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Speaker 1: Pushkin. Welcome back to Risky Business, a show about making

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Speaker 1: better decisions. I'm Maria Kanikova, and today my co host

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Speaker 1: Nate Silver is gallivanting in Norway, I believe, and so

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Speaker 1: I have an amazing guest co host, an old, dear

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Speaker 1: friend of mine, brilliant writer, incredible thinker, David Epstein. David

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Speaker 1: is the author of two international bestsellers of the sports

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Speaker 1: Gene and Range Folks Are Incredible, highly recommend and a

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Speaker 1: book that will be out next year, Inside the Box.

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Speaker 1: And we'll have him back on when that comes out.

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Speaker 1: And I'm really glad to have him here today to

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Speaker 1: talk about a topic that he knows a lot of

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Speaker 1: about to day in the show, our first segment is

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Speaker 1: going to be about genetics and designer babies and the

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Speaker 1: ethics of it, the implications of it, what kind of

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Speaker 1: world we want to live in, and after that we'll

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Speaker 1: be talking about a different kind of determinism for what

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Speaker 1: ends up happening to you in your life, which is

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Speaker 1: does your name matter? Welcome to the show, David, Thank

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Speaker 1: you so much for being my co host today.

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Speaker 2: Nate's lost my gain. I'm glad to be here. Thanks

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Speaker 2: for inviting me.

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Speaker 1: Of course, of course. All right, So first up, we're

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Speaker 1: going to talk a little bit about genetics. So there

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Speaker 1: is a new startup, Orchid, that's been in the news,

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Speaker 1: and the Orchid pitch is that we can help you

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Speaker 1: determine all sorts of genetic things about your embryos so

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Speaker 1: that you can choose one that avoids the risk of

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Speaker 1: certain diseases, et cetera, et cetera. They are doing different

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Speaker 1: sources of testing, different types of testing. They don't really

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Speaker 1: their client lists, but it has been said that a

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Speaker 1: lot of them are among the elites of Silicon Valley

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Speaker 1: tech entrepreneurs, people who are kind of the movers and

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Speaker 1: shakers of the modern world. So first, David, what do

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Speaker 1: you just think in general about the whole concept of

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Speaker 1: being able to determine You know, you have tons of embryos,

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Speaker 1: and yeah, we can pick the ones that we want

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Speaker 1: based on certain factors. And by the way, like little caveat,

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Speaker 1: people have been doing this in terms of disease for

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Speaker 1: multiple years now. Right, if we can test for a

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Speaker 1: disease risk, if I have a family risk, for instance,

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Speaker 1: of schizophrenia something like that, we can try to mitigate that.

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Speaker 1: Somehow schizophrenia was actually a really bad one to choose,

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Speaker 1: because that's a polygrotic, very difficult one. But that's the

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Speaker 1: general idea originally as we as we have just said,

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Speaker 1: this goes further. So what are your initial thoughts on

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Speaker 1: something like that?

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Speaker 2: Yeah, in a very broad way, by the way, I

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Speaker 2: think this get it's an issue where like everyone's talking

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Speaker 2: about how AI is going to change our lives, and

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Speaker 2: I've thought genetics and gene editing and genetic selection in

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Speaker 2: the long term have the potential to more fundamentally change

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Speaker 2: our lives than AI does. But I think the issue

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Speaker 2: I don't know a lot about Orchid. I mean I

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Speaker 2: looked at a very little bit about them specifically, so

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Speaker 2: I don't know all the services they do. But obviously

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Speaker 2: they're emphasizing the health stuff, which I think is a

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Speaker 2: matter of degree difference from what people are already doing. Right, Like,

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Speaker 2: people already screen for chromosomal abnormalities, you know, down syndrome

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Speaker 2: being a familiar one, and then they have the choice

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Speaker 2: of whether or not to do anything about that or not.

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Speaker 2: Nobody's compelled so to the extent that what they're doing

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Speaker 2: is it seems that they have a technology that allows

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Speaker 2: them to amplify DNA really well, so they really really

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Speaker 2: early after conception, they can get a few cells, you

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Speaker 2: get a reliable DNA signal from it, and screen for

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Speaker 2: a whole bunch of potential diseases. So that I think

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Speaker 2: is just a difference in degree of things that are

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Speaker 2: already going on. And as someone who you know, I

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Speaker 2: got off of training to be a scientist and got

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Speaker 2: into journalism because I was a competitive runner and had

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Speaker 2: a training partner who died at the end of a

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Speaker 2: race from a genetic condition. So the idea that we

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Speaker 2: could prevent stuff from that, you know, these genetic mutations

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Speaker 2: that we know of that that like rip through families

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Speaker 2: like a forest fire. I think I think that's a

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Speaker 2: good option for parents to have. But I think there

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Speaker 2: are a lot of more difficult questions, right, Like you

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Speaker 2: mentioned polygenic traits, so a lot of the monogenic stuff

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Speaker 2: is more is easier. In some cases, you see a

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Speaker 2: genetic variant, you know it causes some disease, you can select,

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Speaker 2: you can take it out right people, you know, if

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Speaker 2: someone there may be moral questions about dispensing with the embryos,

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Speaker 2: but that's the case for IVF in general anyway. So

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Speaker 2: I think the I think the reason that this is

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Speaker 2: pretty controversial probably two reasons. One is that it will

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Speaker 2: be much you know, it will be wealthy people that

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Speaker 2: adopt it, right, So then you have this question of

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Speaker 2: are we like bifurcating the world into people who can

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Speaker 2: prevent diseases in their children and people who can't because

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Speaker 2: they have money. So that's one question, and that just

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Speaker 2: feels icky, right when it comes to health, like, no

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Speaker 2: nobody feels you don't feel as ichy about wealthy people

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Speaker 2: having nicer cars as you do about being able to

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Speaker 2: be healthy. But then the kind of third rail, I

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Speaker 2: think is when you start selecting for things that are

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Speaker 2: more traits than diseases, right, like intelligence or athleticism or

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Speaker 2: whatever it is. And in those cases, I think up

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Speaker 2: to now it hasn't been that feasible because those things,

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Speaker 2: unlike some diseases, most things are not caused by one gene.

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Speaker 2: Most things are caused by all sorts of parts of

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Speaker 2: your genes are the parts that code for specific proteins.

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Speaker 2: And now we know like maybe every single gene and

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Speaker 2: a bunch of other parts of your DNA that aren't

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Speaker 2: even genes that interact to create like you know, even

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Speaker 2: how tall you are, maybe every single gene that has

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Speaker 2: some influence in that, each one in a little way.

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Speaker 2: And it hasn't been very feasible since we have no

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Speaker 2: idea what most genes do to actually select for that stuff.

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Speaker 2: But there's now some work where you do this thing

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Speaker 2: called polygenic like gene scores. We have a huge database

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Speaker 2: a huge number of people, and you've sequenced their genomes

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Speaker 2: and you have their health information and you can kind

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Speaker 2: of score give them a score based on a large

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Speaker 2: number of genes that you may not even know what

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Speaker 2: those genes do. But this configuration looks like it might

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Speaker 2: change someone's cognitibility a little or their height a little bit,

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Speaker 2: or some other trade about them. And that's where you

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Speaker 2: get into these things that are not preventing someone from

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Speaker 2: dropping dead, but things that would just be nice to

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Speaker 2: have or bigger advantages for your child. And so I

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Speaker 2: think that that's like the third rail, right. So I

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Speaker 2: think the reason that it's so controversial is that people

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Speaker 2: see it as like the first domino to fall on

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Speaker 2: route to that which is something that is now you know,

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Speaker 2: even though I think if you selected for a lot

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Speaker 2: of those polygenic traits you'd only make a tiny difference.

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Speaker 2: But if you do that generation after generation after generation,

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Speaker 2: like that's how breeding works.

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Speaker 1: Yeah, No, for sure, that that is exactly how breeding works.

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Speaker 1: And you know, I think you've raised a few really

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Speaker 1: interesting points. One that we still like, we really don't

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Speaker 1: know how this shit works a lot of the time, right,

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Speaker 1: And sometimes you think that you might be selecting for

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Speaker 1: something really really good, but you are also like it

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Speaker 1: comes with a trade off, right, that you just do

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Speaker 1: not know about, right. Sometimes that is that's even true

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Speaker 1: of diseases, right, there are some things that make you

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Speaker 1: susceptible to one disease but are protective against other things,

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Speaker 1: and we don't actually know all of those genetic interactions,

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Speaker 1: that's right.

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Speaker 2: I mean a classic example, right, is that some of

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Speaker 2: the genes that cause sickle cell trade and sickle cell

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Speaker 2: anemia are protective against malaria, which is why people of

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Speaker 2: like recent West African descent have those that condition a lot,

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Speaker 2: even if they now live in the United States, for example.

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Speaker 1: Yep, Yeah, that was that was the one that I

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Speaker 1: was thinking. That is a very classic one that we

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Speaker 1: know but now we know, right, but we but we

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Speaker 1: didn't know before, So that I think that that's part

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Speaker 1: of it, right, that we a lot of this stuff

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Speaker 1: is just incredibly complicated. But I do think that like

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Speaker 1: once some is promising, okay, you know what, like I'll

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Speaker 1: make your kid smarter and more likely to be a

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Speaker 1: star athlete and more likely to be Mozart or whatever

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Speaker 1: it is, that there are ethical things on a few levels. One,

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Speaker 1: there's like the ethical the ethics of the promise right

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Speaker 1: of taking money for something that was something like schizophrenia. Right,

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Speaker 1: This was the example I used at the beginning, and

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Speaker 1: I said, this is actually a very bad example, because

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Speaker 1: schizophrenia is something that is determined by so many different factors.

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Speaker 1: We don't know really what causes it, and it's not

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Speaker 1: going to manifest for you know, eighteen years, nineteen years,

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Speaker 1: twenty years. It usually happens in the late teens, early twenties.

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Speaker 1: And so in the meantime, this company can be making

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Speaker 1: claims that we're screening for these things, not actually be

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Speaker 1: doing it successfully, and we have no way of knowing.

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Speaker 1: So there's like the ethics of promising something that you're

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Speaker 1: not really sure about. But then also if we can

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Speaker 1: like let's assume we can do that. I do think

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Speaker 1: that there are concerns both with the haves and have nots,

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Speaker 1: but also with the like I'm selecting now for blue

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Speaker 1: eyes and blonde hair, and I want someone who's a

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Speaker 1: little bit taller. And it's so funny to me, you know,

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Speaker 1: there are people who are now quoting Gatika the movie

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Speaker 1: as this wonderful thing, like, oh, yeah, we're making Gatica babies.

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Speaker 1: So I was like, that movie was not was not positive. Yeah,

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Speaker 1: this kind of technology that was a dystopia, like that

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Speaker 1: is something that we absolutely don't want.

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Speaker 2: That's like Selman Kahn's book Brave New Words, which is

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Speaker 2: obviously referencing Brave New World, which I didn't really think

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Speaker 2: of as kind of a good connotation for children in

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Speaker 2: their schooling necessarily.

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Speaker 1: Yeah, that's that's not a book that I have very

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Speaker 1: positive associations with in terms of the kind of future

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Speaker 1: we want to be living in.

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Speaker 2: Totally, And I think, you know to your point, like

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Speaker 2: I don't want to compare this company in particular to

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Speaker 2: twenty three and me, but like twenty three in me,

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Speaker 2: I was since I was writing a book about genetics,

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Speaker 2: I signed up for a bunch of direct consumer genetic

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Speaker 2: testing companies in the early days before the FDA was

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Speaker 2: really paying attention to them, so you could get all

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Speaker 2: kinds of information, and like I remember, there'd be say

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Speaker 2: it would say, you know, am I allergic to gluten

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Speaker 2: or something like that, and it would show some of

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Speaker 2: my gene variations compared to the population. It would say, oh, well,

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Speaker 2: people with your genotype, are you know your your ODDS

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Speaker 2: ratio is like one point oh two or something. You know,

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Speaker 2: you're like slightly more So one would be if you

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Speaker 2: have the same risk of getting this thing as the

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Speaker 2: as a typical person, so you're slightly slightly increased risk.

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Speaker 2: And then a month later they'd find some other associated

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Speaker 2: gene variant it'd be like, well, now you're actually a

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Speaker 2: slightly decreased risk. And then there'd be like three more

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Speaker 2: found and they'd be like, you're at elevator risk. He did, right,

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Speaker 2: So this is this question of you don't know what

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Speaker 2: a lot of stuff is doing. And so it was

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Speaker 2: like depended when I checked in if I were at

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Speaker 2: increased or decreased risk. And again that was some pretty

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Speaker 2: unsophisticated stuff. My like motto for genetics is genetics more

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Speaker 2: complicated than we thought, you know, because people used to

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Speaker 2: think we'd have like one hundred thousand genes, and then

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Speaker 2: it turned out we only have like twenty twenty three thousand,

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Speaker 2: you know, and onions have way more DNA than we do.

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Speaker 2: So it's kind of disappointing, like we're not that complications.

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Speaker 1: Onions are smarter than we are.

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Speaker 2: It's no, I mean, who would argue with that. That's

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Speaker 2: why so many people don't like them. They're just jealous.

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Speaker 1: I'm allergic to them. What does that say about my.

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Speaker 2: You're allergic to onions?

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Speaker 1: Yeah, I'm allergic to raw onions, all raw onions, onions, shallots, scallions, chibes.

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Speaker 2: And you shouldn't say stuff like that in public. Your

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Speaker 2: kryptonite is now like out on the internet, right, I know,

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Speaker 2: I know.

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Speaker 1: So this is this is genetic. This is actually something

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Speaker 1: that you can look at in your genes. And my

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Speaker 1: body doesn't have the enzyme that can break them down.

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Speaker 1: So soon as they're cooked, I can eat them, but

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Speaker 1: in any raw form I'll become incredibly incredibly sick. So

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Speaker 1: this is potentially actually, you know, bringing it back to

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Speaker 1: our topic at hand, this is something that Orchid or

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Speaker 1: what or whatever company could potentially test for and then

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Speaker 1: I might not exist because they'd be like, oh my god,

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Speaker 1: onion allergy. No, we can't have this, might not.

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Speaker 2: I mean, I think we can all agree that we

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Speaker 2: would get rid of that embryo f and the onion allergy,

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Speaker 2: for goodness sake, changes the whole conversation.

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Speaker 1: If it that it does, it does?

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Speaker 2: Yeah, but yeah, I mean so I don't want to

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Speaker 2: compare them because I think some of the disease stuff,

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Speaker 2: I think is just a more powerful version of things

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Speaker 2: that people already doing. And and by the way, I mean,

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Speaker 2: I guess that's true to some degree for genetic selection.

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Speaker 2: Like I wrote in article years ago about height in

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Speaker 2: sports for ESPN, and I was like, it turned out

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Speaker 2: that in most almost every uh, like sperm donation clinic,

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Speaker 2: like I couldn't donate because I'm not tall enough. Right,

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Speaker 2: I was a Division one athlete and all this stuff.

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Speaker 2: You know, it did well in school.

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Speaker 1: Wait are you serious?

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Speaker 2: Yeah? Yeah, yeah, yeah absolutely. But so it's like when

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Speaker 2: push comes to show if people do do this stuff, right, yeah,

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Speaker 2: that's crazy. And so I guess the question is, you know,

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Speaker 2: if it becomes widespread though, that's yeah, that's the thing.

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Speaker 1: Well, you know, it's it's interesting that you say one

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Speaker 1: push comes to show because people do already like if

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Speaker 1: you know, so, I don't have kids, so I and

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Speaker 1: I've never gone through IVY fertility treatments, et cetera. But

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Speaker 1: if I were to do that and I was looking

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Speaker 1: for a sperm donor, I could go through binders, you know,

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Speaker 1: of backgrounds of people and choose who I would want, right,

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Speaker 1: and it would say things like you know, height and

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Speaker 1: eye color and all of these things where you went

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Speaker 1: to school, you know, So I'd be like, I want

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Speaker 1: someone smart, I want someone from the IVY League.

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Speaker 2: Did you look at the I'm sure in your college

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Speaker 2: newspaper there were advertisements for egg donors like crazy, right, absolutely,

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Speaker 2: and it would say like and they would advertise and

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Speaker 2: like the Harvard Crimson for a reason, right.

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Speaker 1: Yeah, Oh no, I was hit up for egg donations

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Speaker 1: so many times.

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Speaker 2: But they didn't know thri allerg to onions as they

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Speaker 2: did not they did not.

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Speaker 1: Know maybe the two about No, they don't balance out,

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Speaker 1: do they. The onionology is much worse, I understand, damn.

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Speaker 1: But yeah, that that is absolutely true, and you could

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Speaker 1: have you could make a lot of money right by

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Speaker 1: being an egg donor like that sperm donors don't make

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Speaker 1: that much. Egg donors make a lot of money.

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Speaker 2: I imagine why, but fair unfair pay gap, this pay.

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Speaker 1: Gap be addressed, yes, underpay up gender pay up for sure.

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Speaker 1: So yeah, so these types of things have been available

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Speaker 1: in some way, shape or form for a long time.

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Speaker 1: But I do think that you know, the if this

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Speaker 1: becomes the norm, then it becomes problematic in many respects.

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Speaker 1: And one one other issue that we haven't talked about

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Speaker 1: that people have raised is if this becomes the norm, right,

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Speaker 1: and then people who don't actually have fertility issues, who

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Speaker 1: don't need IVF end up doing this so that they

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Speaker 1: are not kind of the same way that people opt

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Speaker 1: sometimes to have c sections when they don't need them

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Speaker 1: because they don't they don't want to give vaginal birth right.

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Speaker 1: If people then make that choice just simply because they

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Speaker 1: want that selection, that is also kind of an interesting

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Speaker 1: ethical area and has repercussions for a lot of society

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Speaker 1: in the sense that so ORCD and we're not picking

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Speaker 1: on organ it just happens to be one that is

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Speaker 1: like in the news topical for the issue exactly exactly so,

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Speaker 1: but or get specifically charges twenty five hundred dollars per embryo.

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Speaker 1: Now you would be you need to make multiple embryos,

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Speaker 1: and it can become much more expensive if you want

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Speaker 1: other testing, et cetera, et cetera. So there is kind

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Speaker 1: of another There are more issues that keep piling up here,

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Speaker 1: and we'll be back right after this.

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Speaker 2: I don't think that it is particularly difficult to envision

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Speaker 2: a scenario where IVF becomes like basically the norm, because

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Speaker 2: you want to be able to do embryo selection, right.

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Speaker 2: I'm not saying that I think there'd ever be an instance,

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Speaker 2: well at least in the foreseeable future, that where everybody

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Speaker 2: would make that choice, because there are a lot of different,

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Speaker 2: obviously ethical considerations. But I don't think it's that hard

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Speaker 2: to see that, And I think you could end up

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Speaker 2: in a kind of collective trap where because some people

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Speaker 2: are doing it, you know, and if it turns into

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Speaker 2: real advantage, maybe there's pressure for insurance companies to then

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Speaker 2: cover it or or or you know, just for moral

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Speaker 2: reasons like it shouldn't be limited access. So I actually

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Speaker 2: I don't know which way we're going to go, but

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Speaker 2: I don't think it's like sci fi to imagine that

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Speaker 2: IVF would become like the standard mode of procreation.

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Speaker 1: Yeah, and people say, you know, oh, but it's still

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Speaker 1: a personal choice. Well, what you said about pressure, kind

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Speaker 1: of pure pressure to do it, I think is actually huge.

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Speaker 1: So when I just graduated from college, moved to New York,

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Speaker 1: wanted to be a writer. It's really hard to make

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Speaker 1: a living as a writer. You don't make a lot

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Speaker 1: of money. So I tutored. I did a lot of

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Speaker 1: private tutoring for very wealthy families, and you know, people

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Speaker 1: that becomes something that like, if you don't do right,

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Speaker 1: you are disadvantaging your child and you're a bad parent.

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Speaker 1: At that point, everyone expects that, like, oh, well, you'll

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Speaker 1: get tutoring in all of these you'll get USA TEA prep,

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Speaker 1: you'll get this, you'll get that. And if you're one

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Speaker 1: of the people who don't get it, then you're like, oh,

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Speaker 1: my parents don't care enough right that they're not giving

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Speaker 1: me that extra step. Now, by the way, I never

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Speaker 1: got any sort of tutoring when I was a kid.

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Speaker 1: I didn't get SAT prep or anything like that. But

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Speaker 1: it was I was shocked at the fact that some

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Speaker 1: of the kids really did need it and others like

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Speaker 1: it was just something that I was being paid basically

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Speaker 1: to act as a tutor, and to kind of be

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Speaker 1: a tutor one technically, I was like, this is a

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Speaker 1: smart kid who could do this by themselves, but no,

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Speaker 1: because this is something you're required to do. And so

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Speaker 1: I think that people might feel pressure to select even

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Speaker 1: if we don't know if it works right, just on

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Speaker 1: the off chance that it might work, and that this

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Speaker 1: sort of stuff might be testable and that you might

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Speaker 1: be giving your kids minute advantages. Now, one of the

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Speaker 1: themes of risky business is that minute advantages matter, right,

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Speaker 1: small percentages matter. As a poker player, you know that

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Speaker 1: if you get an extra one percent edge, that's huge.

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Speaker 1: So even if we know that things like intelligence, you know,

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Speaker 1: things like height, that they're determined by so many different

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Speaker 1: variables that nothing is well with few exceptions. Nothing is

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Speaker 1: all genetics like one gene. And even if you can

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Speaker 1: eke out an extra percentage point, right, an extra tiny

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Speaker 1: bit for your kid, that could be huge. And are

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Speaker 1: you negligent if you don't make that choice? If you say,

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Speaker 1: you know what, I just want to have sex to

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Speaker 1: see what happens.

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Speaker 2: So quaint, yes, I think, and I think you know,

364
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Speaker 2: like parental neuroses is like a bottomless well of spending, right,

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Speaker 2: so there's a lot of business opportunities there. But I

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Speaker 2: do think there is now and I wouldn't have said

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Speaker 2: this not that long ago, that there's real reason to

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Speaker 2: believe that some of these traits could be select could

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Speaker 2: be accurately select for in a small way. Again, that

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Speaker 2: doesn't say anything about are you accidentally then getting something

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Speaker 2: you don't want? Right, There's a lot of unknowns. But

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Speaker 2: I think for the first time some of these other

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Speaker 2: non disease traits, we could load the dice a little

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Speaker 2: a little bit, a little bit, and then over multiple

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Speaker 2: generations that could become more and more and more. But again,

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Speaker 2: I think those are separate from the issue of making

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Speaker 2: the screening for disease better, much earlier, and more extensives.

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Speaker 1: Yeah, no, I think that just to put this out there,

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Speaker 1: I think that the screening for disease is great, you know,

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Speaker 1: I think that that is. I think that's a very

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Speaker 1: good thing.

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Speaker 2: I think that, you know, being I don't know that

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Speaker 2: everyone would agree with that, right because maybe you're still

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Speaker 2: like selecting for.

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Speaker 1: Oh for sure, I think that's less controversial. I think

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Speaker 1: I think that a lot of people won't agree with that, yeah,

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Speaker 1: because they don't think you should be able to select

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Speaker 1: for anything. But you know, I'm an Ashkanazi Jew. Ashkinazi

389
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Speaker 1: Jews have predilections you know, for a lot of different

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Speaker 1: genetic condition and you know, if we can reduce tays acts, right,

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Speaker 1: if we can reduce Huntington's, if we can reduce the

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Speaker 1: risk of some of these really devastating conditions, yeah, that

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Speaker 1: would be amazing, right, I think, I think personally, But

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Speaker 1: you know, related to this, we also do have emerging

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Speaker 1: technologies Crisper, you know, that can edit right, so that

396
00:20:37,004 --> 00:20:40,684
Speaker 1: it's slightly different, right, you're not choosing embryos. But we

397
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Speaker 1: have technology that is becoming better and better where we

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Speaker 1: can actually go into the genome and say, okay, snip, snip,

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Speaker 1: let's let's try to let's try to fix this, and

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Speaker 1: let's try to not literally, by the way, people, you

401
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Speaker 1: don't actually go in with scissors.

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Speaker 2: It's not that different though. That's a that's a pretty

403
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Speaker 2: good analogy.

404
00:21:00,844 --> 00:21:05,884
Speaker 1: But so we have technologies like that that don't require IBF.

405
00:21:06,324 --> 00:21:09,324
Speaker 1: You know, it's it's interesting, like it's very very complicated

406
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Speaker 1: because there are certain things about this that I think

407
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Speaker 1: are amazing. There are certain things that really give me pause,

408
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Speaker 1: and then there are things I actually am kind of

409
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Speaker 1: scared of the unknowns. And I know that, yes, we

410
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Speaker 1: shouldn't be afraid of a technology because we don't know

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Speaker 1: the side effect. But you know, there are certain things

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Speaker 1: like maybe when you are trying to kind of control

413
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Speaker 1: for the schizophrenia cluster genes, you're also getting rid of

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Speaker 1: artistic talent. Just because I say that as like a

415
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Speaker 1: cliche thing that often goes together, but like maybe we're

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Speaker 1: actually getting rid of some good things and some natural

417
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Speaker 1: variation mutations that we have no idea what they do,

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Speaker 1: and they might have existed in this embryo and we

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Speaker 1: just will never know.

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Speaker 2: That's an interesting point on two counts. One. I mean,

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Speaker 2: we know there are things like to give a sort

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Speaker 2: of extreme example, years ago, this was with earlier forms

423
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Speaker 2: of gene therapy, but there were some tests on boys

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Speaker 2: with what I think it's called X linked combined immuno

425
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Speaker 2: deficiency syndrome. People commonly know it as bubble boy syndrome

426
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Speaker 2: because the boy has so it's linked to an X chromosome,

427
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Speaker 2: and because boys only have one, there's not another copy

428
00:22:24,204 --> 00:22:27,244
Speaker 2: to correct it. So some X link diseases are in

429
00:22:27,364 --> 00:22:30,644
Speaker 2: boys and so bubble boy syndrome, meaning they basically have

430
00:22:30,764 --> 00:22:33,444
Speaker 2: no immune system, so they can't go anywhere. They have

431
00:22:33,484 --> 00:22:36,164
Speaker 2: to like live in a bubble. And the gene therapy

432
00:22:37,324 --> 00:22:39,484
Speaker 2: this was a more rudimentary form. It wasn't crisper like

433
00:22:39,604 --> 00:22:42,444
Speaker 2: gene editing, but using a gene created in the lab

434
00:22:42,484 --> 00:22:45,444
Speaker 2: and it gets like shuttled into the cells with a

435
00:22:45,724 --> 00:22:51,524
Speaker 2: deactivated virus. Basically it cured some of them, which is incredible, right,

436
00:22:51,644 --> 00:22:54,204
Speaker 2: but then like a number of them got leukemia. So

437
00:22:54,284 --> 00:22:57,164
Speaker 2: there was like this incredible cure but also this incredibly

438
00:22:57,204 --> 00:23:00,324
Speaker 2: devastating effect, and so you don't know, right, some of

439
00:23:00,404 --> 00:23:02,564
Speaker 2: the stuff that's going on. And to your point about

440
00:23:04,564 --> 00:23:08,204
Speaker 2: that example you mentioned, like schizophrenia and artistic talent, I

441
00:23:08,244 --> 00:23:11,124
Speaker 2: think there have been some studies showing that you don't

442
00:23:11,164 --> 00:23:15,084
Speaker 2: want mental illness to be creative, right, because that incapacitates

443
00:23:15,084 --> 00:23:17,964
Speaker 2: you in all these ways, But that that tends to

444
00:23:18,044 --> 00:23:21,804
Speaker 2: be more in the family of people who are extremely creative.

445
00:23:21,844 --> 00:23:23,844
Speaker 2: And it's like, if there's all these genes that have

446
00:23:23,924 --> 00:23:25,924
Speaker 2: a small effect, some people will have too many of

447
00:23:25,964 --> 00:23:28,044
Speaker 2: them and they'll have mental illness. And some people have

448
00:23:28,084 --> 00:23:29,444
Speaker 2: a bunch of them that make you kind of an

449
00:23:29,524 --> 00:23:32,564
Speaker 2: unusual thinker, but not so many that you're incapacitated. I

450
00:23:32,604 --> 00:23:35,444
Speaker 2: remember some years ago after I wrote about genetics, a

451
00:23:35,884 --> 00:23:39,844
Speaker 2: like a psychiatrist who served Harvard students coming to me

452
00:23:39,884 --> 00:23:42,324
Speaker 2: and saying, like, they definitely are overrepresented, and all these

453
00:23:42,364 --> 00:23:46,444
Speaker 2: conditions that are like borderline mental illness. You know, so

454
00:23:46,604 --> 00:23:48,244
Speaker 2: like you and your peers, you know, it's not just

455
00:23:48,324 --> 00:23:52,124
Speaker 2: your n allergies, like you're weird in other ways that

456
00:23:52,244 --> 00:23:53,524
Speaker 2: might also be useful.

457
00:23:54,164 --> 00:23:57,964
Speaker 1: This also happens obviously with like new drugs, like for instance,

458
00:23:58,124 --> 00:24:00,244
Speaker 1: you were saying the bubble boy, they end up dying

459
00:24:00,284 --> 00:24:02,804
Speaker 1: of leukemia. We know that there were some drugs that

460
00:24:02,844 --> 00:24:05,844
Speaker 1: showed an amazing promise right and then ended up like

461
00:24:06,364 --> 00:24:10,044
Speaker 1: devastating your liver and kidneys and you die or something

462
00:24:10,164 --> 00:24:14,044
Speaker 1: like that, where like oops, we had no idea, or

463
00:24:14,244 --> 00:24:17,844
Speaker 1: you know, causes horrific birth defects or whatever it is.

464
00:24:18,124 --> 00:24:22,964
Speaker 1: Those types of horrible moments, unfortunately, are all too common

465
00:24:23,084 --> 00:24:27,564
Speaker 1: in medical history. But I think the difference there is

466
00:24:27,644 --> 00:24:31,044
Speaker 1: you were trying to cure a person who was incredibly sick,

467
00:24:31,684 --> 00:24:35,284
Speaker 1: and here you have someone who's healthy and potentially in

468
00:24:35,324 --> 00:24:38,524
Speaker 1: the future someone who is perfectly fertile, doesn't need IVF,

469
00:24:38,604 --> 00:24:40,924
Speaker 1: doesn't need any of this, and is choosing to do

470
00:24:41,124 --> 00:24:46,124
Speaker 1: something which might then introduce these sorts of yeah, downstream effects.

471
00:24:46,204 --> 00:24:49,164
Speaker 2: That's a whole you know, like a saying among doctors.

472
00:24:49,204 --> 00:24:51,724
Speaker 2: It's like, it's very hard to make an asymptomatic patient better.

473
00:24:53,244 --> 00:24:55,084
Speaker 2: You know, there are cases where that's true, where you

474
00:24:55,284 --> 00:24:57,364
Speaker 2: catch stuff ahead of time, but as a general rule,

475
00:24:58,084 --> 00:25:03,084
Speaker 2: if someone doesn't need medical intervention, it's usually better to

476
00:25:03,164 --> 00:25:06,164
Speaker 2: avoid medical intervention. So I think you're hitting on an

477
00:25:06,164 --> 00:25:08,364
Speaker 2: important point. I mean, I wonder what do you think,

478
00:25:09,484 --> 00:25:11,604
Speaker 2: Let's say, because we've talked about how there may be

479
00:25:11,724 --> 00:25:13,804
Speaker 2: sort of off target effects that you don't want when

480
00:25:13,844 --> 00:25:15,404
Speaker 2: you try some of this stuff, Let's say we had

481
00:25:15,524 --> 00:25:17,404
Speaker 2: we get it all figured out. So I mean, I

482
00:25:17,404 --> 00:25:18,804
Speaker 2: don't think we're ever going to get it all figured out,

483
00:25:18,804 --> 00:25:21,684
Speaker 2: because this is this complex interplay between all these environmental

484
00:25:21,804 --> 00:25:25,924
Speaker 2: stimulus and genetics. So there's no like to the extent

485
00:25:26,004 --> 00:25:27,404
Speaker 2: that no two lives are the same. We're not going

486
00:25:27,444 --> 00:25:31,924
Speaker 2: to have it all figured out. But theoretically if we did,

487
00:25:32,044 --> 00:25:33,844
Speaker 2: and then it's just a matter of selecting for stuff

488
00:25:33,884 --> 00:25:35,444
Speaker 2: you want the way that you would with like a

489
00:25:35,564 --> 00:25:38,764
Speaker 2: dog breed what then Like then it's like just kind

490
00:25:38,804 --> 00:25:40,884
Speaker 2: of how do we want society to be a question? Right?

491
00:25:40,964 --> 00:25:43,964
Speaker 1: And that's that's that is quite dystopian if we also

492
00:25:44,124 --> 00:25:47,924
Speaker 1: consider that it might be if we pair that with

493
00:25:48,044 --> 00:25:49,884
Speaker 1: what you said at the beginning, right, the divide between

494
00:25:49,924 --> 00:25:52,324
Speaker 1: the haves and have nots, where some people can afford

495
00:25:52,364 --> 00:25:55,564
Speaker 1: this state of the art technology and others can't, and

496
00:25:55,724 --> 00:25:58,284
Speaker 1: so we have a very specific type of person selecting

497
00:25:58,444 --> 00:26:03,324
Speaker 1: for what they want. And yeah, to me, like that

498
00:26:03,484 --> 00:26:06,764
Speaker 1: is dystopian, and that's kind of that's kind of frightening

499
00:26:06,884 --> 00:26:10,684
Speaker 1: because we know from psychology that people will, you know,

500
00:26:10,804 --> 00:26:13,964
Speaker 1: select people who are like them quite often, right, we

501
00:26:14,404 --> 00:26:20,164
Speaker 1: like people who look like us, and it becomes eugenics

502
00:26:20,244 --> 00:26:23,524
Speaker 1: is a bad word for a reason, but it becomes

503
00:26:23,844 --> 00:26:27,684
Speaker 1: very eugenics, like where you are saying, you know, these

504
00:26:27,764 --> 00:26:32,284
Speaker 1: traits are inherently superior. But even I'm actually curious from

505
00:26:32,404 --> 00:26:35,004
Speaker 1: your standpoint of someone who was a Division one athlete

506
00:26:35,244 --> 00:26:39,444
Speaker 1: who knows a lot about sports, Like even with you know,

507
00:26:39,724 --> 00:26:45,324
Speaker 1: sports genes and things that we know are really cool mutations,

508
00:26:45,844 --> 00:26:48,484
Speaker 1: they're sometimes like you will find an athlete who doesn't

509
00:26:48,484 --> 00:26:50,484
Speaker 1: look like other athletes who ends up being the best

510
00:26:50,644 --> 00:26:52,404
Speaker 1: or who ends up discovering kind of a new way

511
00:26:52,444 --> 00:26:56,284
Speaker 1: of doing something that absolutely changes a sport or or

512
00:26:56,284 --> 00:27:01,804
Speaker 1: a way of a way of competing. And are we also,

513
00:27:01,924 --> 00:27:07,564
Speaker 1: like when we select for kind of this one ideal

514
00:27:08,364 --> 00:27:13,084
Speaker 1: that whoever has in their mind, are we potentially homogenizing

515
00:27:13,164 --> 00:27:16,164
Speaker 1: society to the point where that's going Those types of

516
00:27:16,484 --> 00:27:20,044
Speaker 1: breakthroughs are actually going to become quite rare if they

517
00:27:20,124 --> 00:27:20,684
Speaker 1: happen at all.

518
00:27:21,164 --> 00:27:26,124
Speaker 2: Yeah, I mean variation is necessary for both natural selection

519
00:27:26,204 --> 00:27:28,284
Speaker 2: to happen, but I think also artificial selection or whatever

520
00:27:28,284 --> 00:27:30,084
Speaker 2: you want to call it. And I mean maybe artificial

521
00:27:30,124 --> 00:27:34,684
Speaker 2: selection is not the right word, but for new stuff,

522
00:27:35,084 --> 00:27:36,924
Speaker 2: like new ideas and all those things. So I think

523
00:27:36,964 --> 00:27:39,884
Speaker 2: that's way way down the road because there's a ton

524
00:27:39,924 --> 00:27:43,524
Speaker 2: of variation. But if we really start going down this path,

525
00:27:43,884 --> 00:27:45,844
Speaker 2: then I think those are things to think about way

526
00:27:45,884 --> 00:27:48,844
Speaker 2: ahead of time. And you can imagine, you know, I

527
00:27:48,844 --> 00:27:53,804
Speaker 2: think it's already like troubling enough some of the ideals

528
00:27:53,844 --> 00:27:56,644
Speaker 2: of perfection that you have on Instagram or whatever. And

529
00:27:56,764 --> 00:27:59,764
Speaker 2: so if that's then being applied to like all the genomes,

530
00:28:00,364 --> 00:28:03,404
Speaker 2: I think you have started asking questions of like maybe

531
00:28:03,444 --> 00:28:06,364
Speaker 2: there are things where we could be better at certain

532
00:28:06,404 --> 00:28:08,284
Speaker 2: things we know about, but how do we want life

533
00:28:08,364 --> 00:28:10,324
Speaker 2: to be? You know, I think that's an important question

534
00:28:10,404 --> 00:28:14,364
Speaker 2: with technology that should be asked, Like I'm I'm I

535
00:28:14,564 --> 00:28:17,644
Speaker 2: like innovation. I think invention technology is like really cool.

536
00:28:17,684 --> 00:28:20,124
Speaker 2: You know. I started playing with AI immediately as soon

537
00:28:20,164 --> 00:28:23,484
Speaker 2: as I could. At the same time, I don't think,

538
00:28:23,884 --> 00:28:26,644
Speaker 2: you know, we should always just forge a head on

539
00:28:26,724 --> 00:28:29,564
Speaker 2: everything without asking like, how do we want life to be?

540
00:28:29,804 --> 00:28:30,884
Speaker 2: An unfold? Right?

541
00:28:31,524 --> 00:28:35,124
Speaker 1: Yeah, I think that's really important, And I will just

542
00:28:35,204 --> 00:28:40,004
Speaker 1: add one less thing to that, which is that we're

543
00:28:40,124 --> 00:28:45,364
Speaker 1: also ignoring a potentially huge mental health crisis if this

544
00:28:45,764 --> 00:28:47,844
Speaker 1: ends up happening. You know, how do we want life

545
00:28:47,884 --> 00:28:50,244
Speaker 1: to be? Well, what happens if you know, you like

546
00:28:50,404 --> 00:28:53,644
Speaker 1: you're a designer baby that your parents selected these certain

547
00:28:53,684 --> 00:28:56,124
Speaker 1: things for and you feel like you would have liked

548
00:28:56,164 --> 00:28:58,844
Speaker 1: to be something else, You feel like things were chosen

549
00:28:58,964 --> 00:29:02,004
Speaker 1: for you. I mean, I think that, you know, from

550
00:29:02,044 --> 00:29:05,284
Speaker 1: a psychology standpoint, there might be a lot of issues

551
00:29:05,364 --> 00:29:07,364
Speaker 1: with that, and we might have kind of a crisis

552
00:29:07,644 --> 00:29:11,764
Speaker 1: in the generations of children who are subjected to this. So, yeah,

553
00:29:11,844 --> 00:29:13,964
Speaker 1: a lot of things to think about. This is a

554
00:29:14,084 --> 00:29:19,044
Speaker 1: really really complicated thing. You know, it's a really morally, ethically, medically,

555
00:29:19,284 --> 00:29:22,684
Speaker 1: just every single level complicated topic, and I think something

556
00:29:22,724 --> 00:29:24,604
Speaker 1: that people should really give a lot of thought to

557
00:29:25,204 --> 00:29:28,644
Speaker 1: for the long term, because you know, as with much

558
00:29:28,764 --> 00:29:31,284
Speaker 1: so much technology, just because we can doesn't mean we

559
00:29:31,364 --> 00:29:32,204
Speaker 1: should know and.

560
00:29:32,964 --> 00:29:35,164
Speaker 2: Like people, I think it's been pretty good. The discussion

561
00:29:35,164 --> 00:29:38,284
Speaker 2: about AI has been vibrant, and the potential positives and

562
00:29:38,364 --> 00:29:40,844
Speaker 2: the potential negatives, the ethical concerns, et cetera, what we

563
00:29:40,924 --> 00:29:43,764
Speaker 2: want the future to look like. Again, I think genetic

564
00:29:43,804 --> 00:29:46,724
Speaker 2: technology in the long term has a potentially large, much

565
00:29:46,804 --> 00:29:50,484
Speaker 2: larger impact than AI does on fundamental aspects of our life.

566
00:29:50,844 --> 00:29:53,244
Speaker 2: And those conversations have been kind of one note. You know,

567
00:29:53,324 --> 00:29:55,204
Speaker 2: it's every once in a while something like orchid pops

568
00:29:55,284 --> 00:29:58,604
Speaker 2: up and there's a discussion, But I don't think it's

569
00:29:58,684 --> 00:30:02,724
Speaker 2: been diverse and constant to the extent that it has

570
00:30:03,164 --> 00:30:05,764
Speaker 2: with AI, even though I think it's at least equally

571
00:30:05,764 --> 00:30:06,284
Speaker 2: as important.

572
00:30:06,804 --> 00:30:10,444
Speaker 1: Yeah, I think that this is very important note of

573
00:30:10,684 --> 00:30:14,044
Speaker 1: caution and also call to action, that these conversations are

574
00:30:14,084 --> 00:30:21,684
Speaker 1: important and need to be had. Let's take a quick

575
00:30:21,764 --> 00:30:25,404
Speaker 1: break and then we'll talk about a pet psychtopic for

576
00:30:25,804 --> 00:30:29,524
Speaker 1: me and David nominative determinism.

577
00:30:29,164 --> 00:30:31,804
Speaker 2: A much more deterministic aspect of your life than genetics.

578
00:30:39,604 --> 00:30:42,564
Speaker 1: David, for a number of years you and I have

579
00:30:42,764 --> 00:30:48,444
Speaker 1: been exchanging text messages about names that we come up with,

580
00:30:49,324 --> 00:30:51,604
Speaker 1: that we see in the news, and that make us

581
00:30:51,644 --> 00:30:57,724
Speaker 1: both giggle because they are examples of nominative determinism, which

582
00:30:57,804 --> 00:31:02,004
Speaker 1: is a psychology concept which means that your name doesn't

583
00:31:02,124 --> 00:31:06,484
Speaker 1: just matter, but is in some ways deterministic. It determines

584
00:31:07,684 --> 00:31:12,364
Speaker 1: your career path or over jeans, move over jeans. It's

585
00:31:12,444 --> 00:31:15,284
Speaker 1: not the genes that matter, it's the name that matters.

586
00:31:15,764 --> 00:31:19,124
Speaker 1: And you know, the most simplistic example is something like

587
00:31:19,284 --> 00:31:22,284
Speaker 1: someone with the last name Baker is going to go

588
00:31:22,564 --> 00:31:26,084
Speaker 1: on to become a Baker. And this would be, you know,

589
00:31:26,204 --> 00:31:31,364
Speaker 1: a hilarious joke if it didn't happen so incredibly often.

590
00:31:32,244 --> 00:31:34,324
Speaker 1: And some people take it a little bit further, like

591
00:31:34,404 --> 00:31:37,164
Speaker 1: they'll say, oh, well, it's just the first letter that matters.

592
00:31:37,284 --> 00:31:39,284
Speaker 1: So like, if your name is lindsay, you are more

593
00:31:39,404 --> 00:31:42,084
Speaker 1: likely to become a lawyer. I'm like, yeah, no, I

594
00:31:42,124 --> 00:31:45,564
Speaker 1: don't think so. But so there are people who take

595
00:31:45,604 --> 00:31:48,804
Speaker 1: it to an extreme, but basically the idea is your

596
00:31:48,924 --> 00:31:50,364
Speaker 1: name matters more than you think.

597
00:31:50,884 --> 00:31:53,044
Speaker 2: Yes, and so people have funny names that associate with

598
00:31:53,044 --> 00:31:55,084
Speaker 2: their jobs, and I think the idea well, before we

599
00:31:55,124 --> 00:31:56,444
Speaker 2: get to the idea of if there's any like real

600
00:31:56,484 --> 00:31:58,924
Speaker 2: psychology behind this, I have a quiz for you, if

601
00:31:58,964 --> 00:32:01,204
Speaker 2: you're willing to take it. All right, I went back

602
00:32:01,244 --> 00:32:04,724
Speaker 2: through some old of my favorites. I don't think I've

603
00:32:04,804 --> 00:32:07,484
Speaker 2: told you these ones. You may not because you're an enthusiast,

604
00:32:07,524 --> 00:32:10,484
Speaker 2: a collector, a connoisseer, a collector of names as I am. I.

605
00:32:11,484 --> 00:32:12,924
Speaker 2: So I'm gonna give you the name, and you're gonna

606
00:32:12,924 --> 00:32:16,164
Speaker 2: tell me their job. Okay, Okay, okay. Burt Beverage.

607
00:32:17,524 --> 00:32:20,564
Speaker 1: He works in the beverage industry.

608
00:32:20,204 --> 00:32:24,364
Speaker 2: Created Tito's vodka. Oh god, yeah, big okay, Tito's.

609
00:32:24,484 --> 00:32:26,724
Speaker 1: Tito's creator's last name is Beverage.

610
00:32:26,764 --> 00:32:29,924
Speaker 2: Burt Beverage, I did not know. I tell you. Okay,

611
00:32:30,124 --> 00:32:33,324
Speaker 2: I've got some more for you. Okay, Keith Weed. This

612
00:32:33,404 --> 00:32:34,084
Speaker 2: one has a bonus.

613
00:32:34,204 --> 00:32:39,124
Speaker 1: Keith Weed does Keith Weed work in the pot business.

614
00:32:39,324 --> 00:32:43,604
Speaker 2: Head of the Royal Horticultural Society. Mother's maiden name Hedges.

615
00:32:44,724 --> 00:32:48,404
Speaker 1: Oh no, double layer, double So I went the wrong

616
00:32:48,484 --> 00:32:49,084
Speaker 1: kind of weed.

617
00:32:49,164 --> 00:32:51,284
Speaker 2: But this one, you know you were in the in

618
00:32:51,364 --> 00:32:53,244
Speaker 2: the I was, yeah, Joshua, but.

619
00:32:56,604 --> 00:32:59,004
Speaker 1: Is he is he? Is he a fitness trainer?

620
00:33:00,244 --> 00:33:04,124
Speaker 2: I love that you guessed that gastro enterologist? But finished

621
00:33:05,484 --> 00:33:15,564
Speaker 2: sue you lawyer, Yes, correct, probably Lieutenant Les McBurney. We

622
00:33:15,724 --> 00:33:16,324
Speaker 2: may have done this.

623
00:33:18,604 --> 00:33:20,564
Speaker 1: Yes, Les McBurnie is a firefighter.

624
00:33:20,644 --> 00:33:23,524
Speaker 2: That one you and I? Okay, okay, last one. This

625
00:33:23,604 --> 00:33:28,044
Speaker 2: one kind of up your alley. Walter Russell Brain otherwise

626
00:33:28,084 --> 00:33:32,164
Speaker 2: known as the Right Honorable Lord Brain. Is he a psychologist,

627
00:33:32,284 --> 00:33:35,404
Speaker 2: the world famous neurologist. Close enough score for that one.

628
00:33:35,764 --> 00:33:36,644
Speaker 2: Those are some good ones.

629
00:33:37,804 --> 00:33:41,044
Speaker 1: Those are some good ones. One of my favorites that

630
00:33:42,324 --> 00:33:44,364
Speaker 1: I actually, I mean, I think you said that it

631
00:33:44,484 --> 00:33:46,604
Speaker 1: was a hoax and I had to look up the

632
00:33:46,684 --> 00:33:51,164
Speaker 1: whole biography to make sure it wasn't. Nick Burns Cox

633
00:33:54,044 --> 00:33:58,684
Speaker 1: he is a urologist, and he invented something that is

634
00:33:58,804 --> 00:34:00,844
Speaker 1: called the pee in pot.

635
00:34:02,084 --> 00:34:05,124
Speaker 2: That is, that's like a little too on point right.

636
00:34:05,444 --> 00:34:07,724
Speaker 2: We had a doctor doctor recently, We did have.

637
00:34:07,804 --> 00:34:10,964
Speaker 1: A doctor doctor, and I thought that that was very funny.

638
00:34:11,884 --> 00:34:15,164
Speaker 1: But the question is, so, now that we've entertained you

639
00:34:15,284 --> 00:34:18,804
Speaker 1: with some of our famous, famously funny names, the question

640
00:34:19,004 --> 00:34:23,644
Speaker 1: is is there actually anything to this? And so the

641
00:34:23,724 --> 00:34:25,844
Speaker 1: science on it is all over the place, Like, this

642
00:34:26,004 --> 00:34:31,044
Speaker 1: is a hard thing to study because even though there

643
00:34:31,084 --> 00:34:34,004
Speaker 1: are these hilarious examples, like there are billions of people

644
00:34:34,084 --> 00:34:34,284
Speaker 1: in the.

645
00:34:34,364 --> 00:34:37,964
Speaker 2: World, right, what's the chances there wouldn't be a doctor doctor?

646
00:34:38,444 --> 00:34:40,724
Speaker 1: And what's the chance that there wouldn't be a urologist

647
00:34:40,764 --> 00:34:42,124
Speaker 1: whose last name was burns Cox?

648
00:34:42,444 --> 00:34:43,804
Speaker 2: That I think the chances are very high that that

649
00:34:43,844 --> 00:34:46,644
Speaker 2: wouldn't happen. That's a I'm glad we live in this

650
00:34:46,764 --> 00:34:49,164
Speaker 2: one of the multiverse where we got doctor burns Cox.

651
00:34:50,524 --> 00:34:54,804
Speaker 1: But yeah, and we also have base rate stuff, so

652
00:34:54,964 --> 00:34:56,884
Speaker 1: like if your last name is Baker, like and you're

653
00:34:57,204 --> 00:34:59,444
Speaker 1: happened to be in the baking industry, like, come on,

654
00:34:59,604 --> 00:35:03,444
Speaker 1: how many bakers are there? Like? So there are lots

655
00:35:03,484 --> 00:35:06,044
Speaker 1: of confounding factors, and it's not like people have looked

656
00:35:06,084 --> 00:35:10,084
Speaker 1: at lists of every single profession, every and name in

657
00:35:10,124 --> 00:35:14,284
Speaker 1: the world and tried to do any sort of comprehensive studies.

658
00:35:14,364 --> 00:35:16,964
Speaker 1: So all of the data I could find, some find

659
00:35:17,004 --> 00:35:20,564
Speaker 1: evidence of it, some don't. I think it's a wash, right, Like,

660
00:35:20,804 --> 00:35:24,084
Speaker 1: I don't think that there's hugely strong evidence for it.

661
00:35:24,404 --> 00:35:26,804
Speaker 2: But on the other hand, it being that these people

662
00:35:26,844 --> 00:35:28,724
Speaker 2: are actually more likely to go into professions.

663
00:35:28,804 --> 00:35:33,324
Speaker 1: Yea, yea, exactly exactly. But on the other hand, there

664
00:35:34,164 --> 00:35:38,164
Speaker 1: are some reasons to believe that this would be a thing. So,

665
00:35:38,444 --> 00:35:41,524
Speaker 1: for instance, if your last name is Baker, like, chances

666
00:35:41,604 --> 00:35:44,764
Speaker 1: are you are descended from a family of bakers, because

667
00:35:45,244 --> 00:35:46,924
Speaker 1: you know, there is and this is true not just

668
00:35:47,004 --> 00:35:50,404
Speaker 1: in English, so in the Russian language, this is true

669
00:35:50,404 --> 00:35:52,884
Speaker 1: of last names. And I know in a lot of

670
00:35:52,924 --> 00:35:57,964
Speaker 1: different cultures this is true. Names were given to families

671
00:35:58,084 --> 00:36:03,524
Speaker 1: based on the family's professionals directs exactly. So Carpenter, you know,

672
00:36:03,804 --> 00:36:07,884
Speaker 1: was a family of carpenters. And it's really interesting because

673
00:36:07,884 --> 00:36:11,244
Speaker 1: we were just you know, we're talking about genetics, and

674
00:36:11,444 --> 00:36:15,204
Speaker 1: so if you are in a family that wasn't a

675
00:36:15,324 --> 00:36:19,684
Speaker 1: certain kind of craft, so to speak, then you might

676
00:36:19,764 --> 00:36:23,124
Speaker 1: have a genetic predisposition for it, a talent for it,

677
00:36:23,164 --> 00:36:26,564
Speaker 1: et cetera, et cetera. So then exactly it's reverse causality

678
00:36:26,644 --> 00:36:28,884
Speaker 1: that you were named this. You still have a family

679
00:36:29,004 --> 00:36:32,564
Speaker 1: name because it's something that your family has been good

680
00:36:32,564 --> 00:36:33,844
Speaker 1: at for multiple generations.

681
00:36:34,164 --> 00:36:36,004
Speaker 2: And what do you think about I don't know, you know,

682
00:36:36,044 --> 00:36:39,484
Speaker 2: I've never really done a deep dive in this literature

683
00:36:39,564 --> 00:36:41,804
Speaker 2: because I just desperately want it to be true, and.

684
00:36:42,084 --> 00:36:44,764
Speaker 1: Yeah, you know that's that's fair. I don't think you should.

685
00:36:45,244 --> 00:36:47,804
Speaker 2: But what do you think about the idea? I mean,

686
00:36:48,004 --> 00:36:49,484
Speaker 2: I seem to recall when I read a little bit

687
00:36:49,484 --> 00:36:55,044
Speaker 2: about it, that there was the theory was that people

688
00:36:55,084 --> 00:36:57,324
Speaker 2: are like one, just more aware of jobs that might

689
00:36:57,324 --> 00:36:59,084
Speaker 2: be related to their name or people tell them like,

690
00:36:59,164 --> 00:37:01,404
Speaker 2: oh you should be you know sue you oh you

691
00:37:01,444 --> 00:37:04,884
Speaker 2: should be a lawyer, or they're like more positively disposed

692
00:37:05,084 --> 00:37:07,404
Speaker 2: to those things because kind of people like things with

693
00:37:07,444 --> 00:37:09,644
Speaker 2: their names on them, right, Like that's why like puts

694
00:37:09,684 --> 00:37:11,404
Speaker 2: people's names on a body, Like I see my name,

695
00:37:11,444 --> 00:37:13,004
Speaker 2: I like it. And then maybe they have like a

696
00:37:13,044 --> 00:37:17,044
Speaker 2: little more positive association with some of those professions if

697
00:37:17,084 --> 00:37:19,244
Speaker 2: it feels like related to their name, like they find

698
00:37:19,284 --> 00:37:20,164
Speaker 2: it fun just like we do.

699
00:37:21,164 --> 00:37:23,484
Speaker 1: Oh, I think there's actually evidence for all of these things,

700
00:37:23,724 --> 00:37:26,364
Speaker 1: because that does happen all the time, right Like people

701
00:37:26,444 --> 00:37:28,804
Speaker 1: do say, oh you should do X or Y if

702
00:37:28,844 --> 00:37:30,964
Speaker 1: you have a funny name, if you have a memorable name.

703
00:37:31,164 --> 00:37:33,364
Speaker 2: Yeah. So so, like as crazy as it sounds, there's

704
00:37:33,364 --> 00:37:38,004
Speaker 2: some actually like not very insane theories for like why

705
00:37:38,124 --> 00:37:40,004
Speaker 2: like maybe there's some realiti it's just like more salient

706
00:37:40,044 --> 00:37:40,164
Speaker 2: to you.

707
00:37:40,644 --> 00:37:43,404
Speaker 1: And as we've talked about with genetics, you know, what

708
00:37:43,564 --> 00:37:46,204
Speaker 1: kinds of traits are you more likely to select for?

709
00:37:47,204 --> 00:37:51,084
Speaker 1: You do tend to gravitate two things that are familiar, right,

710
00:37:51,204 --> 00:37:54,484
Speaker 1: and that are comfortable, that remind you of you, of

711
00:37:54,724 --> 00:37:56,644
Speaker 1: you know, your parents, of people you know. And these

712
00:37:56,684 --> 00:38:01,804
Speaker 1: sorts of biases manifest themselves incredibly early on right. We're

713
00:38:01,884 --> 00:38:05,724
Speaker 1: talking infants right that are ten months old already shows

714
00:38:05,804 --> 00:38:09,324
Speaker 1: some of these preferences for faces that are same skin

715
00:38:09,564 --> 00:38:12,764
Speaker 1: color as their parents. It's not something that happens at birth,

716
00:38:12,804 --> 00:38:17,124
Speaker 1: but it does actually start manifesting incredibly early, just because

717
00:38:17,164 --> 00:38:19,524
Speaker 1: you know it is familiar, right, it is what you know.

718
00:38:20,084 --> 00:38:23,044
Speaker 1: And so if you do have a name that is Coxburns,

719
00:38:23,284 --> 00:38:30,844
Speaker 1: then then maybe you know, you end up subconsciously consciously

720
00:38:31,284 --> 00:38:34,804
Speaker 1: on some level thinking more about those types of things.

721
00:38:35,084 --> 00:38:37,084
Speaker 1: Maybe if your last name is Judge, You're like, haha,

722
00:38:37,124 --> 00:38:39,364
Speaker 1: wouldn't it be funny if my name was Judge Judge,

723
00:38:40,444 --> 00:38:43,284
Speaker 1: or you know, if I'm doctor doctor or something like that.

724
00:38:43,924 --> 00:38:46,044
Speaker 1: So you know, on the one hand, this is just

725
00:38:46,284 --> 00:38:49,844
Speaker 1: hilarious and it seems like an artifact, and we collect

726
00:38:49,924 --> 00:38:52,884
Speaker 1: these funny names. On the other hand, there are both

727
00:38:53,124 --> 00:38:59,924
Speaker 1: psychological and historical reasons why this effect might actually be

728
00:39:00,844 --> 00:39:04,724
Speaker 1: an effect. And obviously it's weak, right, And like with anything,

729
00:39:05,724 --> 00:39:10,724
Speaker 1: things are multiply determined, and it's not like, you know,

730
00:39:10,884 --> 00:39:16,164
Speaker 1: you end up doing something just because of this one thing,

731
00:39:16,684 --> 00:39:21,564
Speaker 1: and you might have a last name that's incredibly salient

732
00:39:21,684 --> 00:39:23,524
Speaker 1: and yet do nothing.

733
00:39:24,164 --> 00:39:24,924
Speaker 2: Yeah with that.

734
00:39:25,604 --> 00:39:27,964
Speaker 1: My last name in Russian, by the way, is related

735
00:39:28,004 --> 00:39:32,524
Speaker 1: to horses, and I'm allergic to horses, so you know, definitely.

736
00:39:32,524 --> 00:39:34,644
Speaker 2: Definitely not going to be doing anything else with you

737
00:39:34,804 --> 00:39:36,404
Speaker 2: the horse line I was singing of Russian, there was

738
00:39:36,404 --> 00:39:39,324
Speaker 2: a Russian, a great Russian hurdler named Maria step on Nova,

739
00:39:39,564 --> 00:39:41,964
Speaker 2: which is sound sounds like step on over, you know,

740
00:39:42,084 --> 00:39:44,844
Speaker 2: like a hurdle, But I don't know what it means

741
00:39:44,884 --> 00:39:47,564
Speaker 2: in Russian, so that probably wouldn't be it means of

742
00:39:47,764 --> 00:39:51,684
Speaker 2: Stephen of Stephen. Okay, Yeah, I wonder if the fact

743
00:39:51,764 --> 00:39:55,164
Speaker 2: carries if you're sort of named for someone, too, right,

744
00:39:55,204 --> 00:39:59,244
Speaker 2: because then you'd have those associations like name for some

745
00:39:59,364 --> 00:40:01,324
Speaker 2: prominent person for sure.

746
00:40:01,364 --> 00:40:04,204
Speaker 1: For sure. And I'm thinking now back to kind of

747
00:40:04,284 --> 00:40:07,244
Speaker 1: early colonial days, right, think of the names, like, think

748
00:40:07,284 --> 00:40:11,324
Speaker 1: of your early US history where people were named virtues

749
00:40:11,564 --> 00:40:14,564
Speaker 1: or things that they were that you wanted them to exhibit,

750
00:40:14,964 --> 00:40:21,204
Speaker 1: you know, chastity, virtue, grace, like all of these absolutely yeah,

751
00:40:21,644 --> 00:40:25,844
Speaker 1: crazy names. Some of them are just absolutely temperance, these

752
00:40:25,964 --> 00:40:29,644
Speaker 1: names that just sound completely insane to us now, But

753
00:40:30,044 --> 00:40:32,284
Speaker 1: I think that was one of the reasons they thought

754
00:40:32,324 --> 00:40:35,844
Speaker 1: that maybe if that's your name and you're constantly temperance,

755
00:40:36,284 --> 00:40:39,284
Speaker 1: don't are you sure you want another drink? Temperance.

756
00:40:39,484 --> 00:40:41,044
Speaker 2: I feel like that could go either way. Like my

757
00:40:41,124 --> 00:40:43,284
Speaker 2: parents name me Temperance, So I'm going off the rails

758
00:40:43,364 --> 00:40:44,044
Speaker 2: like forget them.

759
00:40:44,724 --> 00:40:48,244
Speaker 1: Yeah, And as I kind of, as I casually mentioned

760
00:40:48,244 --> 00:40:51,404
Speaker 1: at the beginning, you know, base rates are also important here,

761
00:40:51,764 --> 00:40:53,884
Speaker 1: right if we if we're looking at this, you need

762
00:40:53,964 --> 00:40:57,084
Speaker 1: to look at it from a statistically savvy as opposed

763
00:40:57,124 --> 00:40:59,524
Speaker 1: to kind of this name just stands out more, right,

764
00:40:59,644 --> 00:41:02,164
Speaker 1: So I gravitate toward it, and I pick and choose

765
00:41:02,284 --> 00:41:05,844
Speaker 1: the ones that I say, see nominative determinism. Yeah, So

766
00:41:06,284 --> 00:41:08,844
Speaker 1: I think that that's that that's important, And that's an

767
00:41:08,884 --> 00:41:11,644
Speaker 1: analysis that's really hard to do because where are you

768
00:41:11,724 --> 00:41:12,844
Speaker 1: going to get those data sets?

769
00:41:13,044 --> 00:41:17,124
Speaker 2: Yeah? Right, you can't really randomize people to life, you know,

770
00:41:17,364 --> 00:41:20,964
Speaker 2: and see give them different names and see how things turn.

771
00:41:20,804 --> 00:41:22,364
Speaker 1: Out, right, That would be funny.

772
00:41:22,644 --> 00:41:25,164
Speaker 2: Yeah, So basically the bottom line is I'm just going

773
00:41:25,204 --> 00:41:27,844
Speaker 2: to continue to believe that it's completely deterministic and text

774
00:41:27,924 --> 00:41:30,364
Speaker 2: you every hilarious name that I see in the news, and.

775
00:41:30,484 --> 00:41:32,364
Speaker 1: I will laugh every time, and I will text you

776
00:41:32,484 --> 00:41:35,724
Speaker 1: back other hilarious names. So yeah, I think for now

777
00:41:35,804 --> 00:41:38,484
Speaker 1: we can just keep on thinking this is funny for

778
00:41:38,604 --> 00:41:41,564
Speaker 1: anyone listening and wanting tips on whether what they name

779
00:41:41,604 --> 00:41:44,644
Speaker 1: their kid matters, Yes, it does, but don't please don't

780
00:41:44,684 --> 00:41:49,204
Speaker 1: name your kid temperance. That will probably backfire. Yeah, that's

781
00:41:49,284 --> 00:41:51,524
Speaker 1: probably That's probably not not what we're going for.

782
00:41:51,884 --> 00:41:53,524
Speaker 2: Name the me YouTuber instead.

783
00:41:53,684 --> 00:42:00,164
Speaker 1: Yes, exactly, exactly. Influencer, influencer, TikTok star. I'm actually officially

784
00:42:00,564 --> 00:42:02,484
Speaker 1: changing my name to TikTok star.

785
00:42:02,724 --> 00:42:05,444
Speaker 2: I feel like I feel like there's a strong chance

786
00:42:05,484 --> 00:42:10,204
Speaker 2: this has already happened. Yeah, Like when I was I

787
00:42:10,244 --> 00:42:12,284
Speaker 2: have a son, and when we were naming, I like

788
00:42:12,404 --> 00:42:14,564
Speaker 2: downloaded a bunch of data from the Social Security Administration

789
00:42:14,724 --> 00:42:17,444
Speaker 2: on naming and it was I think it was like

790
00:42:19,124 --> 00:42:21,604
Speaker 2: they gave you every name that was used at least

791
00:42:22,404 --> 00:42:24,404
Speaker 2: I don't know, three times or five times whatever, it

792
00:42:24,524 --> 00:42:26,524
Speaker 2: was you didn't They didn't include in the data names

793
00:42:26,524 --> 00:42:29,564
Speaker 2: that were only used once, and so these the names

794
00:42:29,564 --> 00:42:31,484
Speaker 2: that were used the least but three or five times

795
00:42:31,524 --> 00:42:33,524
Speaker 2: were like literally someone hitting their head on a keyboard,

796
00:42:33,804 --> 00:42:36,644
Speaker 2: and those were used like three to five times in

797
00:42:36,764 --> 00:42:40,644
Speaker 2: a given year. So there's for sure somebody has been

798
00:42:40,684 --> 00:42:41,284
Speaker 2: named TikTok.

799
00:42:42,004 --> 00:42:47,524
Speaker 1: Well, apparently there is a baby that was born this

800
00:42:47,764 --> 00:42:52,404
Speaker 1: year who was named after chat GPT. So it's a

801
00:42:52,444 --> 00:42:55,964
Speaker 1: baby girl and her name is chat Yept. It's a

802
00:42:56,004 --> 00:42:56,844
Speaker 1: Colombian baby.

803
00:42:57,604 --> 00:43:00,604
Speaker 2: So seriously, we are yes. Is this like angling for

804
00:43:00,644 --> 00:43:03,804
Speaker 2: an endorsement or just in technological enthusiasm or like what

805
00:43:04,004 --> 00:43:05,524
Speaker 2: what's find what's going on there?

806
00:43:06,524 --> 00:43:10,804
Speaker 1: I'm not sure, but on August fifteenth, chat Ept Bastidas

807
00:43:10,844 --> 00:43:14,364
Speaker 1: Geta was registered at the local National Registry.

808
00:43:14,684 --> 00:43:16,964
Speaker 2: How they used GPT five though, I don't really like

809
00:43:17,044 --> 00:43:19,484
Speaker 2: it that much. Maybe they should rename the baby cloud

810
00:43:19,604 --> 00:43:20,884
Speaker 2: or something or anthropic.

811
00:43:21,084 --> 00:43:23,524
Speaker 1: Yeah, you know, well for the company, you know, you

812
00:43:23,564 --> 00:43:30,804
Speaker 1: can perplexity, perplexity Gemini. Yeah yeah, but what if Gemini

813
00:43:30,884 --> 00:43:33,484
Speaker 1: isn't a Gemini. I mean that's going to be incredibly.

814
00:43:33,364 --> 00:43:34,364
Speaker 2: There's a lot of considerations.

815
00:43:34,404 --> 00:43:37,884
Speaker 1: This is serious, this is a serious business. So listeners

816
00:43:38,204 --> 00:43:42,364
Speaker 1: please consider choose wisely before choosing a name. But yeah,

817
00:43:42,444 --> 00:43:44,524
Speaker 1: this is this is you know, something that I will

818
00:43:44,604 --> 00:43:47,324
Speaker 1: continue looking at the literature and see if any interesting

819
00:43:47,404 --> 00:43:50,244
Speaker 1: studies come out, because I do. I can't help but

820
00:43:50,324 --> 00:43:52,284
Speaker 1: feel that there might be something to this, right, because

821
00:43:52,284 --> 00:43:54,684
Speaker 1: there are so many reasons why, and because there are

822
00:43:54,804 --> 00:43:57,684
Speaker 1: some data sets that show that, yes, there there is

823
00:43:58,284 --> 00:44:00,284
Speaker 1: kind of a some sort of an effect.

824
00:44:00,364 --> 00:44:04,524
Speaker 2: And it's interesting. You're something kind of ridiculous. It's probably

825
00:44:04,644 --> 00:44:07,924
Speaker 2: not true in psychology in this case. Behind this ridiculous thing,

826
00:44:07,964 --> 00:44:10,764
Speaker 2: there are actually some pretty sort of almost common sense

827
00:44:10,804 --> 00:44:12,764
Speaker 2: like ah, someone's just more aware of this profession, or

828
00:44:12,804 --> 00:44:15,604
Speaker 2: people say you should do this, you should look at that, right, Yeah, yep,

829
00:44:15,724 --> 00:44:17,244
Speaker 2: which is not that hard to understand.

830
00:44:17,764 --> 00:44:21,764
Speaker 1: So there you have at people, whether it's genetics or names.

831
00:44:21,884 --> 00:44:27,724
Speaker 1: Today's episode is all about things that determine your future

832
00:44:27,924 --> 00:44:31,204
Speaker 1: and the future of the world. Thanks so much for

833
00:44:31,324 --> 00:44:35,124
Speaker 1: being such an amazing co host, David. I hope you

834
00:44:35,364 --> 00:44:37,764
Speaker 1: will come back both as a guest and as a

835
00:44:37,804 --> 00:44:41,924
Speaker 1: guest co host when Nate inevitably ends up in Norway

836
00:44:42,124 --> 00:44:43,244
Speaker 1: and can't host the show.

837
00:44:43,604 --> 00:44:44,364
Speaker 2: And I would love to.

838
00:44:52,124 --> 00:44:54,004
Speaker 1: Let us know what you think of the show. Reach

839
00:44:54,084 --> 00:44:57,204
Speaker 1: out to us at Risky Business at pushkin dot fm.

840
00:44:57,804 --> 00:45:00,044
Speaker 1: And by the way, if you're a Pushkin Plus subscriber,

841
00:45:00,284 --> 00:45:03,004
Speaker 1: we have some bonus content for you that's coming up

842
00:45:03,164 --> 00:45:06,084
Speaker 1: right after the credits. And if you're not subscribing yet,

843
00:45:06,164 --> 00:45:08,724
Speaker 1: I mean, what's up with that? Please consider signing up

844
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Speaker 1: next ninety nine a month you get access to all

845
00:45:11,564 --> 00:45:15,284
Speaker 1: that premium content and add free listening across Pushkin's entire

846
00:45:15,364 --> 00:45:19,044
Speaker 1: network of shows. Risky Business is hosted by me Maria

847
00:45:19,124 --> 00:45:22,924
Speaker 1: Kannakova and by my fabulous co host Nate Silver. The

848
00:45:23,044 --> 00:45:26,404
Speaker 1: show is a co production of Pushkin Industries and iHeartMedia.

849
00:45:27,204 --> 00:45:30,844
Speaker 1: This episode was produced by Isabelle Carter. Our associate producer

850
00:45:30,964 --> 00:45:34,084
Speaker 1: is Sonia Gerwit. Sally helm is our editor, and our

851
00:45:34,124 --> 00:45:38,724
Speaker 1: executive producer is Jacob Goldstein. Mixing by Sarah Buguer. If

852
00:45:38,804 --> 00:45:40,964
Speaker 1: you like the show, please rate and review us so

853
00:45:41,124 --> 00:45:44,084
Speaker 1: other people can find us too. Thanks for tuning in.