 00:31:01,880
Speaker 5: And we kind of go through life pretending like we're

543
00:31:01,920 --> 00:31:04,640
Speaker 5: never going to need this industry, and then you get cancer,

544
00:31:04,760 --> 00:31:07,200
Speaker 5: your dad gets cancer, your kid gets some rare disease,

545
00:31:07,360 --> 00:31:09,400
Speaker 5: and you go, holy cow. I wish I had thought

546
00:31:09,400 --> 00:31:11,920
Speaker 5: about this before. Maybe all these people who are doing

547
00:31:11,960 --> 00:31:15,680
Speaker 5: this with their lives are not evil bloodsuckers who Bernie

548
00:31:15,680 --> 00:31:21,160
Speaker 5: Sanders needs to take down. And that is I think

549
00:31:21,240 --> 00:31:24,600
Speaker 5: part of what dawned on people during COVID, when we

550
00:31:24,680 --> 00:31:29,600
Speaker 5: all were vulnerable and we all were yearning for a solution.

551
00:31:30,040 --> 00:31:33,440
Speaker 1: Talk a little bit more about, I guess, the financial

552
00:31:33,440 --> 00:31:38,280
Speaker 1: incentives about actually developing new drugs. So we all know

553
00:31:38,600 --> 00:31:41,800
Speaker 1: the story of if you're based in the US, you

554
00:31:41,840 --> 00:31:45,080
Speaker 1: can go to Mexico or wherever else and buy the

555
00:31:45,120 --> 00:31:48,040
Speaker 1: same medicine for like five bucks as opposed to five

556
00:31:48,120 --> 00:31:51,440
Speaker 1: hundred dollars or perhaps even more in the US. And

557
00:31:51,520 --> 00:31:54,719
Speaker 1: the argument for that seems to be that, well, you know,

558
00:31:54,800 --> 00:31:58,680
Speaker 1: the big pharma companies need to be rewarded for all

559
00:31:58,680 --> 00:32:01,160
Speaker 1: the research and the effort, the risk that they actually

560
00:32:01,200 --> 00:32:04,280
Speaker 1: take on and for some reason, the US seems to

561
00:32:04,320 --> 00:32:06,440
Speaker 1: be the designated place to do that.

562
00:32:07,120 --> 00:32:11,920
Speaker 3: But like, why why? Is my question? Why US drugs?

563
00:32:12,720 --> 00:32:18,480
Speaker 5: Well, the big bounty for a drug development company is

564
00:32:18,520 --> 00:32:22,920
Speaker 5: the United States market, and that's partly because we as

565
00:32:22,960 --> 00:32:26,200
Speaker 5: a society have decided that we want all the new,

566
00:32:26,320 --> 00:32:30,640
Speaker 5: most advanced drugs. We want them first, and we don't

567
00:32:30,760 --> 00:32:34,320
Speaker 5: want to deny them to people who could benefit from them. Now,

568
00:32:34,640 --> 00:32:38,440
Speaker 5: the price we pay for those commitments is that our

569
00:32:38,560 --> 00:32:41,800
Speaker 5: drug prices are higher than the prices in other countries.

570
00:32:42,600 --> 00:32:44,960
Speaker 5: And the reason their prices are lower is because their

571
00:32:45,000 --> 00:32:48,840
Speaker 5: governments choose which drugs their people will have access to,

572
00:32:49,760 --> 00:32:53,840
Speaker 5: and they make those choices and then negotiate the prices

573
00:32:53,880 --> 00:32:56,240
Speaker 5: with the companies, and they basically will say to Pfizer

574
00:32:56,720 --> 00:32:59,600
Speaker 5: or Astro Zeneca, look, if you want your drugs sold

575
00:32:59,640 --> 00:33:02,240
Speaker 5: here in Japan, you're going to take the price that

576
00:33:02,320 --> 00:33:05,240
Speaker 5: we give you, and then the pharma company decides whether

577
00:33:05,320 --> 00:33:08,120
Speaker 5: they want to accept that deal or not. Now, the

578
00:33:08,240 --> 00:33:13,160
Speaker 5: United States absolutely could choose as a civilization to negotiate

579
00:33:13,200 --> 00:33:16,840
Speaker 5: in that same manner. Our government could make the choice

580
00:33:16,960 --> 00:33:20,200
Speaker 5: for US as to exactly what we're willing to pay

581
00:33:20,240 --> 00:33:23,080
Speaker 5: for every drug. There would be two consequences to that.

582
00:33:23,440 --> 00:33:28,240
Speaker 5: One is that we would go without certain drugs. The

583
00:33:28,280 --> 00:33:30,880
Speaker 5: second is that a lot of drugs would not even

584
00:33:30,920 --> 00:33:34,520
Speaker 5: be developed in the first place, because the total pool

585
00:33:34,560 --> 00:33:37,760
Speaker 5: of profits available to drug companies would be much smaller.

586
00:33:38,480 --> 00:33:42,760
Speaker 5: And so I don't know that there is any perfect

587
00:33:42,960 --> 00:33:47,640
Speaker 5: answer to how much pharmaceutical innovation we should have in

588
00:33:47,680 --> 00:33:50,560
Speaker 5: the world. We get to choose how much innovation we

589
00:33:50,600 --> 00:33:53,719
Speaker 5: want to occur, and the way we choose that is

590
00:33:53,840 --> 00:33:57,280
Speaker 5: by determining the size of that bounty that exists. How

591
00:33:57,320 --> 00:34:01,200
Speaker 5: big is the profit pool we want to allow for

592
00:34:01,320 --> 00:34:04,080
Speaker 5: innovative drug development, and a lot of that is driven

593
00:34:04,120 --> 00:34:07,120
Speaker 5: by our patent law. Remember, a patent in this industry

594
00:34:07,240 --> 00:34:10,680
Speaker 5: is a legalized monopoly. So we give drug companies a

595
00:34:10,760 --> 00:34:13,759
Speaker 5: legal monopoly for a limited period of time, and that

596
00:34:13,800 --> 00:34:16,279
Speaker 5: dictates how much money they're able to make off of

597
00:34:16,320 --> 00:34:19,160
Speaker 5: a new drug. We could shorten the patent life, and

598
00:34:19,200 --> 00:34:21,640
Speaker 5: that would reduce the profit pool and you'd have less

599
00:34:21,680 --> 00:34:24,560
Speaker 5: drug development. We could remove the patent life, you could

600
00:34:24,600 --> 00:34:27,520
Speaker 5: have a permanent monopoly, and believe me, the industry would

601
00:34:27,520 --> 00:34:30,799
Speaker 5: double or triple overnight. So it's a choice we have

602
00:34:30,840 --> 00:34:32,280
Speaker 5: to make, and it's a civic choice.

603
00:34:32,360 --> 00:34:35,520
Speaker 2: You mentioned the Bernie Sanders of the world, who they

604
00:34:35,600 --> 00:34:37,600
Speaker 2: look at the profits of drug companies, or they look

605
00:34:37,640 --> 00:34:41,520
Speaker 2: at the prices of drugs, and you know if perhaps

606
00:34:41,520 --> 00:34:43,680
Speaker 2: if they got their way, there would be less investment

607
00:34:43,719 --> 00:34:47,680
Speaker 2: in drug discovery, etc. At all, maybe less profits. Going

608
00:34:47,680 --> 00:34:50,840
Speaker 2: back to COVID. However, there was also the backlash on

609
00:34:50,880 --> 00:34:54,640
Speaker 2: the other side, essentially just this deep skepticism towards the

610
00:34:54,680 --> 00:34:57,560
Speaker 2: premise of pharma and that what are these scientists doing

611
00:34:57,600 --> 00:34:59,880
Speaker 2: and why don't they tell you about this root the

612
00:35:00,080 --> 00:35:03,160
Speaker 2: people have used for thousands of years that cured these

613
00:35:03,200 --> 00:35:05,359
Speaker 2: diseases that they don't want you to know about so

614
00:35:05,400 --> 00:35:07,680
Speaker 2: that they can sell your stuff, talk to us about

615
00:35:07,719 --> 00:35:11,920
Speaker 2: like just this sort of political environment investing in biotech

616
00:35:12,160 --> 00:35:15,120
Speaker 2: in a political environment, or a growing number of people

617
00:35:15,640 --> 00:35:20,160
Speaker 2: frankly seem to distrust the premise of scientific expertise.

618
00:35:22,600 --> 00:35:26,719
Speaker 5: Look, it's tough, and some of the blame certainly belongs

619
00:35:26,800 --> 00:35:31,480
Speaker 5: with the scientific community, because you know, to the extent that, say,

620
00:35:31,480 --> 00:35:35,040
Speaker 5: in the early days of COVID, communication with the public

621
00:35:35,120 --> 00:35:39,000
Speaker 5: about say, the value of masks was not clear and

622
00:35:39,000 --> 00:35:41,920
Speaker 5: it was maybe even misleading. Some of the presentation of

623
00:35:42,040 --> 00:35:46,560
Speaker 5: data regarding the efficacy of the vaccines was not transparent,

624
00:35:47,080 --> 00:35:51,640
Speaker 5: and that eroded the public's trust in a very understandable way. Now,

625
00:35:52,200 --> 00:35:56,799
Speaker 5: I'm no apologist for medicine or science, because I don't

626
00:35:56,800 --> 00:36:01,160
Speaker 5: think these are privileged priesthoods. I think every person should

627
00:36:01,160 --> 00:36:06,200
Speaker 5: be able to be engaged in and understand science and medicine.

628
00:36:06,680 --> 00:36:12,960
Speaker 5: And unfortunately, the entire history of medicine began with medical

629
00:36:13,560 --> 00:36:18,640
Speaker 5: science as total witchcraft and sorcery. So if you go

630
00:36:18,719 --> 00:36:23,359
Speaker 5: back to antiquity, the first people calling themselves doctors objectively

631
00:36:23,440 --> 00:36:28,440
Speaker 5: understood nothing. So this was pure sophistry from the beginning.

632
00:36:29,040 --> 00:36:33,120
Speaker 5: And we are on this long journey through which medicine

633
00:36:33,200 --> 00:36:37,880
Speaker 5: is going from total bs and witchcraft to slowly turning

634
00:36:38,000 --> 00:36:41,760
Speaker 5: into a real science, something that deserves to be called science.

635
00:36:42,560 --> 00:36:48,200
Speaker 5: Medicine is filled with common practices that are not rigorously

636
00:36:48,239 --> 00:36:53,000
Speaker 5: based on evidence, and that is symptomatic of where we

637
00:36:53,080 --> 00:36:56,759
Speaker 5: are in that journey that I'm describing. So I'm an

638
00:36:56,760 --> 00:37:01,319
Speaker 5: advocate for medicine becoming always more and more scientific. I

639
00:37:01,360 --> 00:37:05,440
Speaker 5: believe that scientific policymakers, scientists, and academia need to do

640
00:37:05,440 --> 00:37:09,080
Speaker 5: a much better job communicating transparently, and that's the only

641
00:37:09,120 --> 00:37:11,680
Speaker 5: way to engender that kind of trust you're talking about, Joe,

642
00:37:12,160 --> 00:37:15,480
Speaker 5: and the trust is critical because it is what gives

643
00:37:15,520 --> 00:37:17,800
Speaker 5: permission to this industry's existence.

644
00:37:17,920 --> 00:37:21,600
Speaker 1: Wait, talk more about I guess autonomy when it comes

645
00:37:21,640 --> 00:37:25,040
Speaker 1: to medical decisions, because this is, you know, a big

646
00:37:25,160 --> 00:37:28,840
Speaker 1: culture shock of non Americans who come to the US

647
00:37:29,080 --> 00:37:33,120
Speaker 1: is drug adverts on TV where they you know, here's

648
00:37:33,160 --> 00:37:35,719
Speaker 1: this great drug, and then they read off all the

649
00:37:36,120 --> 00:37:38,800
Speaker 1: risk factors really really quickly, and one of the risks

650
00:37:38,880 --> 00:37:43,239
Speaker 1: is always death or so your brain damage. You're something yeah,

651
00:37:43,280 --> 00:37:47,400
Speaker 1: and I'm always like, again, I've never asked for a

652
00:37:47,520 --> 00:37:51,200
Speaker 1: drug that I've seen on TV. I do remember when

653
00:37:51,239 --> 00:37:53,840
Speaker 1: I when I first came to the US as an adult,

654
00:37:54,000 --> 00:37:56,440
Speaker 1: I went to get a prescription. I found a new

655
00:37:56,520 --> 00:37:59,640
Speaker 1: doctor to do that, and I said I needed this

656
00:37:59,640 --> 00:38:01,520
Speaker 1: thing and the doctor was like, oh, well, we have

657
00:38:01,560 --> 00:38:04,120
Speaker 1: to run all these medical tests before we can give

658
00:38:04,160 --> 00:38:07,520
Speaker 1: you that, and it ended up in a big argument

659
00:38:07,680 --> 00:38:10,759
Speaker 1: with my insurance provider. And I remember talking to people

660
00:38:10,760 --> 00:38:12,279
Speaker 1: about that and they were like, well, you should have

661
00:38:12,280 --> 00:38:15,680
Speaker 1: pushed back against the doctor about the testing, and I

662
00:38:15,719 --> 00:38:17,560
Speaker 1: was like, what do I know? I just do what

663
00:38:17,600 --> 00:38:21,280
Speaker 1: the doctor tells me, right, how much say should people?

664
00:38:21,520 --> 00:38:22,000
Speaker 5: Actually?

665
00:38:22,880 --> 00:38:25,759
Speaker 1: It sounds weird but you know, given the lack of experience,

666
00:38:25,760 --> 00:38:29,120
Speaker 1: and given the way other systems work around the world,

667
00:38:29,160 --> 00:38:32,400
Speaker 1: how much, say, should people have in their own medical treatment.

668
00:38:33,320 --> 00:38:36,320
Speaker 5: I think ultimately they should have almost all of the say,

669
00:38:36,640 --> 00:38:39,480
Speaker 5: it's your body. Ultimately, you have to make the best

670
00:38:39,560 --> 00:38:43,640
Speaker 5: decision you can make, and you should regard physicians, nurses,

671
00:38:43,800 --> 00:38:47,480
Speaker 5: others in the system as consultants who support you in

672
00:38:47,520 --> 00:38:52,919
Speaker 5: making wise decisions. The one caveat there, however, is that

673
00:38:53,000 --> 00:38:56,200
Speaker 5: we do socialize a lot of our medical costs, and

674
00:38:56,280 --> 00:39:00,120
Speaker 5: in many other countries they completely socialize medical costs to

675
00:39:00,160 --> 00:39:02,840
Speaker 5: the extent that you want the rest of us to

676
00:39:02,880 --> 00:39:05,799
Speaker 5: pay for your medical care. I do believe we need

677
00:39:05,840 --> 00:39:09,839
Speaker 5: to have some standards around what it's appropriate to pay for.

678
00:39:10,960 --> 00:39:13,239
Speaker 1: Yeah, I mean, at the moment, it seems like most

679
00:39:13,280 --> 00:39:17,680
Speaker 1: of those decisions are left up to the insurers, which again,

680
00:39:18,200 --> 00:39:20,880
Speaker 1: in other places in the world, it would be left

681
00:39:20,960 --> 00:39:26,480
Speaker 1: up to the governments to make those decisions. Are insurers

682
00:39:26,520 --> 00:39:28,919
Speaker 1: the sort of another limiting factor here?

683
00:39:30,960 --> 00:39:34,400
Speaker 5: I believe they are. I believe the private insurance industry

684
00:39:34,480 --> 00:39:38,160
Speaker 5: adds zero value to the United States healthcare system almost that.

685
00:39:38,239 --> 00:39:40,920
Speaker 5: I mean that may slightly overstate it, but it's close

686
00:39:40,960 --> 00:39:43,760
Speaker 5: to zero in my book, and I really don't believe

687
00:39:43,760 --> 00:39:46,480
Speaker 5: insurance companies ought to be the ones making decisions about

688
00:39:46,680 --> 00:39:48,120
Speaker 5: what medical care is appropriate.

689
00:39:48,960 --> 00:39:50,920
Speaker 2: I notice they're in the video. You have a really

690
00:39:51,000 --> 00:39:53,600
Speaker 2: nice looking microphone? Is that a musical? Is that a

691
00:39:53,640 --> 00:39:55,240
Speaker 2: microphone for recording music?

692
00:39:55,600 --> 00:39:57,400
Speaker 5: Yeah? This is the one I uh, this is the

693
00:39:57,400 --> 00:39:58,000
Speaker 5: one I sing on.

694
00:39:58,160 --> 00:40:00,200
Speaker 2: It's it's first of all, you sound good, but it

695
00:40:00,239 --> 00:40:04,279
Speaker 2: also looks a lot cooler than the typical microphone that

696
00:40:04,320 --> 00:40:06,560
Speaker 2: are that our guests to is do you do you?

697
00:40:06,640 --> 00:40:08,640
Speaker 2: Are you still? Are you still playing much music?

698
00:40:09,120 --> 00:40:11,600
Speaker 5: I do, but but thankfully now it's just for fun

699
00:40:11,680 --> 00:40:15,600
Speaker 5: not for money, which is a much more comfortable place

700
00:40:15,600 --> 00:40:16,680
Speaker 5: for it to live in my life.

701
00:40:16,719 --> 00:40:21,320
Speaker 2: Are you do you think it all about AI generated music?

702
00:40:21,760 --> 00:40:24,759
Speaker 2: And uh, the effect that that's going to have on musicians.

703
00:40:25,200 --> 00:40:27,279
Speaker 2: I feel like a lot of musicians, like the ones

704
00:40:27,400 --> 00:40:29,400
Speaker 2: that I follow on Instagram, is there have a lot

705
00:40:29,400 --> 00:40:30,359
Speaker 2: of anxiety about this.

706
00:40:32,000 --> 00:40:36,080
Speaker 5: There is anxiety, And look, I mean it's really hard

707
00:40:36,239 --> 00:40:39,720
Speaker 5: to make a living as a musician now It's always

708
00:40:39,719 --> 00:40:42,920
Speaker 5: been really hard, and you know, I can't imagine what

709
00:40:42,960 --> 00:40:45,880
Speaker 5: the lifestyle was of a loot player in George the

710
00:40:45,920 --> 00:40:49,600
Speaker 5: Second Royal Court or something but you know, it's a

711
00:40:49,719 --> 00:40:53,680
Speaker 5: tough business and it is scary when new technology comes

712
00:40:53,680 --> 00:40:55,879
Speaker 5: on the scene that might change the way you make

713
00:40:55,960 --> 00:40:58,480
Speaker 5: money as an artist. I live through that with Spotify,

714
00:40:58,520 --> 00:41:01,840
Speaker 5: people were terrified of it, and you know, fortunately what

715
00:41:01,960 --> 00:41:02,920
Speaker 5: it did.

716
00:41:02,920 --> 00:41:06,200
Speaker 2: Over done what you did at long Spotify and then

717
00:41:06,239 --> 00:41:08,200
Speaker 2: hedge their own risk to it. But keep going.

718
00:41:08,320 --> 00:41:13,680
Speaker 5: No, but looks spot Spotify by multiples increased the total

719
00:41:13,760 --> 00:41:16,479
Speaker 5: revenue of the recorded music business, which was the goal.

720
00:41:17,000 --> 00:41:21,880
Speaker 5: So mission accomplished. Now, look, AI is going to make music,

721
00:41:22,440 --> 00:41:27,480
Speaker 5: and I think like all creative people, like journalists, like investors,

722
00:41:27,520 --> 00:41:30,799
Speaker 5: everyone's going to think about how they can use it

723
00:41:30,920 --> 00:41:34,040
Speaker 5: to be more effective, have more leverage, have a cooler output.

724
00:41:34,320 --> 00:41:38,680
Speaker 5: I mean, I have very little doubt that artists are

725
00:41:38,680 --> 00:41:43,040
Speaker 5: going to do unbelievably cool and original stuff with AI tools,

726
00:41:43,040 --> 00:41:46,400
Speaker 5: and it's already happening, and for whatever reason, I have

727
00:41:46,560 --> 00:41:49,359
Speaker 5: very little trepidation that they're going to be put out

728
00:41:49,360 --> 00:41:54,200
Speaker 5: of business because I think ultimately music is communication and.

729
00:41:55,440 --> 00:41:57,439
Speaker 2: Real quickly on that when you talk about like doing

730
00:41:57,520 --> 00:42:00,040
Speaker 2: unbelievably cool things with music. So I see in the

731
00:42:00,040 --> 00:42:03,840
Speaker 2: background you have a piano, for example, and one of

732
00:42:03,920 --> 00:42:06,799
Speaker 2: the things when I think about AI music is and

733
00:42:06,920 --> 00:42:09,440
Speaker 2: actually I think, like for example, the founder of Suno

734
00:42:09,440 --> 00:42:12,000
Speaker 2: and some of these other AI music companies have talked

735
00:42:12,000 --> 00:42:14,640
Speaker 2: about this is like, well, music, learning to play instruments

736
00:42:14,680 --> 00:42:18,200
Speaker 2: is really hard, and therefore, can we separate in some

737
00:42:18,280 --> 00:42:21,399
Speaker 2: way the craft of music, the hours that someone has

738
00:42:21,440 --> 00:42:24,399
Speaker 2: to spend just doing scales on the piano before they

739
00:42:24,440 --> 00:42:27,360
Speaker 2: can compose something. Maybe you could what wouldn't it be

740
00:42:27,440 --> 00:42:30,799
Speaker 2: nice if we could just have amazing, beautiful piano sonatas

741
00:42:30,880 --> 00:42:34,760
Speaker 2: without ever having had both put in those thousands of hours.

742
00:42:34,800 --> 00:42:37,760
Speaker 2: You know, Mary had a little lamb and then so forth.

743
00:42:38,000 --> 00:42:40,640
Speaker 2: But it does raise the question to my mind of

744
00:42:40,960 --> 00:42:45,279
Speaker 2: whether one can create great art if they never had

745
00:42:45,320 --> 00:42:47,560
Speaker 2: to learn the craft.

746
00:42:48,600 --> 00:42:54,560
Speaker 5: I think the nuance with which one can communicate through

747
00:42:54,719 --> 00:43:00,840
Speaker 5: music is a function of how many options you perceive.

748
00:43:02,480 --> 00:43:06,120
Speaker 5: In other words, if you know the piano inside out,

749
00:43:07,200 --> 00:43:11,040
Speaker 5: you're aware of so many creative choices that are at

750
00:43:11,120 --> 00:43:15,560
Speaker 5: your disposal at any given moment. And if your ability

751
00:43:15,560 --> 00:43:19,880
Speaker 5: to express yourself is squeezed down to what you can

752
00:43:19,920 --> 00:43:24,480
Speaker 5: put into a natural language prompt, now those musical ideas

753
00:43:24,520 --> 00:43:28,080
Speaker 5: are having to pass through the medium of language to

754
00:43:28,160 --> 00:43:35,560
Speaker 5: be realized, and that inherently erodes the resolution and the

755
00:43:35,600 --> 00:43:38,120
Speaker 5: expansiveness with which you can express yourself.

756
00:43:38,680 --> 00:43:40,600
Speaker 1: I feel like there's a danger here that you go

757
00:43:40,719 --> 00:43:43,920
Speaker 1: off on a big orality tangent and whether ideas can

758
00:43:43,960 --> 00:43:45,879
Speaker 1: exist without words and things like that.

759
00:43:46,160 --> 00:43:49,400
Speaker 2: No, but I do think this that answered very deciightful, Like,

760
00:43:49,440 --> 00:43:52,120
Speaker 2: can you actually create great piano music if you don't

761
00:43:52,120 --> 00:43:53,840
Speaker 2: know the limits of what the piano can do and

762
00:43:53,840 --> 00:43:57,960
Speaker 2: if you're only trying to describe in language, make this

763
00:43:58,040 --> 00:44:00,480
Speaker 2: beautiful sonata? I think that's very tough. But I thought

764
00:44:00,480 --> 00:44:01,480
Speaker 2: that answer made last time.

765
00:44:02,080 --> 00:44:02,279
Speaker 3: DA.

766
00:44:02,360 --> 00:44:04,279
Speaker 1: We're gonna have to wrap it up soon. I have

767
00:44:04,400 --> 00:44:06,759
Speaker 1: one last question, and I'm gonna kind of I'm gonna

768
00:44:06,760 --> 00:44:09,279
Speaker 1: put you on the spot. Can you can you sing

769
00:44:09,320 --> 00:44:12,440
Speaker 1: a little odd lot song for us? Like three bars

770
00:44:12,480 --> 00:44:14,560
Speaker 1: of an odd lot song? I don't care if you

771
00:44:14,640 --> 00:44:18,160
Speaker 1: generate it with you know, I guess Gemini now, but.

772
00:44:18,480 --> 00:44:22,320
Speaker 5: Do you think you could Let's see? I mean, oh wow,

773
00:44:23,600 --> 00:44:27,719
Speaker 5: I'm gonna turn this. Let's see here.

774
00:44:27,719 --> 00:44:29,680
Speaker 2: This is really cool. Yeah, if you came, you are

775
00:44:29,680 --> 00:44:33,319
Speaker 2: watching the video. So he's moving his microphone, he's moving

776
00:44:33,360 --> 00:44:35,160
Speaker 2: his microphone to his keyboard.

777
00:44:35,360 --> 00:44:37,239
Speaker 5: Okay, can you see great?

778
00:44:37,280 --> 00:44:38,160
Speaker 2: Yeah? Go for it?

779
00:44:39,640 --> 00:44:41,000
Speaker 5: Okay, We're gonna try.

780
00:44:44,040 --> 00:44:50,480
Speaker 6: And it's all about it's all about It's all about Tracy,

781
00:44:51,840 --> 00:44:57,239
Speaker 6: It's all about it's all about it's all about Joe.

782
00:44:58,520 --> 00:44:59,759
Speaker 5: How's that pretty good?

783
00:45:00,239 --> 00:45:01,560
Speaker 2: You have a great voice.

784
00:45:01,680 --> 00:45:02,239
Speaker 3: Yeah it is.

785
00:45:02,680 --> 00:45:05,640
Speaker 2: Do you ever want to compose an outro song for uh? Yeah,

786
00:45:05,680 --> 00:45:06,640
Speaker 2: something like that?

787
00:45:06,800 --> 00:45:08,680
Speaker 5: Oh, I would love to. I'm I'm. I am the

788
00:45:08,719 --> 00:45:13,279
Speaker 5: composer of two or three podcast theme songs. And I

789
00:45:13,280 --> 00:45:15,719
Speaker 5: have to say, I love your guys theme music. It

790
00:45:16,480 --> 00:45:18,719
Speaker 5: gets me excited. And I got to end on this

791
00:45:18,960 --> 00:45:21,520
Speaker 5: for you guys. You know, in high school the reason

792
00:45:21,560 --> 00:45:23,319
Speaker 5: I got into investing. In high school, I was an

793
00:45:23,360 --> 00:45:25,879
Speaker 5: economics nerd. Oh yeah, I heard and my.

794
00:45:25,800 --> 00:45:28,640
Speaker 1: Hobby, we heard that you actually wrote like some a

795
00:45:28,719 --> 00:45:31,480
Speaker 1: paper that won like a prize from the FED or

796
00:45:31,520 --> 00:45:32,319
Speaker 1: something like that.

797
00:45:32,719 --> 00:45:36,440
Speaker 5: So the Federal Reserve had this nerd competition they sponsored

798
00:45:36,480 --> 00:45:39,000
Speaker 5: called FED Challenge. And I was the captain of my

799
00:45:39,080 --> 00:45:41,200
Speaker 5: high school team one year and we got to DC

800
00:45:41,320 --> 00:45:44,440
Speaker 5: and we we saw Green Span walk out with his

801
00:45:44,600 --> 00:45:48,600
Speaker 5: wizened face and hands. And anyways, if I had had

802
00:45:48,680 --> 00:45:50,680
Speaker 5: odd lots to listen to in high school, man, I

803
00:45:50,680 --> 00:45:53,040
Speaker 5: would have been in heaven because you guys touch on

804
00:45:53,560 --> 00:45:56,880
Speaker 5: so much interesting stuff, and this just has to be

805
00:45:56,920 --> 00:46:00,520
Speaker 5: the most exciting thing for young people to experience in

806
00:46:00,640 --> 00:46:03,800
Speaker 5: order to get turned onto business and economics and finance

807
00:46:03,880 --> 00:46:08,320
Speaker 5: and recognize these aren't just boring, you know, staid topics.

808
00:46:08,320 --> 00:46:09,160
Speaker 5: They're fascinating.

809
00:46:09,280 --> 00:46:11,080
Speaker 1: Thank you for saying that. I really appreciate it, and

810
00:46:11,080 --> 00:46:13,040
Speaker 1: also thank you for singing for us. I think that

811
00:46:13,080 --> 00:46:16,640
Speaker 1: was an all thoughts first. Yeah, yeah, well on the spot.

812
00:46:16,680 --> 00:46:21,879
Speaker 1: I know we've had merl Hazard the country Singing Economist

813
00:46:21,920 --> 00:46:24,080
Speaker 1: on before, but that was fantastic day.

814
00:46:24,120 --> 00:46:25,879
Speaker 3: Wallack, thank you so much for coming on the show.

815
00:46:25,960 --> 00:46:28,399
Speaker 3: Really appreciate it. Thanks you, guys, than for that was great.

816
00:46:40,719 --> 00:46:41,680
Speaker 3: That was really interesting.

817
00:46:41,719 --> 00:46:43,600
Speaker 2: Joe, that was super fun. He was great.

818
00:46:43,800 --> 00:46:46,920
Speaker 1: He's also pretty good at you know. I know again

819
00:46:46,960 --> 00:46:49,440
Speaker 1: he said it was tenuous, but the through line from

820
00:46:49,719 --> 00:46:51,839
Speaker 1: music to biotech kind of makes sense.

821
00:46:52,080 --> 00:46:53,960
Speaker 2: I think it makes a lot of sense. And the

822
00:46:54,160 --> 00:46:57,919
Speaker 2: especially the fact that you know these are extreme. These

823
00:46:57,920 --> 00:47:01,600
Speaker 2: are all startup investing. We know, you know, there's this

824
00:47:01,680 --> 00:47:05,360
Speaker 2: power law phenomenon where one of your twenty portfolio company

825
00:47:05,400 --> 00:47:06,160
Speaker 2: is going to make all the money.

826
00:47:06,239 --> 00:47:08,239
Speaker 3: Yeah, the lottery ticket, but you.

827
00:47:08,200 --> 00:47:11,000
Speaker 2: Know, like biotech is like lottery tickets upon lottery tickets

828
00:47:11,040 --> 00:47:13,800
Speaker 2: there's so much success uncertainty.

829
00:47:14,200 --> 00:47:16,439
Speaker 1: There's so much with lower payouts as.

830
00:47:16,400 --> 00:47:19,719
Speaker 2: Lower payouts, there's so much success uncertainty. There's so much

831
00:47:19,760 --> 00:47:23,800
Speaker 2: time that elapses between the initial work and where you

832
00:47:23,840 --> 00:47:26,600
Speaker 2: see if there's any signals of traction. It does feel

833
00:47:26,600 --> 00:47:29,600
Speaker 2: a lot like the uncertainty that exists in the music

834
00:47:29,640 --> 00:47:33,279
Speaker 2: industry and selecting which of these hundred bands that all

835
00:47:33,320 --> 00:47:36,040
Speaker 2: sound great and they're all really talented, actually has what

836
00:47:36,120 --> 00:47:38,960
Speaker 2: it takes to be a commercial hit. A lot of parallels.

837
00:47:39,040 --> 00:47:42,520
Speaker 1: Yeah, I thought that the dinosaur bias point was an

838
00:47:42,520 --> 00:47:45,520
Speaker 1: interesting one as well, because you can imagine, like again

839
00:47:45,600 --> 00:47:48,799
Speaker 1: to the timeline point, you kind of have to be

840
00:47:48,880 --> 00:47:52,160
Speaker 1: old to have any success in the industry historically, just

841
00:47:52,160 --> 00:47:54,680
Speaker 1: because it can take you know, a decade to get

842
00:47:54,960 --> 00:47:57,600
Speaker 1: a particular drug to market, so you don't have that

843
00:47:57,719 --> 00:48:01,400
Speaker 1: much opportunity to have you know, those wins unless you

844
00:48:01,440 --> 00:48:02,680
Speaker 1: get old and.

845
00:48:02,640 --> 00:48:05,560
Speaker 2: There's no shortage. There's no you know, there may be

846
00:48:05,680 --> 00:48:08,560
Speaker 2: regulatory things that can be done, but fundamentally, if you

847
00:48:08,600 --> 00:48:10,520
Speaker 2: want to know whether something works, and if you want

848
00:48:10,520 --> 00:48:12,520
Speaker 2: to know whether this drug is going to kill people

849
00:48:12,520 --> 00:48:14,920
Speaker 2: who take it or not, and whether it's safe or not,

850
00:48:15,200 --> 00:48:17,760
Speaker 2: there is no substitute for doing a test and seeing

851
00:48:17,920 --> 00:48:21,880
Speaker 2: what happens. And to your point or to your observation

852
00:48:21,880 --> 00:48:24,719
Speaker 2: about the dinosaurs, like I do think that lots of

853
00:48:24,800 --> 00:48:28,280
Speaker 2: people have this fantasy that anytime there is a legacy

854
00:48:28,320 --> 00:48:30,720
Speaker 2: industry of any sort, that if you just got twenty

855
00:48:30,760 --> 00:48:33,000
Speaker 2: one year olds from Stanford in the same room.

856
00:48:32,960 --> 00:48:34,800
Speaker 3: They gave them a garage to work out.

857
00:48:34,680 --> 00:48:36,480
Speaker 2: Garage, that they would do it a lot better than

858
00:48:36,520 --> 00:48:41,000
Speaker 2: the veterans. That was the Doge premise, and Doge doesn't

859
00:48:41,040 --> 00:48:41,800
Speaker 2: exist anymore.

860
00:48:41,880 --> 00:48:44,000
Speaker 3: So yeah, shall we leave it there.

861
00:48:44,160 --> 00:48:44,919
Speaker 2: Let's leave it there.

862
00:48:45,120 --> 00:48:47,400
Speaker 1: This has been another episode of the All Thoughts podcast.

863
00:48:47,480 --> 00:48:50,760
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway.

864
00:48:50,400 --> 00:48:52,400
Speaker 2: And I'm Joe wasn't Thal. You can follow me at

865
00:48:52,400 --> 00:48:54,719
Speaker 2: the Stalwart, follow our guesst d A Wallack He's at

866
00:48:54,800 --> 00:48:58,280
Speaker 2: d A Wallack. Follow our producers Carmen Rodriguez at Carmen

867
00:48:58,400 --> 00:49:01,920
Speaker 2: armand Dashel Bennett a Dashboy, and kill Brooks at Kilbrooks.

868
00:49:02,120 --> 00:49:04,840
Speaker 2: From our Oddlots content, go to Bloomberg dot com slash

869
00:49:04,920 --> 00:49:07,719
Speaker 2: odd Lots were the daily newsletter and all of our episodes,

870
00:49:08,000 --> 00:49:09,799
Speaker 2: and you can chat about all of these topics. Twenty

871
00:49:09,880 --> 00:49:13,640
Speaker 2: four seven in our discord Discord dot gg slash online.

872
00:49:13,760 --> 00:49:16,120
Speaker 1: And if you enjoy Odd Lots, if you want us

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Speaker 3: Thanks for listening, oh