Dwarkesh Podcast - Steve Hsu - Intelligence, Embryo Selection, & The Future of Humanity

Episode Date: August 23, 2022

Steve Hsu is a Professor of Theoretical Physics at Michigan State University and cofounder of the company Genomic Prediction.We go deep into the weeds on how embryo selection can make babies healthier and smarter.Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.Read the full transcript here.Follow Steve on Twitter. Follow me on Twitter for updates on future episodes.Timestamps(0:00:14) - Feynman’s advice on picking up women(0:11:46) - Embryo selection(0:24:19) - Why hasn't natural selection already optimized humans?(0:34:13) - Aging(0:43:18) - First Mover Advantage(0:53:38) - Genomics in dating(0:59:20) - Ancestral populations(1:07:07) - Is this eugenics?(1:15:08) - Tradeoffs to intelligence(1:24:25) - Consumer preferences(1:29:34) - Gwern(1:33:55) - Will parents matter?(1:44:45) - Wordcels and shape rotators(1:56:45) - Bezos and brilliant physicists(2:09:35) - Elite education Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Today, I have a pleasure of speaking with Steve Shue. Steve, thanks for coming on the podcast. I'm excited about this. Hey, it's my pleasure. I'm excited too, and I just want to say, I've listened to some of your earlier interviews and thought you were very insightful, which is why I was really excited to have a conversation with you.

That means a lot for me to hear you say, because I'm a big fan of your podcast. My first question is, what advice did Richard Feynman give you about picking up girls? Wow. So one day in the spring, spring of my senior year, I was walking across campus, and I see Feynman coming toward me, and we knew each other from various things. And it's a small campus. And I was a physics major,

and he was my hero. So I guess I had known him since bright freshman year. So he sees me. And, you know, he's got this, I don't know if it's long, I guess it's a Long Island, or it's, it's some kind of New York borough accent. And he says, hey, shoe. This is how he says my name, hey, shoo. And I'm like, hi, Professor Feynman. And so we start talking. And he says to me, wow, you're kind of a big guy. And I was a lot bigger then because I played on the, I was a linebacker on the Celtic football team. So I was about almost 200 pounds. I'm a little just over six feet tall. And so I was pretty like a gym rat at that time. And so he was like, I was much bigger than him, obviously. He was like, wow, you're a big guy.

Steve, I got to ask you something. And Feynman was born in like 1918. So he's not really like from the modern era. Like he was, I guess he was going through graduate school when the Second World War started. And so to him, the whole concept of a health club, a gym was like totally, you know, couldn't understand it. And that was the era. This was the 80s. So that was an era when Gold's Gym was like becoming a world national franchise.

And so there were gyms all over the place. his 24-hour fitness and stuff like this. So he didn't know what it was. And he's a very interesting guy. So his suspicion, he says to me, what do you guys do there? Is it just a thing to meet chicks, to meet girls? Or do you guys actually, is it really for training? Do you guys really go there to get buff, to get big? And so I started explaining to him. I said, yes, you know, people are there to get big, but people are also checking out the girls. And there is a lot of stuff happening at the health club or in the weight room. And so, you know, he grills me on this for a long time. And one of the famous things about Feynman is that he has this laser-like focus. So if there's

something he really doesn't understand and he wants to get to the bottom of it, he will just focus in on you and just start questioning you and get to the bottom of it. That's the way his brain works. So he did that to me for like, I don't know how long. We were talking about lifting weights and everything because he didn't know anything about it. And at the end, he says to me, wow, Steve, I really appreciate that. You know, let me give you some good advice. And so then he starts telling me about how to pick up girls, which I guess he, you know, he's a kind of an expert on.

And he says to me, he goes, one of the things he says to me, he says like, I don't know how much girls really like guys that are big as you. Like, I don't, I'm not, he thought like it might be a turnoff, actually. And he said, but you know what? You have a nice smile. So that was the one compliment. You know, he gives me, you have a nice smile. And then he starts telling me, he says, you know, the main thing is it's a numbers game.

Okay. You have to divorce your, you have to be totally rational about it. You're never going to see that girl again. Right? You're, you're in an airport lounge or you're at a bar. It's Saturday night in Pasadena or Westwood. And you're talking to some girl. And he says, you're never going to see her again.

This is your one interaction with her. Five minute interaction. Do what you have to do. And if she, for some reason, doesn't like you, just go to the next one. And that's what he says. So, you know, and he gives some colorful details and stuff. But the point is, he's like, you should not care what they think of you. You're trying to do your thing. And, you know, he's a pretty, he had a

kind of a reputation at Caltech as a womanizer. I could go into that too, but I heard this from the secretaries and stuff. But with the students or with like, no, no, with secretaries, mostly secretaries, who were almost all female at that time. He had thought about this a lot. And he was just like, look, it's a numbers game, just, I guess the PUA type, are you familiar with PUA culture? Yeah, yeah. So the PUA guys would say like, yeah, don't, you know, it's like an operation. Like you're just doing something. You follow the algorithm.

And whatever happens, it's not a reflection on your self-esteem or your internal self-image. It's just that's what happened. And you just go on to the next one. And that was basically the advice he was giving me. You know, and you said other things, which were pretty standard. Like, you know, be funny. You're a funny guy. You know, girls like that.

Be confident. You know, just basic stuff. But the main thing I remember was the operationalization. of it as an algorithm and that you should just not internalize whatever happens if you get rejected, because that's what really hurts that you know you're a guy, right? When you go across the bar to talk to that girl, maybe that doesn't happen in your generation. Maybe you just like swipe. But we had to go, it was terrifying.

We had to go across the bar and talk to some lady and it's loud. And you got like a few minutes to make your case, basically. And nothing hurts more and nothing is more scary. than walking across up to the girl, maybe she and her friends or something, right? So he was just saying, like, you got to train yourself out of that. Like, you're never going to see them again. The face space of humanity is so big, you'll never encounter them again. And it just doesn't matter.

So just do your best. Yeah, that's interesting because I wonder when he was, I mean, in the 40s, when he was at that age, was he doing this? I don't know what the cultural conventions were at the time, but I don't know, were there bars in the 40s where you could just go? hit on girls or? Oh yeah, absolutely. Absolutely. I mean, if you read literature from that time or even a little bit earlier, like Hemingway or John O'Hara or, you know, they talk about, you know, how men and women interacted in bars and stuff like this and in New York City and, you know, yeah, so that was a

thing. That was much more of a thing than, I think, for your generation. That's what I can't figure out with my kids. Like, what is going on? How do boys and girls meet these days? But back in the day, it was like the guy had to do all the work. And it was like the most terrifying thing you could do. And, you know, and you just have to train yourself out of that. Right. But by the way, when, for the context for the audience, when, find men since you were a big guy, like you were a football player at Caltech, right?

And then there's a picture of you actually on your website where maybe this was after after college or something. But yeah, you look like pretty ripped. And it's kind of, I mean, today it seems more common. because of gym culture and stuff, but I don't know, back then, I don't know how common that kind of, that kind of body physics was. It's amazing that you asked this question. I'll tell you a funny story because I was, one of the reasons finding found this so weird

was because the way bodybuilding entered the United States or became widespread was a very interesting story because at first they were regarded as freaks and homosexuals and all kinds of stuff. And I remember growing up, our high school football coach, swimming was different. Swimming because it was international. Swimming picked up a lot of advanced training techniques from the Russians and from East Germans and stuff. But football was more, you know, more kind of just American and not very international. And so our football coach used to tell us not to lift weights when we were maybe when I was in junior high school. And they said it makes you slow. You're, bulky is no good. You got to be, you know, you got to be fast in football. And then something changed

around the time I was in high school where the coaches figured out. Because as a swimmer, as a swimmer, I had been lifting weight since I was an age group swimmer, like maybe at age 12 or 14, I started lifting weights. So, but then the football coaches got into it. And mainly because the University of Nebraska, University of Nebraska had a very famous strength program that really popularized it. And so at the time, though, there just weren't a lot of big. guys and the people who knew how to train the way everybody like you probably go to the gym and train using what would be considered kind of advanced knowledge back in the 80s okay like how to do a split routine or squat on one day and do your upper body on the other day next day that was like

considered advanced knowledge at that time and so i remember once i had an injury and it was in the trainer's room at the caltech athletic facility and the lady was looking it was a female trainer and she's looking at my quadriceps and because I'd pulled a muscle and she was looking at the if you know your anatomy like right above your kneecap your quadriceps kind of insert right above your kneecap and if you have well-developed quads you like you actually have a bulge a bump right above your kneecap and she was looking at it from this angle where she was in front of me and she was looking at my like my leg from the front she's like wow it's really swollen and I was like that's not the injury that's my quadrice muscle and she was a trainer so

And at that time, like, I could probably squat, I could maybe squat 400 pounds at that time. So I was pretty strong, right? And I had big legs. And so anyway, the fact that the trainer didn't really understand like what well-developed anatomy was supposed to look like was just blew my mind. I was like, no, that's my, that's my quadricep. We built that up. And she's like, oh, I thought that was the injury. I was like, what are you talking about? So anyway, we've come a long way. This is one of these things where you've got to be old to have any kind of understanding of how this stuff evolved over the last, you know, 34 years. But, you know, I wonder if that was a phenomenon of that particular time or if, like,

if throughout human history people have just not been that muscular or like, because you hear stories of like Roman soldiers who are carrying like 80 pounds for 10 or 20 miles a day. And I mean, there's like a lot of sculptures in the ancient world where, I mean, not that ancient, but like the people look like they have well-developed musculature. So the Greeks were very special because they were the first to really think. think about the word gymnasium and there's a thing called the palestra, which where they would train like wrestling and boxing and stuff like this. They were the first people who were really seriously into physical culture and training, specific training for athletic competition.

But if you look at like even in the 70s, so when I was a little kid and I remember in the 70s and now when I look back at old photos from the 70s, it's very apparent, guys are skinny. guys are so skinny. You know, the guys who went off and fought World War II, whether they were on the German side or the American side, they were like 5, 8, 5, 9, and they weighed like 130 pounds, 140 pounds. They were totally different than what modern U.S.

salt marines you would think of look like, right? So, yeah, physical culture was a new thing. Of course, the Romans and the Greeks had it to some degree, but it was kind of lost for a long time. And it was just coming back in the U.S. when I was growing up. And so, yeah, if you were, you know, 200 pounds of fairly lean 200 pounds and you could bench over 300, that was pretty rare back in those days. Yeah, yeah, yeah. Okay, so let's talk about your company, Genomic Prediction.

Yeah, do you want to talk about what this company does? Do you want to give an intro into what this is? Yeah. So if you don't mind what I should say, there are two ways to introduce it. One is the scientific view, and then the other is the. IVF view and I can kind of do a little of both. So scientifically the issue is we have more and more genomic data. If you give me the genomes of a bunch of people and then you give me some information about each person like do they or do they not have diabetes or how tall are they

or you know what's their IQ score or something. Then any, all of your listeners will be familiar with AI and machine learning. It's a natural AI machine learning problem to figure out which features in the DNA variation between people are predictive of whatever variable you're trying to predict, whatever phenotype. The biological term is phenotype. So this is an ancient scientific question of how do you relate the genotype of the organism, the specific DNA pattern, to the phenotype, the actual expressed characteristics of the organism. And if you think about it, this is what biology is. Like, once we had the molecular revolution and people figure out that DNA is the thing which stores the information, which is passed along,

and evolution selects on the variation in the DNA as it's expressed as phenotype, and as that phenotype affects fitness, okay, a reproductive success. All that's the whole ballgame for biology. And I'm lucky that as a physicist who's trained in kind of mathematics and computation, I arrived on the scene at a time when we're going to solve this basic fundamental problem of biology

through brute force AI and machine learning. So that's how I kind of got into this, right? Now you ask as an entrepreneur, like, okay, fine, Steve, you're doing this in your office with your postdocs and collaborators on your computers and stuff, but what uses it, right? What uses all this stuff? The most direct application of this is in the following setting.

Every year around the world, there are millions of families that go through IVF. typically because they're having some fertility issues and also mainly typically because the mother is older, like typically in her 30s or maybe 40s. And in the process of IVF because they use hormone stimulation, they generally produce more eggs instead of one per cycle they might produce, depending on the age of the woman, anywhere between 5 or 10 or 20 or even I recently learned for young women who are hormonally stimulated if they're egg donors. they could produce 60 or 100 eggs in one retrieval cycle.

And then it's trivial, as you know, men produce sperm all the time. We're just producing it. You can fertilize those eggs pretty easily in a little dish, and you get a bunch of embryos, which they grow. They just start growing once they're fertilized. Now, the problem is if you're a family and you produce more embryos than you're going to use, you have what we call the embryos, embryo choice problem. You have to figure out, like, okay, have these 20 viable embryos,

which one am I going to use? And so the most direct application of the science that I described is, well, we can now genotype those embryos from a small biopsy. And I can tell you things about the embryos. I could tell you, hey, number four is an outlier for breast cancer risk. I would think carefully about using number four. Number 10 is an outlier for cardiovascular disease risk, you might want to think about not using that one. The other ones are okay. And so that is what genomic prediction does. And I think we work with two or three hundred different IVF clinics on six continents now. Yeah, yeah. So the super fascinating thing about this is that the disease users are talked about, or at least their risk profiles, they're polygenic. So you can have

thousands of SNPs, single nucleotide polymeroporisms, that determine whether you're going to get this disease or not. And so I'm really curious to learn, like, how you were able to transition to space and, like, how your knowledge of mathematics and physics was able to help you figure out how to make sense of all this data. Yeah, that's a great question. So, you know, first of all, again, like, I was kind of stressing, like, the fundamental scientific importance of all this stuff.

If you go into a slightly higher level of detail, which you were getting at with the individual snips or polymorphisms, those are individual locations in the genome where I might differ from you and you might differ from another person. And typically, if you just take pairs of individuals, each human, each pair of individuals will differ at a few million places in the genome. Okay. And that's what's controlling. That's why I look a little different than you.

And, you know, so. Just a little. Just, yeah, a little bit. I mean, you look better than me, but, you know, the question is the following. So a lot of times what theoretical physicists do is they have a little spare energy. They have some spare cycles, and they get tired of thinking about quarks or something. And they want to, like, maybe dabble in biology or they want to dabble in computer science or some other field. And the thing that we always have to do as theoretical physicists, we always feel like, oh, I have a lot of horsepower.

I can figure a lot of stuff out. Like, for example, Feynman helped design the first parallel processors at thinking machines. I got to figure out which problems I can actually make an impact on because I can waste a lot of time. Some people spend their whole life studying one problem, like one molecule or something or one biological system. And I don't have time for that. I'm just going to jump in and jump out. I'm a physicist, right? That's a typical attitude among theoretical physicists.

So the thing that I had to confront about 10 years ago was I knew the rate at which sequencing costs were going down. So I could anticipate we would get to the day today when there are millions of genomes with good phenotype data available for analysis. So that a typical run for us, a training run might involve almost a million genomes or half a million genomes or something. So the mathematical question is, what is the most effective algorithm given a set of genomes and phenotype information to build the best predictor? Right? So it can be boiled down to a very well-defined machine learning problem.

And it turns out for some subset of algorithms, that algorithms, there are theorems. They're actually performance guarantees that tell you, they give you a bound on how much data you need to capture almost all of the variation in the in the features. And so I spent actually a fair amount of time, like probably a year or two studying, these results. Very famous results, some of them were proved by a guy called Terence Tao, who's a Fields Medalism. And these are results on something called compressed sensing, which is a penalized form of high dimensional regression, which tries to build sparse predictors. Machine learning people might know it as L1 penalized optimization. And anyway, so the point is, the early, the very first paper we wrote on this was to prove that, you know,

using real genomic data that these theorems that were very abstract could be applied in order to predict how much data you would need to, quote, solve individual human traits. So we showed that you would need at least around a few hundred thousand individuals and their heights, their genomes and their heights to solve height as a phenotype. And we proved that in a paper using all this fancy math in 2012, I want to say the paper came out around 2012. And then around 2017, when we got a hold of half a million genomes, we were able to implement it in practical terms and show that our mathematical result from some years ago was correct. And the transition from low performance of the predictor to high performance.

There's a kind of what we call a phase transition boundary between those two domains, occurred just where we said it was going to occur. So some of these technical details are really just not understood, even by practitioners and computational genomics who are not quite that mathematical, they don't understand actually these results that in our earlier papers, they don't really know why we can do stuff that other people can't do or why we can predict how much data we're going to need to do stuff. It's not well appreciated even in the field. But if you look carefully when the big future AI in our future, in the singularity, looks back and says, hey, who gets the most credit for this genomics revolution

that happened in the early 21st century? They're going to find, that AI is going to find these papers on the archive in which we proved this was possible. And then five years later, we did it. And et cetera, et cetera. Right now it's underappreciated. But the future AI, that Roko's basalisk AI, when he looks back, is going to give me a little bit of credit for it. Yeah, yeah. So I was kind of a little interested in this a few years ago. And then at that time, I looked into like how these polygenic risk scores are calculated. And it was basically, you just find the correlation between the phenotype and the all the allele that correlate with it. And you, you know, you just add off how many copies of this allele do you have? What is the correlation? So it seemed like

and you just do a weight at some of that. So that seemed like a very, it just seems super simple, especially in an era where we have all this machine learning. And, but it seemed like they were getting good predictive results out of that. So what is the delta between how good you can get with all this fancy mathematics versus just like a very simple like some of correlations? Yeah. So you're absolutely right that the ultimate models that are used when you've done all the training and the dust settles. The models are very simple. They have an additive structure. So it's basically like I either assign a non-zero weight to this particular region in the genome or I don't. And then I need to know what is the weighting. But then the function is a linear function of,

it's an additive function of the state of your genome at some subset of positions. So the ultimate model that you get is very simple. Now, if you go back 10 years when we were doing this, there were lots of claims that it was going to be super nonlinear, that it wasn't going to be additive the way I just described. It was going to, there were going to be lots of interaction terms between regions. Some biologists are still convinced that's true, even though we already know, like we have predictors that don't have interactions. Okay. The other question which is more technical is that

there are, in any small region of your genome, the state of the individual variance is highly correlated because you inherit them in chunks. And so you need to figure out which one of those. you want to use. You don't want to activate all of them because you might be overcounting. So that's where this L1 penalization sparse methods, they force the predictor to be, you know, sparse. And that is a key step. Otherwise, you might overcount. You might have 10, 10 different variants close by that have roughly the same statistical significance if you just do

some simple regression math. But then you don't know which one of those tend to use, and you might be overcounting effects or undercounting effects. So what you end up doing is a super, high dimensional optimization where you only activate, you grudgingly activate a snip when the signal is strong enough. And once you activate that one, the algorithm has to be smart enough to penalize the other ones nearby and not activate them because you're overcounting effects if you do that. So there's a little bit of subtlety into it, in it, but the main point which you made, which is that the ultimate predictors, which are very simple and additive, just sums over effect sizes times states, actually works really well. And that is related to a

statement about the additive structure of the genetic architecture of individual differences. So in other words, it's kind of weird that the ways that I differ from you are merely just because I have more of something and you have less of something and it's not like, oh, these things are interacting in some super, you know, incredibly un-understandable way. And so that's a very deep thing, which again is not appreciated that much by biologists yet, but over time I think they're going to figure out that there's something interesting here. Right, no, I thought that was super fascinating. And I commented about that on Twitter.

What is really interesting about that is, I guess, two things. One is you have this really interesting evolutionary argument about why that would be the case. You might want to explain. And the second is, it makes you wonder if it's just, if just becoming more intelligent, it's just a matter of, like, turning on certain snips. It's not a matter of, like, all this incredible optimization that, you know, it's like, solving a Sudoku puzzle or anything. If that's the case, then why aren't we already, why hasn't the human population already been selected to be maxed out on all these traits,

if it's just a matter of a bit flip? Yeah. So, okay, so the first issue, which is how, you know, why is this genetic architecture so simple, surprisingly simple? And again, 10 years ago, we didn't know it was going to be simple. So when I was checking to see whether this is a field that I should go into, because either we are capable or not capable of making progress. We had to study the more general problem of the nonlinear possibilities as well, but eventually we realized that probably most of the variance was going to be captured in an additive way, so we could narrow down the problem quite a bit. There are evolutionary reasons for this. So there's a famous theorem by Fisher, who's the father of population genetics and also of really

what you call frequentist statistics. And so Fisher proved something called the fundamental, Fisher's Fundamental Theorem of Natural Selection, which says that if you impose some selection pressure on a population, the rate at which that population responds to the selection pressure, like say it's the bigger rats that outcompete the smaller rats, at what rate does the rat population then start getting bigger? He showed that it's dominated by the additive variance, that that dominates the rate of evolution. And it's easy to understand why. If it's a if it's a nonlinear mechanism that you need to make the rat bigger, when you sexually reproduce and that gets chopped apart,

you might break the mechanism. Whereas if each little allele has its own independent effect, you can just inherit them without worrying about breaking the mechanisms. So it was well known for, at least among a tiny population of theoretical population biologists, that added variance was the dominant way that populations would respond to selection. So that was already known. And the other thing is that humans have been through a pretty tight bottleneck and we're not that different from each other.

So it's very plausible to me that if I wanted to edit a human embryo and make it into a frog, then there's all kinds of nonlinear, subtle things I have to do. But all those very nonlinear complicated subsystems are fixed in humans. You have the same system as I do. You have the non, the human not frog or ape not frog version of that region of DNA. and so do I. But the small ways in which we differ are just these little additive switches, mostly little additive switches. And so that's the deep scientific discovery from the last, say, five, ten years of work in this area. Now, you were asking about why evolution hasn't like completely, quote, optimized all traits in humans already. Now, I don't know if you ever do deep learning

or very high dimensional optimization, but you realize, like, in that high dimensional space, you're often moving on a surface which is slightly tilted, so you're getting gains, but it's also kind of flat. So even though you, like, scale up your compute or data size by an order of magnitude, you don't move that much farther. You get some gains, but you're never really at the global max of anything in these high dimensional spaces. I don't know if that makes sense to you, but it's quite plausible to me that two things are important here. One is evolution has not had that much time to optimize humans. And what do you mean by optimization?

Because the environment that humans live in has changed radically in the last 10,000 years. Like, for a while we didn't have agriculture, now we have agriculture, now we have swipe left, if you want to have sex tonight. You know, the environment didn't stay fixed. And so when you say fully optimized for the environment, what do you mean?

The ability to diagnose matrices might not have been very adaptive 10,000. years ago. It might not even be adaptive now. But anyway, so it's a complicated question. One can't reason that naively about, oh, well, if God wanted us to be 10 feet tall, we'd be 10 feet tall. Or if it's better to be smart, my brain would be like this, this big or something. So you can't reason that naively about stuff like that. I see. Yeah. Okay. So I guess it could make sense, for example, with certain health risk, like the thing that makes you more likely to get diabetes or heart disease today might be, I don't know what the pliatoric effect of that could be, but maybe that's not

that important one. You're not that obese. Let me just point out that that most of the diseases that we care about now, most of them, not the rare ones, but the common ones, they manifest when you're like 50, 60, 70 years old. And there was never any evolutionary big advantage, I think, of being super long lived, right? So there's even a debate about whether like, okay, if the grandparents are around to help raise the kids, that raises the fitness a little bit. of the family unit. But most of the time in the past, and most of our evolutionary past, humans just died, you know, fairly early. And so a lot of these diseases would never have been optimized against evolutionarily. But we see them now because we live under such good conditions.

We can, you know, people regularly approach 80 or 90 years. Regarding the linearity and addativity point, I was going to make the analogy, and I'm curious if this is valid. But when you're programming, one thing that's good practice is to have all the implementation details in separate functional. calls or separate programs or something, and then have your main, have your main, you know, a loop of operation just be call different functions, like do this, do that, so that you can easily comment stuff away or change arguments. And this seemed very similar to that, where you have, just by turning these sneeps on

and off, you can change what the next offering is going to be. And you don't have to worry about, like, actually implementing the, whatever the underlying mechanism is. Well, what you said is related to what Fisher proved in his theorems, which is that, you know, if suddenly it becomes advantageous to have X, like white fur instead of black fur or something, it would be best if there were little levers that you could move somebody from black fur to white fur continuously by just modifying those switches in an additive way. It just turns out for sexually reproducing species where the DNA gets scrambled up in every generation, it's better to have switches of that kind. And so the other point related to your software analogy is that there are,

there seem to be modular, fairly modular things going on in the genome. So when we looked at, we were the first group to, I think we had like initially like say 20 major disease conditions we had decent predictors for. And we just started looking carefully at the, just something trivial as the overlap of, you know, my sparsely trained predictor turns on, uses these features for diabetes, but it uses these features for schizophrenia. And how much overlap? Just the stupidest metric is like how much overlap or variants accounted for overlap is there between pairs of disease conditions? And it's very modest. It's actually the opposite of what naive biologists would say when they talk about pliotropy or they're just disjoint. They're just disjoint regions of

your genome that are governing certain things. And so why not? You have three billion base pairs. There's a lot you can do in there. There's a lot of information in there. So you can have, if you need a thousand to control diabetes risk, I can have, I think I estimated you can easily have a thousand roughly independent traits that are just disjoint in their genetic dependencies. And so if you think about like D&D, like your strength and your decks and your wisdom and your intelligence and charisma, Those are all disjoint. They're all just independent variables. So it's like a seven-dimensional space that your character lives in.

Well, there's enough information in the few million differences between me and you. There's enough for a thousand dimensional space of variation. Like, oh, how big is your spleen? My spleen's a little bit smaller. Yours is a little bit bigger. That can vary independently of your IQ. Oh, it's big surprise. The size of your spleen can vary independently of the size of your big toe.

Oh, yeah, yeah. There's about a thousand. If you just do information theory, there's about a thousand different parameters I can vary independently with the number of variants that I have between me and you. So, and this thing, because you understand some information theory, is like kind of trivial to explain, but try to explain to a biologist. You won't get very far. Yeah, yeah. Do the log two of the number of, uh, uh, is that basically how you do it? Yeah, okay.

That's all it is. I mean, well, I mean, well, we, it's in our, it's in our paper. like we basically look at, okay, how many, how many variants are typically accounting for most of the variation for any of these major traits? And then imagine that they're mostly disjoint. Well, just how much length of DNA? How many variants do you need then to independently vary a thousand traits? Well, it's a few million differences between me and you are enough, right? So it's trivial.

It's very trivial math. Once you understand how to reason about information theory, then it's very trivial. But it ain't trivial for theoretical biologists as far as I can tell. But the result is so interesting because I remember reading in the selfish gene that, like, he hypothesizes, the reason we have aging, or one of the possible reasons we have aging, is that there's antagonistic pliotropy. There's something that makes you healthier when you're young and fertile that makes you unhealthy when you're old. And evolution would have selected for such a tradeoff because when you're young and fertile is when evolution and your young and you're, genes care about you. And so, but if there's enough space in the genome before you, where these tradeoffs are not necessary, then this may be like a bad explanation for aging,

or do you think I'm straining the analogy? No, no, you're, it's, it's, it's, I love your interviews because, uh, the point you're making here is, is really good. So Dawkins, who is a kind of evolutionary theorist, but from the old school, when they had almost no data, okay, to deal, you know, you can imagine how much data they had compared to today. He would like to tell you a story about a particular gene that maybe it has this positive effect when you're young, but it makes you age faster, so there's a trade-off. And, you know, we know about things like sickle cell anemia and, you know, we know stories like that. And no doubt there are stories like that, which are true about specific variants in your genome. But that's not the general story.

The general story, which we only discovered in the last five years, is that almost every trade is controlled by thousands of variants, and those variants tend to be disjointed. from the ones that control the other trait. So they weren't wrong, but they didn't have the big picture. Yeah, I see. And then, yeah, so you had this paper, I think it was polygenic health and eggs, general health and disease risk. And then you showed that with 10 embryos, you could increase disability-adjusted life years

by four, which is like a huge increase of, like, if you think about like if you could just live four years longer in the healthy state. Yeah, what's the value of that? What would you pay to buy that for your kid? Right, yeah. But I don't know, this seems like this, going back to that earlier question about the tradeoffs or like about why this hasn't already been selected for, if you're right and there's no like tradeoff to do this, just living four years older, even if that's been on your fertility, just like being a grandpa or something, that seems like an unmitigated good. So why, it's kind of mysterious that that hasn't already been, you know, selected for. So, no, I'm glad you're really asking about these questions because these are things that. that people are very confused about, you know, even in the field. So first of all, let me say,

if you have a trait that's controlled by a 10,000 variance, like height is controlled by order 10,000 variance and probably cognitive ability a little bit more, the square root of 10,000 is 100. Okay, so if I could come to this little embryo and I said, I want to give it one extra standard deviation of height plus one standard, deviation, if I only need to edit 100.

I only need to flip 100 minus variance to plus variance. These are very rough numbers, but, you know, one standard deviation is like the square root of n, right? If I flip a coin n times and I want a better outcome in terms of number ratio of heads to tails and I want to increase it by one standard deviation, I only need to flip square root of n heads because if you flip a lot, you're going to get a very narrow distribution peaked around a half. and the width of that distribution is square root event.

So once I tell you, hey, your height is controlled by 10,000 variants, and I only need to flip 100 genetic variants to make you one standard deviation for a male. That would be three inches tall, two and a half for three inches taller. Suddenly you realize, wait a minute, there's a lot of variants up for grabs there. I mean, if I could flip 500 variants in your genome, I would make you five standard deviations taller. you'd be like seven feet tall, and I didn't have to do that much work. And there's a lot more variation where that came from, okay,

because I only flipped 500 out of 10,000. I could have flipped even more, right? So there's this kind of quasi-infinent well of variation, which evolution or genetic engineers could act on. And again, the early population geneticists who breed corn, who breed animals, they know this. This is actually something they explicitly know about because they've done calculations. Now, interestingly, the human geneticists who are mainly concerned with diseases and stuff,

are often not familiar with what the math that the animal breeders already know. And you might be interested to know that the milk you drink comes from heavily genetically optimized cows who are actually bred artificially using, and they're using almost exactly the same technologies that we use at Genomic Prediction, but they're doing it to optimize like milk production and stuff like this. So there is a big well of variance. It's a consequence of this super multi-polygenicity of the trait. And it does look like people could, coming back to your question about longevity,

it does look like people could, quote, be engineered to live much longer than they currently do by just say flipping the variance that make, that reduce risk. for individual diseases that tend to shorten your life. And then the question is back to, well, why didn't evolution give us lifespans of a thousand years? Like back in the Bible, people in the Bible used to live for a thousand years. Why don't we, I mean, that probably didn't really happen. But the question is you have this very high dimensional space

and you have a fitness function and how big is the slope in a particular direction of that fitness function? Like how much more successful we're productively would Joe have been, Joe Caveman have been, if he lived to be 150 instead of only, you know, 100 or something. And there just hasn't been enough time to, you know, explore this super high dimensional space. That's the actual answer. But now we have the technology. We're going to fucking explore it fast now. That's the point that people, you know, the big light bulb should go off. Like, no, we, we're mapping this.

space out now, pretty confident in 10 years or so, the CRISPR gene editing technologies will be ready for massively multiplexed edits. And we're going to start navigating in this high dimensional space as we like. So that's the more long-term consequence of these scientific insights. Yeah, that's super interesting. And what do you think will be the plateau for a trait, like, you know, how long you live. Well, is, because four is, I guess, with the current data and techniques. You think it could be significantly greater than that? Well, we did a very simple calculation, which amazing that it gives the kind of the right

result. We said, like, given this polygenic predictor that we've built, which isn't perfect, I mean, it's going to improve a lot as we get more data. Given this polygenic predictor for overall health, which is used in selecting embryos today, if you just say, like, well, out of a billion people, what's the best person, typically? What would their score be on this index? And then how long would they be predicted to live? It's about 120 years.

So it's actually spot on. It's basically that's one in a billion type person lives to be like 120 years old, roughly. How much better can you do? Probably a lot better. Probably. I mean, I don't want to speculate, but because other effects, nonlinear effects, things that we're not taking to account will start to pull. play a role at some point. So it's a little bit hard to estimate what the limiting, what the true

limiting factors will be. But the one statement which is super robust, and I'll stand by, I'll debate any Nobel laureate in biology or whatever who wants to talk about it, there's clearly a lot of variance available to be selected on or edited. There's, that's just, there's no question about that. And that's been established in animal breeding and plant breeding for, you know, a long time now. So we can, if you want a chicken that grows to be this big instead of this big, you can do it. If you want a cow that produces literally 10 times or 100 times more milk than a regular cow, you can do it. The egg you ate for breakfast this morning, those bioengineered chickens, they lay almost an egg a day. A chicken in the wild lays like an egg a month.

How the hell did we do that by genetic engineering? That's how we did it. Yeah, yeah. And that was just brute our artificial selection. No fancy machine learning there. Last 10 years, last 10 years, it's gotten sophisticated machine learning, genotyping of chickens, artificial insemination, modeling of the traits using ML. Last 10 years. Basically, for cow breeding now, it's totally done by ML now.

I have no idea. That's super interesting. What is, so you mentioned that you're accumulating data and improving your techniques over time. Is there a first mover advantage to a genomic prediction company like this? Or is it just whoever has a newest best algorithm for going through the biobank data? That's another super question. So for your entrepreneurs in your audience, I would say in the short run, if you ask like, like, what how's, you know, what's, what the, what should the valuation of GP be?

You know, that's, that's how the venture guys would want me to answer the question. There is a huge first mover advantage because very important is the channel relationships between us and the clinics. And nobody's going to be able to get in there very easily when they come later because we're developing trust and a big track record with clinics all over the world and we're well known. Could 23 and me or some company? that has a huge amount of data and if they were to actually get better AIML people working on this,

could they kind of blow us away a little bit and build better predictors because they have just much more data than we do? Possibly, yes. Now there's a core expertise that we have in doing this kind of work for years and years and years that we're just really good at it. And so even though we don't have as much data as 23 and me, we might still our predictors are better than theirs right now. And I'm out there all the time working with bio banks all around the world, like in countries like, I don't want to say all the names, but other countries trying to get my hands on as much data as I can. But there may not be a lasting, you know, advantage beyond the actual business channel connections to that particular market. It may not be a defensible, purely scientific moat around the company. We do have patents on specific technologies about how to do the genotyping or how to do error correction on embryo DNA and stuff like this.

We do have patents on stuff like that. But this general idea of like who's going to be the best at predicting human traits from DNA, it's unclear who's going to be the winner in that race. Maybe it'll be the Chinese government in 50 years. Who knows? Yeah, that's interesting. I mean, if you think about like a company like Google, like theoretically it's possible you can come up with a better algorithm than the page rank and then beat them. But it just seems like probably the engineer at Google is going to be the one that comes up with whatever edge case or whatever improvement is possible. That's exactly, see, it's exactly what I would say.

I would say like, yeah, maybe, I mean, page rank actually by now is totally deprecated. But even if somebody else comes up with a somewhat better algorithm or somewhat better, maybe they have a little bit more data, if you have a team that's been doing this for a long time and you're really focused and good, it's still tough to beat you, especially if you have a lead in the market. Yeah. No, no. And then, so what layer of the stack do you, so you guys are, are you guys doing the actual biopsy or is it just that they upload the genome and you're the one processing and just giving recommendations? Is it just like an API call basically? Or? So it's great. I love your question. So it is totally standard. Every good IVF clinic in the world regularly takes embryo biopsies. So that's totally standard. It's like a lab tech doing that. Okay. And then what happens is they take the little sample and they put it on ice and they just ship it. And the DNA as a molecule is extremely robust and stable. In fact, my other startup solves crimes that are 100 years old.

from DNA that we get from some semen stain on some rape victim, you know, serial killer victims, brostrap. We've done stuff like that. Jack the Ripper, when are we going to solve that mystery? If they can give me samples, we can get into that. For example, we just learned that you can recover DNA pretty well from the, like if someone licks a stamp and puts it on their correspondence. So, I mean, if you can do Neanderthals, you can do a lot for solving crimes.

So in the IVF workflow, our lab, which is in New Jersey, can service every clinic in the world because they just take the biopsy, they put it in a standard shipping container, and they just send it to us. And then we actually genotype the DNA in our lab, but we've actually trained a few of the bigger clinics to actually do the genotyping on their site. And at that point, it's just like they upload some data into the cloud, and then they get back some stuff from our platform. So at that point where it's going to be the whole world, man, every human, every human who wants their kid to, you know, be healthy and, you know, get the best they can, it's going to, that data is going to come up to us and it's going to, the report is going to come back down to

their IVF position. Right. Yeah. Which is great. If you think, uh, let's say you think there's a potential that this technology might get regulated in some way. Um, you could just have like, just go to Mexico or something, have them upload the, have them upload the genome. Uh, you know, don't care what they uploaded from, and then you can just get the recommendations there. Yeah, I think we're going to evolve to a point where, because the genotyping technology is getting better and better, eventually we are going to be out of the wet part of this business and only in the bit and cloud part of this business, because eventually the clinic, no matter where it is, they're going to have a little sequencer, which is this big, and their tech is going

to do it, and then they're just going to hit upload, and then they get the report back like three seconds later from us for the physician to look at and the parents can look at it on their phone or whatever. Actually, we're basically there, actually, with some clinics. So, yeah, it's going to be tough to regulate because it's just bits, right? So you have the bits and you're in some repressive, terrible country, you know, that doesn't allow you to select for some special trades that people are nervous about. But you just upload it to some vendor who's in Singapore or in, you know, some free country. And, and they, they give you the report back. It doesn't have to be us. I mean, we're not, we don't do those, we don't do the edgy stuff. We only do the health-related

stuff right now. But if you want to know how tall this embryo is going to be, I'll tell you a mind-blower, when you do face recognition in AI, you're basically mapping someone's face into a parameter space of like, I think it's on the order of hundreds of parameters, right? Each of those parameters, is super heritable. So in other words, if I take two twins and I measure their, I photograph them and the algorithm gives me the value of that parameter for twin one and twin two, they're very close, obviously. That's why I can't tell the two twins apart. The face recognition can ultimately tell the twins apart, the really good face recognition, but you can just conclude that almost all these parameters are the same for those twins. So it's highly heritable. So we're going

to get to a point soon where I can do the inverse problem. where I have your DNA and I predict each of those parameters in the face recognition algorithm. And then from that, I reconstruct the face. So I say, like, this embryo, when she's 16, this is what she's going to look like. When she's 32, this is what she's going to look like. And I'll be able to do that for sure. It's just a data, you know, it's just an AIML problem right now. But the basic biology is clearly going to work.

So then you're going to be able to say, like, oh, look, here's a report. poor. Look, let's, Enrio 4 is so cute. Why don't we, you know, we don't,

we don't do that, but it's going to be possible. Right. Before you get married, you'll want to see what they, what their genotype implies about their, faces longevity. Yeah,

it's interesting. You hear stories about these cartel leaders who will, you know, get plastic surgery or something to evade, um, evade the law. You could just have,

um, a check where you like, lick this, like, lick this, uh, lick this, uh, lick this, and then,

yeah, does that match the face? that you would have had five years ago when we caught you on tape. Yeah, well, and also, you don't, it's a little bit back to old school Gattaca, but you don't even need the face.

You can just say, like, I'm going to take a few molecules of, you know, skin cells from you. I'm going to take a few skin cells off of you and just unotype you. I know exactly who you are. So I've had conversations with the spooky intel folks about, you know,

they're very interested in like, oh, if some Russian diplomat comes in, and we think he's actually a spy, but he's, you know, with the embassy over there, and he has a coffee with me, and I save the cup and send it to my brother, my buddy at Langley, can we figure out who this guy is and that he has a daughter who's going to chote, you know, can do all that now. Oh, that's true. If that's true, then like in the future, people will be so concerned, like world leaders or something, whether we're visiting a foreign country, country, they're not going to want to eat anything, drink it.

Like, they'll be wearing like a hazmat suit to make sure they don't, like, lose a hair follicle. Yeah, the new, the next time Pelosi goes, she's going to be in like a space suit. If she cares. Or the other thing is they're just going to give in. They're just going to be like, yeah, my DNA is everywhere. If I'm a public figure, my DNA, I can't track it. It's all over.

Yeah, but the thing is, like, there's so much, as you, I'm sure you know, there's, like, speculation that Putin might have cancer or something. If we have his DNA, we can just see, like, oh, actually, like, his probability of having cancer at age 70 or whatever he is is, you know, 85%. So, yeah, that's like a verified rumor. You know, that would be interesting. I don't think that would be very definitive. Like, I don't think we'll reach that point where you could say,

yeah, Putin definitely has cancer because of his DNA, which I could have known when he was an embryo. I don't think it's going to reach that level, but we could say, yeah, he is high risk for this kind of cancer. Yeah, you could say that. Yeah, yeah, yeah. So if, if, if, like, let's say in 50 years or 100 years, all the majority of population is doing this.

And if that means that the diseases that are highly heritable get pruned out of the population, does that mean will only be loved with the diseases that are like lifestyle diseases? So you won't get like breast cancer anymore, but you will still get, you know, you'll still get like fat or I don't know, whatever, lung cancer from smoking or something. I think it's hard to discuss the asymptotic limit of what's going to happen here. I'm not very confident about making predictions like that. You know, it could be that we'll get to the point where everybody is, well, everybody who's rich or has been through this stuff for a while, especially if we get the editing working, is super low risk for all the top 20 killers, the diseases which, you know, have most life expectancy impact. And yeah, maybe those people live to be 300 years old naturally.

I don't think that's excluded at all. So I think that's within the role of possibility. But, you know, it's going to happen for a few lucky, you know, Elon Musk like people before it happens for schlubs like you and me. You know, there's going to be very angry inequality protesters about, you know, the Trump grandchildren who models predict will live to be 200 years old. Right. Yeah, yeah, yeah.

Like, people are not going to be happy about that. Yeah. That's so interesting. And, okay, so one way to think about these different embryos is, like, if you can produce multiple embryos, you can get to select from one of them. Each of them is like, each of them is like a call option, right? And therefore, you probably want to optimize for volatility as much or if not more than just the expected value of the trait.

And so I'm wondering if there's mechanisms where you can, I don't know, like increase the volatility in meiosis or in some other process. So you just get a higher variance that you can just select from the tail better. Well, I'll tell you something related to that, which is quite amusing. So I had conversations with some pretty senior people at the company that owns all the dating apps. So you can look up, you can figure out what company this is, but they own Tinder and match and stuff like this. And they're kind of interesting, interested in, wow, what if we have a special feature where instead of Tinder or gold or platinum, you upload your genome and you match. We talk about how well you match the other person

based on your genome. Actually, one person told me something which was really shocking is that apparently guys lie about their height on these apps. And if you could have a DNA verified. Truly shocked. Truly shocked. If you could have a DNA verified height on, because our accuracy is like, an inch or something. Right. So it would prevent like really gross distortions. Like someone claims there is 6-2 and they're actually 5-9. Probably the DNA could say that's unlikely actually.

But no, the application to what you were discussing is more like, let's suppose that we're selecting on intelligence or something. And let's suppose that the regions where your girlfriend has all the plus stuff. stuff is complementary to the regions where you have your plus stuff. So we could model that and said like your kids, just because of that, you know, the complementarity of the structure of your genome in the regions that affect intelligence, you're very likely to have some super smart kids way above your, the mean of your, you and your girlfriend's values. So you could actually say things like, yeah, it's better for you to marry that girl than that girl. you know, as if you're going to go, as long as you're going to go through embryo selection, we can throw out the outlier, the bad outliers.

That is so fast. All that's technically feasible. And I think actually it's true that one of the earliest patent applications, they'll, they'll all deny it now. What's her name? Gosh, I can't remember the CEO of 23 and me. Wajitski. Yeah.

She'll deny it now. But I think if you look in the patent database, the very, one of the, very earliest patents that 23 and me filed when they were like still a small startup was about exactly this is like advising parents about mating and how their kids would turn out and stuff like this. So, you know, we don't even go that far in GP. We don't even talk about stuff like that, but they were thinking about it when they founded 23 and me. That is unbelievably interesting. And by the way, speaking of high, this just heard to me, but, you know, it's like supposed to be highly heritable, but especially people in like Asian countries who, uh, we have the experience

of like having grandparents that are much shorter than us and then parents that are shorter than us, which is just that like the environment has like a big part to play in it, just like malnutrition or something. Yeah, so how do you scare that, uh, the fact that like often our parents are shorter than us with the idea that like height is supposed to be super heritable? Another great observation. So the correct, the real correct scientific statement is we can predict height for people who are born, who will be born and raised in a favorable environment. So in other words, if you live close to a McDonald's

and you're not, you know, you can afford all the food you want, then the height prediction, the height phenotype becomes super heritable because the environmental variation now doesn't matter very much. But you and I both know, if we go back to where our ancestors came, from people are a lot smaller. And also, if you look at how much food, how many calories and how much protein and calcium they eat, it's totally different than what I ate and what you ate growing up. So we're not saying, we're never saying the environmental effects are zero. We're saying for people raised in a certain very favorable environment, maybe the genes are a cap on what can

be achieved. And we can estimate, you know, we can predict that. Yeah. So in fact, in our data, actually, we have, like, I have data from Asia where, yeah, you can see there clearly are much bigger environmental effects, age effects, actually, just older people for fixed polygenic score on the trait are much shorter than younger people. Oh, okay, interesting. Yeah, that actually raises the next question I was about to ask, which was how applicable are these scores across, you know, different ancestral populations? Huge, huge problem right now because most of the data is from Europeans.

And what happens is that as you, if you train a predictor in this ancestry group and you go to a more distant ancestry group, there's a fall off in the quality of prediction. And this is, again, this is like frontier questions. So we don't know the answer for sure. But most people believe or many people believe that what happens is that there's a certain correlational structure in each population. where if I know the state of this snip, I can predict the state of these neighboring snips. And that is a product of the mating patterns and the ancestry of that group.

And sometimes the predictor, which is just using statistical power to figure things out, will grab one of these snips as a tag for the truly causal snip that's in there. It doesn't really know which one is truly causal. It's just grabbing a tag. But the tagging quality falls off you then go to another population.

Like this was a very good tag for the truly causal snip in the British population, but it's not so good a tag in the South Asian population for the truly causal snip, which we hypothesize is the same. It's the same underlying genetic architecture in these different ancestry groups. We don't know that's a hypothesis. But even so, the tagging quality falls off. So my group, you know, we spend a lot of our time looking at performance of predictor, trained in population A on distant population B and doing all the stuff modeling and trying to

figure out, trying to test hypotheses as to whether it's just the tagging decay, which is responsible for most of the fall. So all of this is an area of very active investigation. I think it'll probably be solved in five years. The first really big bio banks that are non-European are coming online. And so I think we're going to solve it, you know, in some number of years. Oh, what does the solution look like, I guess? Because if you don't know, unless you can identify like the causal mechanism by which each snip is having an effect, how can you know that something is a tag or whether it's the actual underlying, you know, switch? The resolution will be, again, the nature of reality determines how this is going to go. So, and we don't know the innate underlying biology. If it's true, and this is the amazing thing. Like, people are. about like human biodiversity and all this stuff. And we don't even know whether the specific mechanisms that say predispose you to being tall or to having heart disease are the same in these different ancestry groups.

We assume that it is, but we don't know that. And you know, like as we get further away, like to Neanderthals or Homo erectus, you might be like, yeah, they have a slightly different architecture there than we do. But let's assume that the causal structure is the same for South Asians and for British people. Okay? And then it's a matter of improving the tags. And you might say, wait a minute, Steve, how do I know? How do I know? If I don't know which one is causal, what do you mean by improving the tags?

This is a machine learning problem. So the question is, if there's a SNIP, which when I use it across multiple ancestor groups, is always coming up as very significant. Maybe that one's truly causal. As I vary the tagging correlations in the neighborhood of that SNP, I always find that that one is in the intersection, the intersection of all these different sets. That makes me think that one's going to actually be causal.

So that's a process we're engaged in now, is to try to basically do that. It's basically just a machine learning problem, but we need data. That's the main issue. Yeah, I was kind of hoping that wouldn't be possible because one worry you might have about this research is that, you know, like, it itself become taboo or it caused other sorts of bad social consequences if you can like definitively show that on certain traits there's differences between ancestors or populations, right? And I was kind of hoping that maybe there's like just an evasion button where like, yeah, we can't say because they're just tags and the tags might be different between different ancestors or populations. But I guess with better machine learning, we'll know.

That's a situation we're in now where you have to do some fancy analysis. If you want to claim like Italians literally have lower height potential than Nordics, which is possible. And there's been a ton of research about this because there's signals of selection. It looks like the the alleles, which are activated in height predictors, it looks like they've been under some selection between North and South Europe over the last 5,000 years, for whatever reason. We don't know the reason, but this is a thing which is debated by people who study molecular evolution.

But suppose it's true, okay? And then what that would mean is that when we finally get to the bottom of it and we find all the causal low-sipher height, literally the average value for the Italians is lower than the average value for the people living in stock. And that might be true. People don't get that excited. They get a little bit excited about height, but they would get really excited if this were true for some other traits, right? Suppose like your extroversion, you know, the causal variance affecting your level of extroversion is systematic, the average value of those weighted, the weighted average of those states is different in, you know, Japan versus Sicily. Right? People might freak out over that. Right.

I'm supposed to just say that's obviously not true. It's obviously not true. Can't be true. How could it possibly be true? Because there hasn't been enough evolutionary time for those differences to arise after all. It's not possible that despite what looks to be the case for height over the last 5,000 years in Europe, no other traits could possibly have been differentially selected for over the last 5,000 years. That's the really dangerous thing.

there are few people who understand this field well enough to understand what you and I just discussed and who are so alarmed by it that they're just trying to suppress everything. There are people like that. But most of them actually don't really follow it at the technical level that you and I are discussing. So they're just like kind of instinctively negative about it, but they don't really understand it very well. That's good to hear because that's, yeah, in a lot of other spaces you see this pattern that by, the time that somebody might want to regulate or in some way interfere with some technology or some information, it already has achieved wide adoption.

You could argue that that's the case with crypto today. But if it's true that a bunch of IVF clinics across the world are using these scores to do selection and other things, yeah, by the time that people realize the implications of this data for other kinds of social questions, by that time, this has already be like a natural consumer technology, hopefully. I think that, I think that's true. I think the main outcry will be if it turns out that there are really big gains to be had and only the billionaires are getting them.

But that might have the consequence of causing countries to make this free part of their national health care system. So Denmark, Israel, they pay for IVF. for infertile couples. So it's part of their national health care system. And they're pretty aggressive about genetic testing. In Denmark, one in ten babies born now is born through IVF. Right.

So, yeah. So it's not clear how it's going to go. But yeah, I mean, we're in for some fun times. There's no doubt about it. Yeah, I guess one way ago is some countries decided to ban it all together. And another way ago is countries decide to give everybody free access to it. Yeah, exactly. If you had to choose between the two, I guess you would want to go for the second one, which I guess would be the hope. And maybe only those two are compatible with people's, I don't know, their moral intuitions about this kind of stuff. It's very funny because most wokest people today hate this stuff. But most progressives, like Margaret Sanger or, you know, anybody who was progressive, the intellectual, well, in some sense, the forebears of today's woke.

in the early 20th century, they were all what we would call today eugenesis, because they were like, oh, shoot, thanks to Darwin, we now know how this all works, and we should take steps to keep society healthy and not in a negative way where we kill people we don't like, but we should just help society do healthy things and when they reproduce and have healthy kids. Right. And so now this whole thing has just been flipped over among progressives. So, yeah. It's sad. Yeah, even in India, like that was like very recent. recently, less than 50 years ago or what, when, Indira Gandhi, you know, she's like the left side of India's political spectrum. And yeah, she obviously she was infamous for putting on these like forced sterilization programs.

And yeah, so, you know, I don't want to credit the person, but somebody made an interesting comment. They wouldn't want their name associated with this maybe, but somebody made an interesting comment about this where they said, they were asked like, oh, is it true that progressives in history, the history always told towards progressives. and if so aren't, isn't everybody else doomed, aren't their views doomed? And the person made a really interesting point, which is that, yes, whatever we consider left at the time tends to be winning, but what is left changes a lot over time, right?

So in the early 20th century, prohibition was a left cause, right? It was a progressive cause. And then, you know, that changed. Now that's no law. I mean, the opposite is the left cause. Yeah, now legalizing pot is progressive. Exactly. So the way, if the, you know, if conquest, second law is true

and everything just till it's left over time, just change what left is, right? That's a solution. Yeah, absolutely. I mean, of course, one can't demand that any of these woke guys be like intellectually self-consistent or even like say the same things from one year to another. But if one could, you know, you wonder what they think about these literally communist Chinese. I mean, these are literally communists. They're recycling huge parts of their GDP to help the poor and do all the other.

stuff that, you know, medicine is free. Everything, you know, education is free, right? They're literally socialists. They're literally communist. But in Chinese, the Chinese characters for eugenics is a totally positive thing. It's just like healthy production. It means healthy, well, that's actually what it means in Greek, too, but more or less. But the whole viewpoint on all this stuff is like 180 degrees off in, in East Asia compared to here. And even among the literal communists, You know, so. So let's talk about one of the traits that people might be interested in potentially selecting for, which is intelligence. Do we, what is the potential that we'll be able to actually acquire the data to be able to correlate the genotype with intelligence?

Well, that's the most personally frustrating aspect of all of this stuff. Like, if you ask me, like, 10 years ago when I started doing this stuff, what did I think we were going to get? I think everything is gone kind of on the optimistic side of what I would have predicted, so everything's good, you know, didn't turn out to be interactively nonlinear. It didn't turn out to be interactively polytropic. You know, all these good things, which nobody could have known a priori how they would work, turned out to be good for gene engineers of the 21st century.

The one thing that's frustrating is because of crazy wokeism and fear of crazy wokeists, the most interesting, what I consider the most interesting phenotype of all is lagging because everybody's afraid, even though there are very good reasons for medical researchers to want to know the cognitive ability of people in their studies. For example, when you want to study aging or decline of cognitive. of function, memory in older people, it's you want to have baseline measurements of how good their cognitive function was when they were younger, right? So they're very good reasons for why you want to have all this data.

But researchers are afraid because it's also linked to all these controversial social issues. And so the amount, there's just a ginormous amount of genomic data where there's actually no cognitive measurement attached as a field to that data. which would have been very cheap to measure. Again, wokeists hate this, but I can measure your IQ on like a 12-minute test, no problem, right? I mean, not with perfect accuracy, but I can get a pretty, I can get a very useful measurement. So I just take, like the NFL has this thing called the Wonderlich, which every player that's being considered for the draft is asked to take this wonder-lick. You can go back and look at the Wonderlick scores of every NFL player.

It's a short test. It's like 12 minutes long or something. And it's pretty highly correlated. It's like probably correlates point eight or point-9. point eight maybe with a more fulsome IQ measurement. So it would be trivial and inexpensive to gather this data. And then once we have my prediction from this earlier math that I was talking about, is that when you get to order a million, it could be one million,

it could be two million, well phenotyped people and genomes, we would be able to build a pretty decent IQ predictor that might have a standard error of maybe 10 points or something. So that would be incredibly for science, just unlimited, interesting stuff in there, but not getting done. Yeah. And if there are differences between, I mean, differences in how things are tagged between different ancestral groups, I'm not talking about the average differences or anything, just how the genotype is tagged. And if the Chinese do this first, then that's like they have an advantage that can't be transferred over, I guess, right? because it's only applicable or advantageously applicable to their population.

No, that's a great point. You can easily imagine, even in a small country like Singapore or Taiwan, has enough data to do this, no problem, Estonia. And they could do it and have this thing working and just not share it with anybody. So it's certainly possible. Now, that's a little bit too science-fifference. because the leaders who run these countries are not transhumanist, rationalist, people who read, you know, your blog, my blog posts on the internet.

They are not. They're not dominant coming. So I don't think anything that exciting is going to happen, but maybe it will. Yeah. And do you think the potential for pliotropy is higher with intelligence? I mean, with certain populations, oh, of course, by the way, disclaimer, 5,000 years, not enough, blah, blah, blah, blah. But given that.

Obviously. Obviously. Obviously. But given that, you see with certain populations like Oskina-Jews, you have a higher incidence of, is it nervous system disorders, you know, like TASX and other things. And that seems potentially to be the trade-off of, you know, the higher average intelligence.

You think that maybe the pliotropy has a higher chance of occurring with intelligence? It can only be speculation, okay, at this stage. Now, with the history of the Ashkenazi Jews, they also went through some very narrow population bottlenecks. So there's some special aspects of their genetics. And whether it's related to cognitive function or not, you know, we don't really know for sure. But there are lots of reasons why they have fairly high proportion of inherited diseases and things like that that they're dealing with. This is one of the reasons why Israel is so progressive when it comes to genetic screening and IVF and things like this. One thing people talk a lot about is schizophrenia.

So they say like, oh, schizophrenia could be correlated with creativity. So if your brother's schizophrenic, maybe you're more likely to be creative. And he's super creative, but we don't know what he's talking about. And so people say like, oh, if you start screening against schizophrenia, maybe we won't get creative geniuses. So there's all kinds of pliotropic things that are possibly true. But the thing I keep wanting going, I want to go back to this is that if it's 10 or 20,000 different genetic variants, locations in your genome that are more or less determining

your genetic cognitive potential, I can go around. It's a high dimensional space. If I find out this little cluster, okay, you can make someone smart in this little, using this stuff in this cluster, but it makes them dull, or it makes them autistic, or it makes them, they don't have big muscles, like, okay, I'll just go around. I don't need to use those. I have plenty more. Look over here. Those 500 I don't need to use. I will use these 500. And this is why it's important to look at historical geniuses who were pretty normal. And maybe they were even good athletes. And maybe they even were good with the ladies. These people existed. So you have these existence proofs that I can if I need to, if I'm a really

good genetic engineer and I can operate in this 10,000 dimensional space, whatever obstacle you put for me, I will just drive around it. And I just need some good data. I need lots of data and need lots of AI ML. And I'll do it. And that's the answer, which again, most people don't really get this, but it's true. Right. Yeah. So, I mean, there's a thing where if two traits are correlated at the ends, you know, the person who is like, for example, the smartest will not necessarily be the person who is a strong, I guess these aren't necessarily correlated. But the person who has the highest mathematical ability will not be the person who has the highest verbal ability,

even though the two are correlated. And at some point, it'll be interesting because parents will have to make that trade-off, even if two things are extraordinarily correlated. And it'll be interesting to see how they make that trade-off. Eventually, you're really going to have to trust your friendly neighborhood genetic engineer to advise you.

You know, it's going to be like a lot of modeling going on in the background. Right. Now, I guess for the time being, we're stuck with educational attainment as a correlate. And that concerns me because educational attainment also probably correlates with other things that somebody might want or they might not want, which are conscientiousness and conformity, which is, you know, if you're Brian Kaplan, in the case against education, he says that the three things, education signals are conscientiousness, conformity and intelligence. You want the intelligence. Probably most parents actually do want conscientious and conformity, but some might not, right?

So, yeah, could you, I guess, hopefully we can get the direct intelligence. data itself, but if we don't, is there some way to segment out the conformity part of that educational attainment data? Well, here's the thing. Like, in my dream world, like if I were the CEO of 23 and me or something, what would I do? Oh, warning, they're actually secretly doing this, but you didn't hear that for me. I would have little surveys on the site that's like, oh, can you do a personality survey? And one of the categories will be conscientiousness, and one will be extraversion, right? And one will be like conformity is not a traditional, a big five thing, but you could have questions that kind of measure like how conformist someone is. And of course,

we know how to do a little math so we can we can diagonalize the matrix of correlated measurements of all these different things. So I might be able to remove the chunk within EA, which is due to conformism, remove the chunk, which is due to conscientiousness, and leave the chunk. behind the chunk, which, oh, wow, and that correlates really highly with my separate IQ predictor, G predictor that I built separately using a different method. All these things are very, like at our level, these are understood. The solutions, these problems are understood. It's just a data problem. Yeah. Okay. I'll tell you an interesting thing. So we, my group was the first to do, there are 20,000 sibling pairs in the UK Biobank. So we were the first to say,

you know, this is like three years ago or more. You know, some people don't really understand these polygenic scores and they're very skeptical and they think, oh, we're capturing, we're not really capturing the real stuff, etc. Well, you know what? I will just look to see how well we can predict which of the two brothers who experience the same environment is going to be taller. How well does my predictor do that? I'm going to predict which of these two brothers has diabetes.

Does the diabetes predictor really do that? And you're modding out all the environmental shit because they grew up in the same family, right? So, and we show that the predictive power fall off if you're trying to do this trick with unrelated pairs of people versus brothers who grew up in the same house or sisters is minor. It's a small fall off in predictive power. So, so basically we are getting the true genetic stuff, okay? One of the interesting things is when you look at EA, if you ask, I built an EA predictor, does it work better or worse when I try to predict which of the two brothers got more education? It turns out it works much worse because part of what that predictor is capturing is some

maybe property of the parents who beat them and made them go to school. But both brothers got beaten and had to go. So the reduction in quality of EA prediction for brothers is quite a bit higher than if you're just trying to predict G. So we have predictors we built that just predict G. And those have a much smaller reduction in quality when you apply them to brothers of Sibs than in unrelated pairs. And so I went through that a little fast so people can go look up the paper. But the point is we can see EA is a very different trait than G from these kinds of results. That's super fascinating.

And again, like people who criticize this have no idea how sophisticated the work is. They just don't, they don't read our papers. If they try to read our papers, they can't understand them. But we've done all this stuff. So it's now a guy who comes from a physics background or from an AIML background, if I just start explaining to them, they're like, oh, yeah, okay, cool, you guys are doing that. Yeah, that's how it works out. You can absorb it, but a lot of our critics just can't absorb it.

It's literally a G thing. They can't absorb it. So, but they just want to keep criticizing us forever. So, you know. Yeah. Yeah, the funny thing is when I read your papers, I have a much easier time, like the prose part and the explanation in the organization is, I don't know if it's your physics background or whatever. But I noticed with Scott Arons's papers as well. It's like they're written like essays.

They're so easy to, as long as you understand the underlying ideas, they're so easy to absorb. Whereas if I just read to like a random thing on bio archive, it just, I don't even know where to get started with this. It just written so turgently. I'm totally with you. I mean, of course, there are multiple reasons for this. But one is that, yeah, maybe I'm a outsider. So I'm trying to write it very clearly and conceptually maybe like a theoretical physicist would write it. But also it's like it's a slightly selected population.

Like Scott has an enormously populated popular blog and he writes these huge posts all the time. And I have a blog too. So we are a little bit better at expressing ourselves or clarifying ideas than the average scientist who's just try to get the thing out and get it published in nature. Awesome. Okay, so let's talk a little bit about what consumers will actually want. Goren has this really detailed post about embryo selection. And he writes in it, my belief is that the total uptake will be fairly modest as a fraction of the population. And he's talking about embryo selection here.

A large fraction of the population expresses hostility towards any new fertility-related technology whatsoever, and the people open to the possibility will be deterred by the necessity of advanced family planning, the large financial cost of IVF, and the fact that the IVF process is lengthy and painful. So, yeah, he seems like very pessimistic over the possibility that this is something that millions of people are using. What do you think was your reaction to his take here? There are two perspectives that you could adopt in looking at this. One is a perspective of a venture capitalist where you say, how big is this market? What's it worth to dominate this market?

What valuation should I accept from these pirates at GP? The other perspective is, hey, I'm really worried that humans are all going to engineer themselves to be blonde and 6'4, and we're going to be suddenly susceptible to all kinds of diseases and one single cold virus will kill all of us. So there's like two different perspectives on like what level of penetration this technology will have, right? There are two different perspectives. So from the venture guys perspective, I will just say this, one out of ten babies in Denmark is born this way. Would you like to capture a market that, you know, interfaces with one out of ten families? and that's going to grow, of course, right?

One out of 10 families in all developed countries, maybe including China, you have the genome of mom and dad and the kid. And maybe you can sell them some health services later on. Maybe you can, maybe it's sticky your relationship with these people. Okay, so that's for the venture guys. Okay. You know how to get in touch with me. From the, oh, I'm really worried about human evolution. Or when are we going to get another von Neumann?

That's a different question. And it may be that it'll never be more than 10 or 20% of the population that's using IVF. And then through IVF, embryo selection and maybe potentially editing someday. So in that sense, why worry? There's always going to be this natural reservoir of the wild type, you know, that have much more genetic diversity, et cetera. I think there's a very, maybe this is like the Goldilocks world. But imagine the Goldilocks world where, you know, there's plenty of wild type people

and then there's plenty of people using these advanced technologies and everybody's happy, including our investors. Yeah. Something tells me that that will not be satisfying, you know, to the people who are concerned about. I have the sense that this whole argument about like the, oh, we're not going to have the evolutionary diversity or whatever. That's just a front for just like a moral reservation.

about this technology. Exactly. It's a front for people who just hate it. But what is Gwern saying? Like, is he saying that like, well, you know, these 10% of babies born in Denmark, they're already mostly screened for chromosome abnormalities. And if I take that same data and I can generate this other report, are you really not going to look at that report?

Are you going to say, like, well, you know, one of my, one of these kids is going to be super high risk for, you know, macro, macular degeneration or something, you know, something, but I'm not going to, I'm not going to look at which one, but I'm already screening them for chromosomal abnormalities. Is that really going to happen? I don't think so. I think that 10% of the population that's using IVF is going to look at the report, which can be generated by the, you know, at the cost of running some bits through AWS server, right? So I'm not sure what he means by that. Like, like, I mean, Gory and I admire him a lot, but what does he mean by that? not very many people are going to adopt it.

Does he mean like the percentage adoption within IVF families or the fraction of the population that's already doing IVF? Because those are already big numbers. So I don't know what he means. Yeah, yeah. You know, it's interesting. Like, I guess one way to think about generic prediction, given your earlier statement that, you know, these Scandinavian countries, a lot, there's a huge amounts of IVF happening there.

And part of that is because of how old people are when they're having babies. A venture capitalist can think of your company as a way to get exposure to demographic collapse, right? Yes. That's been, it's been mentioned. By the way, it's like three to five percent in the U.S. So it ain't small. Like if you go to a kindergarten, there are some IVF babies running around in the playground.

So it's not small. So I don't know whether the perspective is, is this a big enough market fee to make money in it? Or is this like going to change the future of the human species? You know, you can have different perspectives. Yeah. By the way, Gwern is such an interesting character. I've been reading for him a long time,

but obviously, like, his person is very mysterious. I don't know if you have, like, something, obviously nothing that isn't already public, but, like, what is going on here? Like, how did this person get into, like, it's a really interesting and detailed report that he published in every selection, and it's super interesting.

What is going on here? Well, Gwern is a super smart guy, and he, you know, I know, I know a lot of, lot of scholars and serious scientists and intellectuals in the academy and outside. And I will pay, even though I didn't quite agree with his take that you just mentioned. I mean, it might not be technically wrong because he used words there that I'm not sure what he means by those words. But, and I'm not sure he would disagree with the quantitative things I just mentioned to you. So, but I just want to say some positive things about Gorn because I like to read his stuff. And so

in the early days, he was following a lot of this stuff about genoid prediction and embryo selection. And, you know, he's written stuff on that. He's written stuff on GPT, 3, and alignment risk. He's written lots and lots of insightful things. And I think he's quite impressive, even if you compare him to, like, the most, you know, famous academic scholars, like, whether it's a Steve Pinker or, you know, somebody who just has written a lot of stuff that people read and has obviously been thinking deeply about a lot of different things during the course of a very serious life, reading and thinking and writing. I think Goren is super awesome.

I think he's right up there with those guys. So I think it's awesome that we live in this internet age that some totally anonymous dude can produce really good thinking about a wide variety of things. And he's not wrong. Most of the stuff he writes about embryo selection is pretty much right. So, yeah, I have a very high opinion of Gorn. And yeah, so it's interesting with people like Gwerin. It's almost in the model you can think of early a 20th century or late 19th century,

these gentlemen scholars who would just pontificate about a lot of different subjects. I wonder if we're going to see a return of this sort of generalist thinker. And maybe we've over-indexed on specialists, but now it's like, now it's the time for, like somebody like you, right, like theoretical physics, bringing all that computational and mathematical knowledge to genomes. Is that the new trend in science, at least at the upper levels? I don't think it's a trend. So in terms of Gwern having a platform, so first of all, he's there, he's thinking, really,

you can tell, he's thinking, he's reading a lot, he's thinking, and then he's writing very insightful stuff. And he has an audience thanks to the internet, right? So people can read it. That is an amazing positive trend, which I think will continue. So I think we're in a kind of, in a way, we're kind of in a golden age for intellectual, you know, exchange. Even this conversation that you and I are having is an example of that. The thing I'm afraid is not going to happen just because science is so specialized now.

And it takes so much, you know, money and resources and institutional support within a university or lab or something to get stuff done. I don't, I think it's getting less and less common. to find polymathic people who are actually able to do things at the frontier where they really make a significant contribution and it's recognized by the natives in that sub-specialty. That's becoming rarer and rarer. It was much less rare in the time of like Feyn and Van Neumann and people like that just because the science was smaller. You know, Feynman played around with some molecular biology.

When molecular biology was becoming a big thing, he was friends with Francis. Crick, who was down in San Diego. And so he would do stuff like that. And now it's almost impossible. And people would tell me, Steve, like serious theoretical physicists would be like, Steve, why are you fucking around with this stuff? You're wasting your talent. You know, they'll literally say stuff like that to me. So I don't think the trends are good for that. But for general intellectual exchange, I think the trend is good. Yeah, that's interesting. Going back to IVF, Do you think the gains will be greater in any given trait you could think about for parents who are already high in that trait or for parents who are lower in that trait compared to the average of the population? I don't think that the base level of mom and dad is a very big factor, actually.

The big factor is how good are your predictors and how many embryos are you looking at? Or how good are your editing tools? By the way, I just want to reinforce something I recently learned. It was so amazing that it freaked me out because I thought, oh, I'm kind of in this field, so in this industry, so I kind of know about it. But we were, our company was having some conversations with a company that does, that handles egg donation. So it's in the IVF space. And the egg donors are typically young women, like 22, 23.

They could even be college-age women who are paid. you know, fair sum of money to go through an IVF cycle and just donate the eggs to some, you know, billionaire family or whoever wants, you know, whoever needs the eggs. And I was told that 60 to 100 eggs per cycle is not unknown. So it's totally shocking because usually it's an older woman who's like in her 30s or 40s who's going through it and they're struggling just to get, you know, some viable embryos. And then so then when you run that same process with a 19 year old, what do you get? And I was kind of shocked at how high these numbers were.

So in principle, let's just imagine you're a billionaire oligarch and but very tech savvy. And you want to have some, you want to have a large family and you want to have really, you know, high quality kids, maybe very long lived, healthy kids. you might be selecting the best out of hundreds. Like, there are 100 parallel universes I could live in. I get to peek into each one and then choose. I'm going to step through door number of 742 because that's the outcome I like. Not that expensive, actually.

But amazing that people can now do this. I guess that'll imply that the returns of being young when you have kids are going to increase. Because IVF is like theoretically supposed to be, oh, you can have kids when you're old as well now, right? So it's evening the playing field. The addition of this with the additional embryos where somebody was young is like, no,

we're tilting it way in favor of young now, at least if you care about those kinds of traits that IVF could, sorry, genetic screening could help you figure out. So let me just ask you what you think about some of the possibilities that Gordon talks about in that post. One is that we might be able to turn induced pluripotent stem cells into embryos, and then we'll be able to select across hundreds of embryos without having to harvest eggs.

Yeah. So eggs are the limiting factor. Sperm is cheap. And the technology, the stem cell technology, to take a skin cell and revert it to the pluripotent state so that it can. become some other kind of cell, not a skin cell, but maybe an egg cell. That technology has been more or less mastered for mice and rats, I believe. At least maybe rat is the most common model system. So there's a few labs like in Japan where they seem to have fully mastered

this and they've done multiple generations of rat using induced pluripotency to make the eggs. And so my guess would be to get it working in humans is not that hard. It's a matter of some years of just slaving away in the lab to get it working. And I know of startups that are actually working on this. And now there's going to be some trepidation initially. Like, why would you do that if you can just pay some 19-year-old to be your egg donor or something? For example, some gay couples really want to do it because maybe they think. think they can also, well, maybe they can, you know, their partners can make it into an egg.

Okay. So there are reasons why you do it. But for a lot of people, I think they would say, like, that's, that, that's a, that's a, that egg was made through a new and untested process. I'd rather have an egg where I don't have that additional risk in this whole thing. So I don't know adoption-wise what's going to happen there. But I do think that it's just a. technological prediction, it will be possible. It won't, it isn't, we're not that far from being

able to do it. I mean, the fact that we can do it in rat means I think we're not too far. And yeah, it could have huge implications for natural selection. If you really wanted to be able to select from best of 1,000 embryos, there's no technical, I mean, eventually there's no technical barrier. Now, I would say that on roughly the same time scale for the pluripotent production of eggs to get mature, to be tested so that people are confident in it. I think on that same time scale, multiplex, very accurate sort of CRISPR-based editing will also arrive. And so at that point, it's like, why are you fooling around this? I'll just go in and did it make the changes I need to make? And over that same time scale, I think it's roughly the time scale. I think it's roughly the

time scale over which we're going to figure out where the real causal those I are. Exactly. I was just about task because otherwise you're just changing the tag. Yeah. So all of this is stuff that you're younger than me. So I'm fully confident you're going to see it all. I may not see all of it, but I'll see it, you know, the technology perfected.

I won't necessarily see this impact on society. But you'll probably see it all. I'm hoping it's ready by the time I'm ready to have kids, which is still a while away. another possibility that Gwern discusses is iterated embryo selection where you just you can keep I'll let you describe how it actually works but what do you think about this possibility yeah so there it's like you make the embryo you make a bunch of embryos and then you decide which ones you like and then before you actually make it into a person so that then that person grows up and reproduces you actually reproduce just using iteration of embryos that's also plausible too so I think um all of these

you know, very molecular technologies have a chance of working. I don't know anybody who's working on that, actually really, like spending all their time working on that. But, yeah, that could work as well. Well, I just do want to say that, you know, like I made these jokes about the wokes and progressives and people like that who hate us. And I actually just feel it's kind of wrongheaded of them. I think actually the goals, like, I actually consider myself a progressive.

I don't consider myself woke. The goals of having healthy people, maybe healthy, beautiful people who live to be 200 years old. Who's against that? You know, like, I'm also against inequality in society. I think, you know, consistent with growth and advancement in science and technology, we should try to have a fairly egalitarian society. I'm for all those things. So I think if you're a wokester who's watching this interview to just like hate Steve Schu or something,

think about it. Think about why you're angry at me. Like, I'm actually exploring how the world actually is. And don't you want to know how the world actually is? If we have an inequality problem, because some people don't do well in school, don't you want to give those families, these resources so they can fix it for the next generation? Isn't that the ultimate goal of what you want? I mean, just think about it.

Yeah, yeah. To steal, I guess to steal, I guess the steel manned them a little bit. Somebody might say, listen, one of the things that prevent. just runaway divergence between families over time in the model of like Pickety or something is just

a reversion to the mean. And I listened to your conversation of Gallaghery Clark where he says this is kind of already the case. But to the extent that it doesn't get like magnified over time, the reason is, yeah, it's hard to like maintain a leech in genetics

because of aversion to the mean. If you can keep that up and if there's like increasing returns to having good genes because you can then afford these kinds of treatments. Then the possibility of society, like, you know, instead of like a normal distribution for society, you can just have a bimodal distribution that keeps getting further and further apart. That is a potential possibility.

The Morlocks and the Eloy. Yeah. I mean, I think that is a fair concern that this could lead to grotesque, huge inequality. And that is a risk of the technology in it. A lot of that depends on society, too. I mean, like, when someone confronts me with that, I will acknowledge it as a legitimate concern. But then I'll say, like, you know, we live in a country, which is the rich, in some sense,

the richest country in the world. And there are plenty of people who don't even have health care? Are you worried about that inequality? Like, you know, like we have a lot of inequality. There's a lot of things for you to worry about when it comes to inequality. And this is some technology, which could contribute to it, but doesn't have to. actually maybe this might not be globally beneficial but at least this particular debate it might be beneficial if the case was uh when i asked you like oh do people who are lower on some trade have a greater potential for increasing that trade than somebody who's higher up on it if that was the case then you could just say like listen the the smart people are just going to ask them taught at some point whereas the dumb people can just catch up over time right well i think again like if you're more of a left guy and you like government intervention and so this becomes part of the government health care system and it's free.

And you say, we will allow more aggressive edits or more embryos to be produced for below average families. There's a very natural way you can redistribute, just like you're going to forcibly take a bunch of money from me when I die that I would rather pass on to my kids. You're going to forcibly take it from me. Well, you can forcibly give more genomic prediction resources to people who need them. It's easy. So in your, just the shift topics are quite a bit here. In your, you had an interesting post on that recent Twitter viral meme about the word cells and shape rotators about how actually the content of a shape rotator is combining two separate abilities, math and spatial ability that are, yeah, when you do like principal component analysis and psychometrics, they turn up to be different but correlated. I am as a programmer I'm really curious about which of those is the one that is required more for that particular skill set because I'm the kind of person you know when we're talking about like abstractions and data structures and the flow of a program I'm the kind of person that intuitively likes to think about it from I just like imagine what it looks like visually whereas I know friends who I said like okay so clearly programming is a viso spatial ability and they said that

that actually they don't imagine it visually at all. That for them, it's much more of just like looking through the loop and like what's going to happen next, what's going to happen next. So yeah, I'm curious, like which of these is a better description of what programming is like? I think your description captured the whole story that people are very different in the way they attack, even though they're attacking the same problem, the way that their brain does it. I think that's one of the most fascinating things about this field of psychometrics and psychology is that, you know, really trying to get into that. One of the things that fascinated me

when I was being educated and going through training as like in theoretical physics and math is like looking at how my, you know, whatever classmates at Caltech or Richard Feynner or somebody approached a problem, which might be totally different than the way I would do it or the way that we would communicate about the solution once we got it. And there clearly are people who are visual, like Feynman was a very visual thinker. Other people are more kind of lot. logical verbal where they're like stepping through things and it might even be like they hear the arguments as they're stepping through it or something. So everybody's different. And I think those things are super fascinating. Something that's kind of gone out of fashion now, but was very in fact, very in very standard when I was growing up is like when I took shop class, I don't know if you had to take shop class in junior high or high school. But we had to take like shop class, which we'd go and bend metal. And literally they have machines that would, I made like an ashtray or something out of steel or something. So yet in that class, which is very spatially loaded, like you could have guys, like I had a friend who was, you know, had a very high SAT score and went to Princeton to study English.

That guy could not spatially rotate at all. He was totally lost in figuring out how to like do the bends to make the ashtray or whatever, right? So you see that very clearly. And in those old days, when things were more based, when you went to shop class, sometimes you just give you a standardized test, which was a standardized test of spatial ability. So we're all, like my generation is like, you don't have to lie to me about all these things. We saw how it works. We saw people take the standardized test for spatial visualization.

And then we saw people try to fucking work the metal bending machine. And some people just couldn't do it. Like they couldn't actually make the thing look the way the product looked the way it was just to look. So in the real economy of atoms and grams of steel, kilograms of steel, which has all moved to China now or something, that all this stuff is super important. Like you can't just like theorize about like, okay, then I have this module that does this and this function is going to have these types. And well, that's nice. That's super valuable in this part of the economy. But somebody's got to get this plant working.

and it's got to be efficient and we've got to put the machines here and here so we don't have to carry the shit too far from here there's a lot of like that's very spatially loaded stuff which used to be part of the American economy and education system and now I think it's all gone

but it's real it's not fake no people are not making this up and psychometricians of the 1950s and 60s would have been like yeah here's here's my 10 volume treaties on spatial you know measuring spatial visualization ability or something so Yeah. Even if you read the biography of somebody like Einstein, I mean, he was especially known for being a spatial thinker.

Oh, he was incredibly visual. Yeah, yeah. Incredibly visual. Right. Just like thought experiments that are just basically what does it look like or what does it feel like to be moving at this speed or whatever? Yeah, that's interesting. Yeah, so I guess in the case of programmers, I'm not sure I got your answer, but for that particular discipline, which do you think is the more pertinent skill? I was going to say people are going to do it different ways. I do think that if you compare the category of engineers to the category of software developers, engineers generally, I think, have higher, on average, higher spatial ability and they're using it. Whereas you can be an awesome programmer with like zero, I think zero spatial ability. That's my guess.

Yeah. I wonder if the, you know, when you're studying history or something, you notice that some people are really attracted to the military history aspect of it and seeing how the units are moving and stuff. And I wonder if that's because they have a higher spatial ability and they just, they just need to be able to understand how the units are moving and so on. I was going to say, this is a very weird thing for me to reveal, but like sometimes when I'm having trouble falling asleep, I'll be visualizing. Like recently, I was thinking about, you know, how I would use a ballistic missile to target like an aircraft carrier, right? But like, like, have the Chinese actually solve this problem or, you know. And sometimes if I'm trying to go to sleep, I'll just like be visualizing like, okay, when you're a,

about an altitude of, you know, five kilometers, what can your radar see and how much resolution do you need and then how much time do you have for course correction to hit the ship? And, you know, like, I'll be thinking about stuff like that for relaxation. But I'm, and it is highly visual and also quantitative because you have to make some estimates. But, but like, I think that would be typical of like a lot of physicists. Because if we start talking about it, we'd be like, Oh, yeah, right. And you've only got about point of order a tenth of a second to do this. But you're thinking do it in milliseconds.

So we're okay. And then anyway, that kind of thinking is very prevalent among certain types of people. Right, right. Now, I'm curious why it's the case that people from physics so often transition to finance. I think that was something you were considering at one point. Is the underlying knowledge in mathematics is just the same? Or is it just such a credible signal of mathematics? mathematical ability and G that, you know, quant firms and whatever, they want to hire physics students?

The answer is a little bit complicated. I think all the factors you mentioned are true. But one of the things was that in the early phase, like in the 80s and 90s, when a lot of people in my generation went into finance, a lot of them went to do to trade derivatives. And if you look at options, pricing theory, it looks a lot like physics. It's kind of like the mathematics of random walks, basically. And so there was a very tight, not tight connection, but the concepts were strongly related that were necessary. Now, if you brought it out a little bit more to say like, okay, but nowadays, if you go to really big quant funds and they're looking for signal and analyzing tons of data and they're not trading derivatives, they're trading, you know, just actual names

like stocks or whatever, a lot, I think that's a lot. I think that's a lot, there's more loading on machine learning and CS background now. And the physicists who go in, they're having to, they're using that subset of their skills. But the funds would just as soon hire a CS or ML type guy to do it. So it's a little bit of a complicated answer. Yeah, that's super interesting. Because I mean, back in the 90s and early 2000s, I don't know, I was watching, I read that book about the fall of long-term capital management. And obviously, I guess this is a cautionary tale. But still, it's kind of cool to... Actually, there are two books.

There's one called When Genius Failed. And then there's actually three books, at least three books, but they're all good. Yeah, yeah. Yeah, and then you just hear about, like, obviously the people who create a pop option pricing theory are there, but the applying, you know, calculus to random walks and stuff. The stuff I don't understand, but just super cool that you have these mathematicians that are just coming in and applying these ideas to finance. I do want to say one thing about physicists, which is a little different from mathematicians

and computer science guys, maybe not so different from data science guys, but definitely different from most computer science guys and most math guys, is that we spend a lot of time looking at bad, noisy data. So even if you're a theorist, you had to go through these lab courses where, I mean, those, for me, those lab courses were among the hardest, like the worst, because you had to go in and build some electronic equipment to take some data, and, you know, it could be extremely noisy, like you're measuring muon cosmic rays coming through the roof and hitting your detector. And then you have to analyze the data. And when you're building this thing, you screw it up.

And so, like, you get data that makes no sense or something about the amplifier wasn't right. Or there's, you're used to seeing data that sucks. And you have this theoretical view of what should be happening. Like maybe you're visualizing it, like the muon comes in and it does this. and interpolating between the theoretical view of what should be happening with the particles and the systems and what the actual data looks like and saying like, oh, shit, we didn't do this, or we didn't shield this part, so that's why we're getting that. That's something physicists are very, very used to doing, and mathematicians are often shitty at it. They just accept, oh, I just accept, this is the data,

now I'll reason with this data. And the same could be true for computer science people. You need someone who's actually had to deal with shitty data and tried to connect it to a very elegant mathematical model. That's something physicists kind of uniquely are used to. No, but I think that's also true of CSPB, which is that you have, obviously, in debugging, there's many potential problems that could happen. One of them, obviously, is just you wrote the code wrong, but often you get the actual implementation just right. It just, there's so many layers of abstraction beneath you and above the actual hardware that you have to figure out, like, why is the correspondence between this idea I had and the actual program output, not the same. I think that's fair because, yeah, debug, when you debug your code, there are many different ways it could have failed. And you have to actually, in a sense, step back and model, like, oh, maybe this module is feeding me something back wrong and that's what's causing the problem where it's this other layer.

So that is very analogous to like when we have to deal with a physical experiment in the lab. The thing with physics, though, is that we're really, really geared toward getting toward the underlying reality. Like, it's really late at night and my buddy, my lab partner and I, we just want to get out and go to sleep. We can't like tell ourselves that things are okay. We didn't actually screw up the shielding on that. It's okay. We'll just bring the data home and look at it. No, we got to actually decide.

Do we have to spend three more hours ripping this thing apart and reshielding it? Or we have to get to the real underlying reality. We can't fake it. We can't just pretend like, oh, this admission scheme will work perfectly. You know, like, you know, we can't lie to ourselves about it. And I guess that's true for coders too. But anyway, it's very different from like social scientists and stuff where they can just decide, I don't like that reality.

I'll just make up this model for how society behaves. that I'm done. We can't do that. Yeah. So given the skills that the article physicists have, as you just mentioned, is it potentially the case? I mean, obviously the common criticism of, like, physics as a community is that they're absorbing too much

talent, you know, like three or four standard deviations above average intelligence people are working on a field that I guess in popular convention at least seems like isn't making as much progress. And then, so should more people in physics be making the

step that you made, which is just like, yeah, I learned all these skills in theoretical physics. I want to move out of it. Maybe finance is one way in which, like, we're getting these pro-social benefits from the skills that physics builds, but also, yeah, just like stepping into fields like genomics or things like that. Should more physicists be just using their skills elsewhere? Yeah, so number one, the attrition rate is super high. So even if you cut, you say, like, take this set of kids that are plus three or four samples. and deviations in ability, and they enter, they enter a physics major at Princeton or MIT or something, what fraction of them actually end up as practicing physicists? It's pretty small, so they're bleeding

off at all points. Like, you know, Bezos started in physics and toward the end of his Princeton career switched to computer science, and, you know, Elon was in graduate school in physics at applied physics or physics at Stanford, and he bled out. So it's already the case that for me, one way to say it is the education is phenomenal. You should try to get that education. It'll pay off for you later and probably you're going to bleed out. You're going to trit away and do something else. Now, if you say like, okay, of the thousands of theoretical physicists or physicists who do fundamental research, including the experimentalists around the world, there are tens of thousands and maybe some of those guys should also be doing some more cancer research or doing financial modeling. Yeah,

maybe so. Maybe so. I mean, maybe even some of those guys should, you know, we should should tear off, you should remove even more of those guys and have them do more apply stuff. There's still some argument in favor of that. But we do need a core of people that are trying to do these really hard fundamental, answer these hard fundamental questions about nature. By the way, the basis of the example is really interesting. And obviously, by the way, for the people in the audience who might not know,

is he was asked once why, I mean, his original plan, I think, was to become a theoretical physicist. And the reason he didn't pursue it is that he noticed one of his friends was just so much obviously more gifted than him at that skill that the story he tells is like they were had been uh, Bezos was working on a problem for many hours and making no progress. And one of his friends just looks at it in an instant. He's like, oh, the answer is, I don't know what it was, but like, blah, blah, blah, blah, cosign of something. And then he's just like, yeah, I just eliminated all the terms. I recognize a similar problem. And so Bezos's like, okay, this is not my competitive

advantage. Can I just say one, I got to add one anecdote. The guy, that guy, so I know a lot of the guys that were in Bezos's eating club and were also, because we're very similar in vintage and who took all these classes with him. And a lot of them were late. One was a friend of mine from high school, but another were guys, a whole other set were guys that I went to grad school at Berkeley because there's a whole Princeton contingent that would go to Berkeley for grad school that I knew that were Bezos's classmates. So I know all these guys. I know all these Bezos stories. The funny thing is the guy you're talking about whose name, I believe, is Yassanta. is an Indian guy, Sri Lankan guy.

He went to grad school at Caltech. And so actually, he and I, I don't remember how, I looked him up at one point. I think we met up at Caltech when I was visiting at one point, and he was in grad school. And so I actually met this guy and talked to him about Bezos. Because we had friends and other friends. We weren't focused on Bezos as we had other friends in common,

but I actually met this guy that is in that anecdote that you just mentioned. Oh, no, because actually that's really good to know, because that is relevant to my question. My friend and I have this continual debate about the importance of intelligence at the peaks of entrepreneurial ability or engineering ability. And he tries to use that anecdote to say that, oh, clearly, Bezos was not smart enough to be a theoretical physicist. So therefore, intelligence is not that important beyond like a certain, not especially high point. And afterwards, as such Bezos was creative or blah, blah, blah, he was hardworking. Um, and I don't know, my perception of the story was like, okay, he's not smart

to be a theoretical physicist. He's like below five standard deviations or four standard deviations above the, but like, clearly, uh, just studying Princeton at, uh, sorry, physics at Princeton is itself a testament that he's probably at least like two or three standard deviations about, well, at least three, I mean, um, but, okay, so can you tell me more about like, what was the perception of those people you talk to at Winston about Jeff Bezos? What, like, is it that he just like, he just was super high in other traits like, hard working or creative or it's actually intelligence was super high, just not high enough to be

a theoretical physicist? Yeah, this is a great topic that, you know, I think a lot of people are just in this topic and even among my close friends, including these friends who know Bezos or new Bezos in school. We all talk about this kind of stuff. So first of all, you got to make a distinction between the very abstract kind of intelligence, which is useful in physics and math or maybe computer science versus a more kind of generalist intelligence. And those are correlated, but they're not the same thing. And so, you know, I would say Bezos is probably very off-scale for ability to work hard, take risk, function under pressure, be focused, and generalist intelligence. So he's just probably

off-scale on, you know, if you're just, since these traits are at least somewhat uncorrelated, if you're top 10% in each of these five simultaneously are already pretty rare, individual, right? Because plenty of the physics guys who did better than Bezos in the physics classes, they could not lead a company. They could not put together a presentation that would convince a venture capitalist to invest. So it's sort of, you know, different skill sets that we're talking about. I think the idea that there's a unidimensional measure of cognitive ability is just not that useful. You know, I'm probably guilty. People will say, wait, Steve Schu just said that, but he's the guy most responsible for promulgating this perspective. But it's only because it's a simplest thing

to talk about is if you compress it to one general factor, it's just easier to talk about. It doesn't mean that the other components are not meaningful. We just got done talking about verbal versus spatial versus some more generalized mathematical talent. So obviously it's a much, it's a high dimensional, not that high dimensional, but it's at least a multi-dimensional space of abilities that we're talking about. Now, the point about Bezos, I think, which is non-trivial, which I think is directly relevant to like the life experiences of like physicists who leave physics and do other stuff, is that very often in an engineering setting or a startup setting, people will be like, you don't know shit about that. What are you talking about? Right. But the reality is people who do perform, I'm on a technical problem.

Okay, not about what's the right way to get a good, a warm intro to this VC. not something like that, but some technical problem that the startup has to solve. Like in Bezos's case, it was often like optimization of some supply chain thing or optimization of some sorting process or reducing the error rate and some like, you know, address labeling. You know, it was a very well-defined thing once you operations problem. And the people in the company uniformly say, like when Bezos comes in the room, he will give us, he will give us very good feedback on the solution to this ops problem. that, you know, it could be out of the blue better than what we said,

or at least he finds the problems with what we said, or if we did a good job on it, he gets it right away, which is some executives might not get it right away. So my point is that people who have these super high, just raw G-abilities, they generally can be useful in these technological environments, even if they don't have a lot of background. Like, they can still come in and be helpful, and sometimes they can solve problems that the people who are well-trained in that area

are having trouble with. I think that is fair. But it's not fair to say there's just some unimensional measure of intelligence, and this guy always beats this guy. This guy always beats. It doesn't work like that. But it's just that some of these off-scale guys are just generally more useful than the critics would like to give them credit for.

Your life story is kind of an example of that. But, you know, I had another experience of this, which was I recently knew Sam Bickin-Fried, who is the CEO of FTX on my podcast. And one interesting, like, for that interview, and I guess generally for all interviews, I try to come up with questions that I think that the guest has probably not heard before. And in that one, in that case, I tried really hard to come up with questions that he might not have heard before. And that might have been like really interesting and challenging. You know, I listen to all the interviews ever done. And then, yeah, prefer a long time.

And if you listen to that interview, the thing you'll notice is the way he answers, it like sounds. like he was just, oh, I was just talking to somebody about that. Let me just say again what I was just thinking. It's like, no matter how creative a question I could try to throw at him, it's just his ability to grok, like all the context explained in the most, in the way that an audience would understand. It was kind of exceptional. Being a super successful founder selects for the ability to figure out, okay, this guy's, this investor, is from private equity. This is how he's going to think about the problem, and this is how I should explain it to him. This guy's from a very tech-heavy venture fund. This is how I got to talk to him

about the problem. This is the due diligence guy that they sent me, and he's a computer science professor at Stanford. I got to talk to him in this language. So founders are very selected population for being very good multi-band communicators across different cultures and stuff like this. This is a dumb investment banker from Goldman. I mean, they're not dumb, but he's not technical at all, and so I got to explain it to him this way. But this guy's a lawyer. I got to talk to him that way. So it's not surprising to me that, you know, this guy would have those capabilities. It's not, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, right.

those disciplines is that you can really punch above your weight, right? And that, you know, Royce Gracie in the UFC is a great example of this. Is that possible with a trait like intelligence? Is it possible that we have techniques or other ways of compensating for your, I guess, analogously, your just raw weight that, what jiu-jitsu is to fighting? That's a great question. So in a way, jiu-jitsu is like applied physics because you're thinking about like, I have two arms, you have two arms. You know, is it easier for you to punch me and knock me out before I can close a distance and force you to grapple with me?

It really is, the reason I like jiu-jitsu so much is because it's very rational. It's basically scientific analysis of what two humans can do to each other. And so it's a technology. And in terms of what technologies people can use, like to amplify their brain power, obviously we're surrounded by it. So like, here's an interesting thing. Suppose you and your girlfriend are trying to like get the answer to some question and you're both using Google.

There's an enormous variance in who immediately puts the search term in that gets the right, like the top hit is the direct answer to your question. And that's very G-loaded. But you could use technologies to improve yourself, you know, if you train, if training is not the right word, but if you, if you kind of get good at using certain technologies or certain information channels, you can't amplify your ability beyond just what the raw, you know, capability. Yeah. So I think my answer is that there are tools, but there's nobody who like, there's no dojo where you can go and Henzo Gracie just. like starts teaching you immediately like this, do this, this, this, and this, and then you're going to, the guy is bigger than you, but you're going to take him down and choke him out.

There isn't something like that for cognition. Right. But I can see, like, people can amplify their capabilities in different, either more or less effectively. Now, you had a blog post a long time ago about elite education, and in it, you, you talk about how even if you control for SAT, at the very top jobs, the people from elite schools are overrepresented. And so I'm curious, do you think this is, this is because of a selection effect based on, like Harvard selecting based on personality as well and that, you know, that selects for

certain high achievers? Or is it something about being at Harvard that makes you a high achiever? What is going on? So, first of all, I, I researched this question pretty aggressively when I was first, when I first became an entrepreneur, because I was like, well, we can raise this much money. We can get these meetings with these funds. But how the hell did this guy get raise $100 million for this stupid idea? Like what the hell? And then so I would start looking into this guy's background. I'd be like, well, he went to Harvard and, oh, he was in skull and bone, you know, whatever.

So I got intensely interested in like, okay, these super outlier guys, like, how did this guy get a job writing for the Simpsons? You know, like what I would like to write, you know, this other guy would like to write for the Simpsons, but he went to Ohio State. So he's like 10 layers of social networking away from the Simpsons. But the Harvard guy's not, actually. His buddy's at the Crimson. I'll write for the, you know, the Simpsons, right? So there are multiple factors why take two kids. They both scored 1580 on the SAT.

One goes to Ohio State on the Ohio Regents Scholarship for Engineering. And the other one says, no, fuck, no, I'm going to go to Harvard, even though the engineering school. there sucks, but I'm going to go to Harvard instead. So what's a difference in their lives? One, somehow, maybe the guy went to Harvard because he kind of understands how the world works a little better than the other dude. Okay? Two, when he gets to Harvard, he's going to meet a lot of super ambitious, aggressive, smart kids. Some of those kids are children of super wealthy people. Some of them are children of super influential people. And all of them are trying to get ahead. they're super ambitious. They know what it means to like make managing director at Goldman or become a partner in McKinsey. They know what those things are. Okay. And if you didn't know them because you grew up in Ohio, you learn them right away because you see what Joe, who was two years ahead of me but had the room across the hall. He interviewed. Now he's at McKinsey. Now he's doing this. You just get a better view of what's possible in the elite sector of society from that exposure. So there are multiple factors. Networking. Some of these Harvard kids come from super wealthy families.

Some of them, their dad used to play golf with the head of the, you know, of, you know, the fund that he's trying to get a meeting with, right? So it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, it's, I'm not saying it's good, but, uh, I kind of want to understand how the world works. I kind of understand how this other dude can raise so much more money than I can raise or get meetings that I can't get, right? So that that's how I was initially interested in this question. Um, why are China and India, um, why are China and India, underrepresented in, like massively underrepresented in Nobel Prizes per capita. And, you know, even in computer science, when I would like try to find papers on certain subjects, often those papers, like very, it was rare that they would come from China or something like that. And when they did, it was just the quality was much worse than the ones that I could find from, like a professor in the U.S. And I'm curious why you think that is. So obviously, it's clear that it can't just.

speed the population or anything like that because when those researchers come to the U.S., they're producing stellar research. What is happening here? Why is, is this effect real? And if so, what is the explanation? Well, the easy answer to that question is many of the things, or almost all the things you mentioned, are lagging indicators. So they reflect the fact that the West was developed and had a strong scientific and engineering tradition when China and India were desperately poor and just didn't have any of that. And in my own life, I went in the last 20 years from when I would visit a university in China or even like South Korea and Taiwan, I could see them go from, they had plenty of talented

undergraduates, but the best of those undergraduates always want to come to the U.S. for their Ph.D. They went from that to now some of the best undergraduates stay there. And the researchers who are professors there are because, coming world class. But that happened only in my adult lifetime. So you can see it's a heavily lagging indicator. Interestingly, like in my physics career, I knew several, I think the Indian term is called Toppers. I don't know if you know this term, topers. So the people who take the IIT exams, they literally rank every kid in the country who takes the exam, right? So I knew guys who are number one or number two or number five on the IT entrance exam.

But they ended up going to Caltech or they ended up going to MIT. So there's this huge brain drain. I mean, it's super powerful elite brain drain. And MIT recently has just been recruiting. If you win one of these Olympiad, you get a gold medal in the Informatics Olympiad or the Mathematopi, MIT will try to get you to come to MIT. So there's this huge sucking of talent into the United States, which is great, I think. But that's why when you go to IIT, even though the undergraduates are super smart, the professors are actually, no offense to my colleagues who teach there.

But the professors there would generally, if they get a bid from UCLA, they'll move to UCLA on average. So that's the difference. But that's gradually evening out. Are there any downsides to the fact that we can have, we can pay researchers or postdocs in the U.S. less because we're partially paying foreign. workers in visas. Is that just a market arbitrage that has, you know, that's just like positive externalities for the economy? Or is there some downside to the fact that it's not competitive for native born workers?

Good for the U.S. overall, on average. Bad for developing countries because you're stealing their talent. It's bad for native born Americans who have to compete against the best breaks from all over the world. So much harder for an American kid to, you know, get the job he deserves, you know, at these elite levels where he's strongly impacted by immigration. So, you know, you got winners and losers. Whether there's a long-term problem for America. So now, like, there's some guys who are super obsessed who like comment on my blog every now and then who study, like, where are all the IMO, you know, international math Olympiad winners going.

where are they? What, you know, and they claim they're seeing this huge drop-off in, like, kids who grew up in America who are not like first, like children of immigrants, but they've been here a while. They just never win these competitions anymore. So, so ultimately you might be kind of discouraging the native talent pool by just letting the door, opening the door and bringing in, like, all these super talented people from outside.

So there could be some second order effects. that aren't so good. Although it's interesting when you look at an industry like tech, where there's a similar aspect of foreign competition being allowed in because of BATRAs, but the compensation has remained really competitive. Is it just because tech is a super, it's like super inelastic demand for the talent?

Yeah, because you're maybe a little more focused on things like software development and ML and stuff. But if you look at like more kind of traditional engineering, field, which aren't as hot, probably those guys, like, you know, like an engineer at Boeing or those guys would probably say, like, no, my fucking salary is heavily suppressed by the existence of hungry engineers from India and China and stuff like that. So, you know, software, because it's been so hot for so long, doesn't feel this effect so much. It's got plenty of elasticity. Awesome. Okay. Steve, this is so much fun. I really, really enjoyed this conversation. And in

preparing for it and in talking to you, I really got to learn a lot more about this subject that I was interested in for a long amount of time. Is there anything else that we should touch upon on any of the subjects we covered today or have failed to cover today? Wow, we covered so much, and I just, I really think you're a great interviewer because your questions are like always getting at a key thing that I think a lot of people are confused about and there's a lot of depth there. So I thought it was great. There's plenty more that we could talk about. We should just get together and do this some other time. But I don't think you left anything out.

If you're willing, I would love to do a version two of this, where we talk some about your physics work. And, yeah, the other subjects we might have missed this time around. Yeah, we got to talk about many worlds and quantum computers. This will be fun. In the meantime, do you want to give people your website, your podcast and your Twitter so they know where to find you? Yeah.

So my, well, my last name is 18. HSU, that's like the hardest thing for people because it's kind of anti-phonetic. H, then S, then you. And just search for me. I'm on Twitter. I have a blog and I have a podcast called Manifold, which doesn't have a huge listenership, but I try to keep the quality level really high where I try to get really best in class kind of guess and then we're willing to go into some depth.

So it's got a very kind of niche audience. But if you like the conversation that we just had here, you'll probably like Manifold. So you can look for that in all the usual places you get your podcast and also on YouTube. The podcast is like it's similar. It's exactly what I'm trying to do here in the sense that it's like really in-depth conversations. But you just know so much about so many different fields. So it's so fun to listen to where you're having expert level conversations and everything from social science to foreign policy to, yeah, obviously your fields. but that is a podcast I really recommend.

So yeah, Manifle podcast is one you should check out. Yeah, my pleasure. Thanks for watching. I hope you enjoyed that episode. If you did and you want to support the podcast, the most helpful thing you can do is share it on social media and with your friends. Other than that, please like and subscribe on YouTube and leave good reviews on podcast platforms.

Cheers. I'll see you next time.