Adventures in Physics, Trump, and more, with the Information Theory podcast — #75
01:19:03 Share

Welcome to the Information Theory Podcast. This is my episode with Stephen Hsu. I would say this about the NIH. It's by far the biggest chunk of our basic science budget. It's way, way bigger than the other major funding agencies. And this will get me into huge trouble, but I'm not a VPR anymore, so I can just say it. The NIH produces a lot of low-quality research.

You're breaking news here because I don't think I've ever said this publicly. So in 2016, when Peter Thiel was playing a big role in the transition because he had supported Trump during that election, I almost joined the Trump administration in 2016. I was at a very senior level that would have required Senate confirmation. Steve is a theoretical physicist, a technology entrepreneur, and a prolific blogger and podcaster.

His main research work has been in quantum field theory, but he's also done pioneering research in computational genomics and co-founded multiple startups. I first heard of him through his blog, Information Processing, which he started writing all the way back in October of 2004. So first topic, Steve, I want to start with the education of a theoretical physicist.

There's a photograph of you at your Caltech graduation. You're only 19 and you're standing next to Richard Feynman. So there's something almost surreal about that image today because, you know, we think of him as a legendary figure. So my first question is, how did you end up at Caltech at such a young age?

Well, Feynman definitely was a legendary figure. It's almost, it's a little mind-blowing for me to actually think that, yeah, I actually interacted with this guy over several years. So I was always very precocious as a kid. So I was always highly accelerated. That was unusual back in my day. So you grew up, you grew up in the Midwest, right? But it was a university town. Yeah.

Yeah, I grew up in Ames, Iowa, which is a college town. The local university there is a it's actually Iowa State University of Science and Technology. So it's it's pretty engineering science kind of focus has a pretty solid physics department. My dad was a professor.

So I benefited from pretty strong, talented, gifted programs growing up, but there was resistance in the school system to letting you accelerate. And so I was literally the first kid at my high school who was allowed to take courses at the university during high school.

And by the time my senior year rolled around, I was spending half the day at the university. And so I took a lot of, I guess even today, it sounds pretty unusual. I took quantum mechanics while still in high school. I took courses in ordinary and partial differential equations, linear algebra. I just taught myself. I took complex analysis, which is actually a kind of an advanced class. I think a lot of kids don't take it anymore these days. So that's, that's,

things like Cauchy's theorem and analytic functions. I took, I think, a senior level advanced analysis class at Iowa State. So it was pretty unusual. I think I wasn't that far from the core requirements for the applied math degree at Iowa State by the time I finished high school. You actually finished high school like a few years ahead of time, right? Yeah. So I was 16 when I graduated from

Yeah. So I was young. I'd skipped some grade. Then, you know, my older brother was very tolerant of me being in the same grade as him. We and we kind of hung out. We had all the same friends, you know, despite all this crazy stuff that I'm telling you.

I had a very all-American childhood because, you know, I was co-captain of the swim team and, you know, pretty good athlete given that I was younger than all my competitors. And just had an all-American childhood in addition to, you know, studying all these advanced topics. I would actually characterize, I don't know if kids these days anymore, they watch these crazy shows.

movies about high school. There's sort of a lot of big whole eighties genre of high school movies like risky business and fast times at Ridgemont high and stuff like that. And we, we actually live that. So we actually did all kinds of crazy stuff in high school, drinking, racing our cars, um, you know, amazing pranks that we pulled off in high school. So anyway, I had a, I had a great childhood. I can't, I couldn't possibly, um, complain about my childhood. Um,

And when it came time to go to college, it was a much more naive time than today. So most of the smartest kids from Iowa would just stay in Iowa and go to Iowa State or University of Iowa. There'd be a few odd kids, especially like kids of professors, Jewish kids that I grew up with. They were more ambitious. They somehow knew they were supposed to apply to like Ivy League schools or Stanford or things like this. Yeah.

But, you know, I had a close friend who got into Cornell but decided to stay at Iowa State, for example. I think there was another guy who got into Chicago and decided to stay at Iowa State. So that was much more typical of the times. Because I knew about Feynman, you know, Feynman was not, even among scientists, as much of a cult figure at the time as he is now. But because I knew about him through his Feynman lectures, they had been recommended to me by a professor at Iowa State because he kind of realized, like, the

The level they were teaching the classes at at Iowa State was pretty elementary for me. And he said, oh, why don't you get these Feynman lectures and study them? And if you have any questions, we can talk about it. So that was really... And you mean the physical books, right? Because back then you couldn't just go on YouTube or whatever and watch them. Yes. I have the paperback versions of the... They were red and white, three volumes set, which I think I ordered through the Iowa State University bookstore for like $30. Yeah.

And $10 a volume. So I studied those when I was in high school.

And so I knew about Fineman and I knew something about Caltech. But, you know, other friends of mine would say, oh, you should go to Harvard or Princeton or something like or MIT or something like this. But somehow I was really fascinated by Caltech and also the fact that it was so small. My graduating class was only 186 kids at the size of my high school class, actually. And so I thought, wow, this is a very special environment. Maybe I'll get to meet Fineman.

At that time, the two top theoretical physicists, Gelman and Feynman, were both there. And anyway, so it was a strange confluence of events by which I ended up there. So in retrospect, for your intellectual development, like, was all this acceleration a good decision? Like, you know, if you were to give advice to a young kid nowadays who has aspirations of becoming a physicist, would you tell them just try to get through as much as you can in high school, try to finish it early, and then go to college? Yeah.

Every kid is different. So I'm unusual in that I am very polymathic and also an autodidact. So I can like jump into a new area and zero in on what the essential issues are. This actually shocks people that I, friends of mine or colleagues that are like in economics or biology or genomics or even AI, they're often shocked because I'll come into their office and just start asking them some questions and they'll realize like, wait a minute, this guy's like

penetrated pretty far into what is known in the subject. It's not true of all kids, not even all smart kids. So there are plenty of kids who are really, really smart, maybe like math Olympiad smart kids or whatever, but they're not necessarily going to be able to go into a particular subject and create a coherent logical overview of what's important or not important in the subject. And

and self-guide their learning. I had plenty of friends throughout the years who were, you know, at that level of IMO gold medalist or Putnam fellow, but they needed to sit in a classroom and have a really good professor lead them through the material, even though they could pick it up really fast once they were taught it.

they could pick it up really fast but they didn't necessarily figure out what were the interesting things to try to understand you think that's just like a personality difference or what accounts for that i think it's a cognitive difference i feel like i'm just logically strong like if i start learning the subject i can fit it in it into a coherent structure and then if i see a hole in the structure or especially a foundational hole i i love to just zero in on it and say well how do you know like all this is built on this but how confident are you that this is correct and

And so that capability is just, I think, just a specific idiosyncrasy of mine. So it's kind of like the desire to drill down and like question assumptions or like think from first principles. Do you think that's an accurate way of summarizing it? Yeah, I think actually Feynman was this way too. So the idea is like, you should be able to rebuild it all yourself. So I don't like it that, you know,

Why are things this way? And then someone just says, oh, well, it is. Then go look it up in this book or something like that. That's not satisfying to me. To me, I should be able to sit down with a piece of paper or whiteboard and actually kind of work it through from first principles. And if I can't, then that's like a signal like, wait, I got to understand this point better.

And isn't that extremely time consuming? Like, don't you find yourself getting into rabbit holes that just consume days of your life if you start digging down this way? Yes and no. So, first of all, you need to have a lot of horsepower to operate this way. And this is something people said about Feynman too, is like, if he didn't have so much horsepower, they would have disregarded him as kind of a crank and he would have gotten caught in rabbit holes and not reemerged.

So you need you do need a lot of horsepower to be able to figure stuff out fast. And but some of it is you have to know when to coarse grain over something and when not to. So if there's some body of intuition that this group of people is just telling me that's true, I can provisionally accept it. I can say, OK, let me provisionally accept that the stylized fact is true. So let me just accept that provisionally. But I have a mental note that that's actually a conjecture. But then I can move forward.

And so, of course, I know some guys who are very rigorous math guys, they will get caught up because like even physics, even the well-accepted body of physics, they're placed as where if you want to make it fully mathematical rigorous to the standards of professional mathematicians, you can't actually get past that.

And so you need to have this dichotomy where, yes, I do want a first principles logical map. I want to build that myself. But I can accept that certain nodes in that graph are actually provisional.

And that's very powerful because now if you're studying some field like genomics where there's some conjecture, this is just junk DNA or this doesn't matter or this does matter, you can accept it and then proceed. But you have in the back of your mind an alternate map that might actually be the real map. We might have to default to this other one. If I get some empirical data that contradicts one of these provisional hypotheses, I might default over to this other map. And real science, not math, because math is not actually the same as science. To be good at real science, you have to be able to...

have those provisional assumptions built in.

and work with them, but then change them when you need to change them. Concretely, in the 2010s, you transitioned or forayed into computational genomics, right? And that was not your training at all. I'm wondering, how did you even proceed? Like, did you read a bunch of textbooks? Did you go through a lot of papers? There's multiple bodies of knowledge that you need to be conversant in before you even start, right? Like you need to learn population genetics, you need to learn molecular biology. Did you actually have to read all that prior work or? So,

I had some prior training because when I entered Caltech, this is in the 80s, people were already saying that physics is over. Like all the important stuff has been figured out. But the really exciting place where the first derivative, the pace of progress is really fast is molecular biology, which was true because there are all kinds of things that just weren't understood at all, like even the nature of DNA or something, and then fairly rapidly evolved.

They made a lot of progress on those specific molecular mechanisms and stuff. So I had taken some classes in molecular biology at Caltech. So I had some idea of what they were doing. But when I took those classes, I realized like they are so far because at that time they had not come even close to sequencing the genome of an organism, let alone a human. I just realized like the theoretical questions that I have in mind, because I learned about natural selection and evolution when I was still in high school.

So already, like in high school, what I learned about, you know, like when you take biology and they teach you about natural selection, if you're more mathematically inclined, you're like, wait, is there a more because the biology teacher is not going to teach you a mathematical version of it, which which today you would call like population genetics or something. Right. Which is descriptive of like allele frequencies. How do allele frequencies change given some relationship between the different allele variants and fitness? And so how do you describe all that?

If you're trained in physics type stuff, you might think of some kind of either differential or difference equation that maps the distribution of alleles one generation to the next in the population. So I already had that in mind that this was like an interesting theoretical problem in a natural system. But then when I encountered real molecular biology in college at Caltech, I realized, wait, these guys are so far from being able to connect

the stuff they're doing in the lab to the actual, this kind of theoretical construct of population genetics, which, which is actually true. So, so it's kind of tragic, like the early people in population genetics, like Fisher, he was,

Very few instances where he could directly connect the mathematics that he was working on to actual observations or empirical studies, let alone actual DNA sequences. So I kind of understood all that. And I was in a state of waiting because I knew like in my mind, I knew this is a field which could become tractable during my lifetime. And it all depends on technological innovation, like how inexpensively are we going to be able to

sequence DNA and stuff like this. So I was kind of waiting. And then in the 2010 era, that's when I started to see articles with graphs showing the super exponential decline of sequencing costs over time. And so I just realized, oh, if I extrapolate this easily within my lifetime, these questions that I've just been describing to you will be approachable.

And so I thought, okay, now I got to, let me drill down now and start thinking about this stuff. And let me also say that like, this is a case where science fiction actually influenced my taste in problems because I really liked the novel Dune. And if you remember in Dune that Betty Jesseret have been working for 10,000 years to breed a super mentat or quits that's hot Iraq, you know, the guy who Paul Atreides ends up being.

And because they had fought this war with the machines that outlawed computers, they outlawed thinking machines. The Bene Gesserit had to use some very old piney kind of like sort of cattle breeding kind of methodology to figure out like who would actually, who was likely to become the Kwisatz Haderach. So like this whole problem had been lodging in my brain, thanks to Frank Herbert for a long time. Like, well, oh, so if you want to make a superhuman machine,

How exactly do you do it? And what's the science? Is there a real science behind this eventually or not? And so I was I was very sensitized to like, that's an interesting thing that if we had better technology, we might eventually make progress on that problem. And so around 2010, that's when I said, Oh, let me just roll up my sleeves and start thinking about this a little bit.

At first, I sort of had this dual strategy where I was reading the literature. I was just searching on the internet trying to find what is the state of the art, what is known about these things. And at the same time, the way a physicist or mathematician work, you just have a blank piece of paper and you say, well, what are we likely to get? We're likely to be in a situation where we have, say, a million people

we have the genome read out for each of those million people and then we have some vector describing all their different phenotypes let's just suppose i have that information and there is some map between the state of each allele and the phenotype what is the best algorithm for backing that model out you're trying to you're trying to back out a model

From a data set like I just described, because I thought, okay, we'll get to this point where we have that data set. What is the algorithm that can, in an automated fashion, learn the right predictive model? And so then I started thinking specifically about the math of like, how do you do that? Now, it turns out there had been a lot of work on that. So there's a whole field called compressed sensing, which Terry Tower didn't actually do.

And so anyway, it turned out I was able to make contact with a whole body of mathematics that already existed. And coincidentally, Terrence Tao came to the University of Oregon right around that time in the early 2010s to give a lecture. And the lecture was actually on compressed sensing. So I was primed until I realized, wait a minute, this can be applied in genomics. And so that's sort of how it went.

So was it purely a mathematical result or, I mean, it doesn't seem like something that could be figured out from first principles, right? Like you must have relied on some experimental results as well to know that the genomic architecture is additive, right? So the main thing that, the main conjecture that had to be made was that most phenotypes are sparse.

So in other words, in principle, all 3 billion base pairs could affect, let's suppose a phenotype is height. It could be that your height depends on the state of all 3 billion base pairs. Okay. Or the counter assumption is it's a small fraction of those 3 billion base pairs that affect height. It's a small subset.

And I was willing to accept that conjecture that it was going to be a small subset for each particular phenotype. And in fact, it turns out there's largely disjoint sets. So the set of SNPs, they're of course spread out, but the specific subset that's controlling their height is largely disjoint from the subset that's controlling your diabetes risk and the subset that's controlling your heart disease risk. So-

Like that assumption of sparsity was enough because if you try to reconstruct a sparse signal from noisy data, it turns out compressed sensing provides an almost optimal methodology for doing this. By the way, this particular set of things I'm telling you is not actually appreciated by most genomicists today. Like most genomicists, if I go to a conference and I'm trying to explain this to them, they don't really care. They're like, well, I use this algorithm or I'm doing that or whatever. If I tell them, no, there's actually this theorem of optimization.

regarding optimality and performance guarantees of this particular set. It's called L1 penalization. They're actually performance guarantees of how this particular algorithm will perform on that kind of problem. Most genomicists don't actually know that. So I already, like after like reading Terry's papers and like coming to the problem from this kind of abstract perspective already was, I think, way ahead of what other people in the field were thinking.

Yeah. Yeah. That reminds me. I mean, I have a friend who's like very deep into machine learning. You know, it's his full-time job and he basically complains that most practitioners have no idea what they're doing from first principles and they're just, you know, using, using packages. Yeah, that's exactly how it works. So,

You could say that like, okay, genomics is in a way like this part of genomics is a branch of like machine learning. And in machine learning, you have a very broad spectrum where some guys are just using cookbooks or recipes or other people's packages just to try to do something. But then there are people like if you go to the math department, you can find guys that are actually proving theorems. So you have both ends of the spectrum, but, but, uh,

The people on this far end of the spectrum is a very small population of those people, whereas most people are over here. So to get back to theoretical physics, so can you break down what a theoretical physicist actually does? Yeah.

Kind of like minute by minute, right? Like, do you spend most of your time just thinking silently in a room? Like, are you scribbling equations or are you going off on long walks? And I assume there's like a lot of collaboration with your collaborators, right? So I'm curious about how that works. It's kind of all of the above. And it really, it could vary by what particular thing is occupying my attention. So you could be in a phase where there's a particular area that you want to work in and

It's a very active area. So you spend a lot of time looking at the archive because maybe every day or every week there are new papers coming out on the exact thing that you want to work on. And so you have to spend a lot of time reading other people's papers and figuring out like, you know, trying to try to learn from what they've done, what's right about what they did, what's wrong, what, you know, what should I try to make use of? And then sometimes you're emailing them.

And saying like, hey, I don't really think equation 44 follows from 42 the way you say. So there's a lot of that going on. And a lot of that in reverse, too, because every time you write a paper, there's people writing you about what they didn't follow in your paper, what they object to in your paper. So there's a lot of that going on. I actually don't like having to read other people's papers on the archive. To me, if my postdocs or students want to drag me into an area where it's kind of like that, where you have to follow a lot of what other people are doing and it's very...

kind of crowded thing. I'll do it if the problem, if the specific thing is interesting enough, but I won't like it because I don't want to have to like read other people's stuff. Okay. So my ideal mode of working, which you get to much later in your career. So I've been like a professor for like almost 30 years. And so,

After a while, you're aware of certain problems that are not fully resolved or at least not everything has been worked out that should be worked out. And you know it well enough that you can return to it because you know it and you can come back to it and just think about it. So that's the most pleasurable phase where like,

I could be taking a long walk and I'm actually thinking about something that I've already thought for years about, but I'm coming back to it with maybe a new perspective or something like that, or maybe you could try this. That's the most pleasurable part for me, that and also just discussing physics with another theoretician. Like if I'm, you know, I just came back from this trip in China and like, you know, discussing some theorists that I've never met before, maybe some student who's still pretty young, like we could have this...

interesting conversation. I enjoy that a lot.

Um, one of the things about having worked on stuff, a body of knowledge for decades is that you have a lot of it in your head. And in a way, when I look back and I say like, oh man, I could have gone to work at Renaissance and made like, uh, I'd be like retired with a hundred million bucks right now if I had done that. But then I think to myself, yeah, but I wouldn't have this like super finely constructed understanding of quantum mechanics or quantum tilt here in my head that I can actually manipulate the way that I do. So like,

That you'll never get that other than spending a lifetime doing this kind of work. The other thing I would want to say is that like I noticed when I was a kid that my dad was sometimes absent-minded. He seemed absent-minded to me. Now I realize why he's a space monster. It's not necessarily a personality thing. It's actually just that if you're that deep into a subject and you're an intellectual and you keep returning to it, you can do stuff internally. Like he might be at the dinner table, but he's like actually working through something in his head.

And he can do that because he's developed that capability over many years. But to us, it looks like he's a weirdo. So in your work, like I'm wondering, what's the nature of the relationship between math and physics? So do you use your intuition differently?

to work through some thought experiments to come up with some story of what happens in a black hole and then try to back fit some math that formalizes it? Or is it the other way around where you start with some equations and then go wherever the math takes you, you know, derive some physical results from that, you know, like how you can derive the speed of light from Maxwell's equations? Like which one of those two modes do you work in? So this is a really great question.

Every physicist, every theorist is different on this particular question. And I am more toward the physical intuition where I'm thinking about what's happening. I'm visualizing it, or I'm even like in a kind of logical way, like saying, well, if this happens, like verbally, if this happens, then wait, doesn't it imply this? And that wouldn't that. So oftentimes I'm not just automatically manipulating equations.

I'm manipulating physical intuitions and concepts. And there are times when a problem has been boiled down enough. There are a couple of equations I could write down on a sheet of paper and just start noodling with them and then get to something non-trivial just through that sort of, in a sense, mechanical process. For me, that's the minority of situations. So usually if I make some advance, it's not through that.

I am actually jealous of people because there are people like I actually have colleagues who if I'm talking to them and I'm trying to explain my intuition about the problem, they're like, shut up, Steve, you're just confusing me. And then they're like, they want to write out equations and just manipulate the equations and they can make progress. Just, you know, again, mechanically, it's not really fair to say mechanically, but what seems to be mechanical manipulation of equations, they can actually make non-trivial progress. So I have one collaborator, collaborator,

A guy called Roman Booney, who's actually Ukrainian, Western Ukrainian, but trained in what was the Soviet system. But he's exactly the opposite. So so we make a great team because like I can I can read them through something and I say, like, I'm pretty sure this is true.

And I'm good enough with equations that I can then like formalize it for him. But then he's better than me. He'll generate like a hundred pages of calculations. Like if we're working on a problem and then like he goes away and calls me back in a few days, he might've literally generated like a hundred pages of calculations and also use like Mathematica to do stuff and...

So we're very complimentary in that way. I'm actually jealous of people that way because there are certain types of breakthroughs that I'll probably never make because of my style. And there are certain types of things he will never do because of his style. And then there are some people who probably have both. We're probably like equally as strong as I am in one way and as strong as Roman is in the other way. Like I'm envious of those people.

But I guess what you're saying is like progress actually happens in both directions, like by people going in both directions. Yes, absolutely. So the next topic I want to talk about is scientific progress in general. So there's this idea that's associated with Tyler Cowen called the Great Stagnation. The idea is that economic progress measured by medium wage or living standards has slowed down since the 1970s.

And he has some theories about why that is. So do you agree with like the broad contours of the theory? And what do you think is the underlying cause of the slowdown in progress? Yeah, I could have a huge multi-day discussion about this with Tyler and Patrick Collison and all these guys. First of all...

There is a measurement problem. Like, are you sure? Like, what do you mean by living standards slowing down? Like you and I are having this conversation, I've got my phone on a little tripod and

And we're thousands of miles apart, but we're having this great conversation, which we never could have had 10 years ago or 15, 20 years ago. Right. Or like I spent, people spend all their time like doing stuff on their phone, which they only pay, you know, if they bought a cheap phone like mine, a few hundred bucks for what do you mean by living standards went down? How are you measuring that? Because if I look at what people actually do with their time, it's,

Some of the inventions that we came up with recently have totally morphed the way people spend their time. And so isn't that an order one change in your quality of life? Because I prefer that to what I used to be doing, obviously, by my own revealed preference. It just happens that we can produce that very cheaply. So what do you mean by the rate of increase of your...

economic standard of living slowed down. So there is this overall measurement problem that I think needs more attention. I think economists are too willing to just accept that because they believe in equilibrium. So basically, they'll just say like, well, whatever the dollar value of this thing, that's the value of this thing, right? And I'm like, wait, wait, hold on, hold on, right?

Beyond that, though, I would say, like, if you look at other metrics, which are not as subject to measurement error, like if you say, like, well, what's the energy usage per capita or energy production per capita in the United States? It's flat. It hasn't changed in 50 years or something. So you could say, like, well, OK, aside from like this kind of fuzzy economic, you know, measured in units of utils or dollars or something.

Let's put that aside for a second. But just say, well, our ability to manipulate the physical world has not, the rate of our improvement in the ability to manipulate the physical world has slowed down.

And right, like all the progress has been in bits instead of atoms, right? That's like Peter Thiel's formulation. Right. So just to give a little more nuance to that, I think the right claim is macroscopic physical quantities, like how fast is my car? How fast is my airplane? How many kilowatt hours of energy are delivered to my house for me to use? Those have not changed that much in the last 50 years. And so you could regard that as a real slowdown.

On the other hand, it could be that like, it just turns out nature is hard that way. Like, it's just not easy for me to have a car that flies at supersonic speeds, right? There are fundamental aspects of physics itself that made that difficult. And we shifted our energies to manipulating the nanoworld and manipulating the nanoworld to make semiconductors, to make chips and memories and stuff. We have gotten factors of a million in my lifetime, more than a million. And so I kind of feel like

People just reproach this in like a way too naive way. It seems like you do agree with their thesis that like the low hanging fruit of like technological breakthroughs has been exhausted, right? Yeah. So to me, like one of the most persuasive ways they would frame the problem is to say like the way my grandmother's life changed where she started out with no running water, no electricity, you know, and she died with an iPad in her lap.

And, you know, no shortage of electrical energy or heat or and a washing machine and a robot that's like vacuuming the floor for them to say, like, OK, the arc of change for my grandma's life is so different than what I seem to be experiencing. And we seem to be like capped out on these what I was just referring to a moment ago is like macroscopic physical quantity. I think that's true. Now, is it because our systems or institutions suck?

Or is it just because fundamentally we picked some low hanging fruit, internal combustion engine. We figured out how to do that Bernoulli effect for lift and air, you know, like airfoils, like, okay, we'd pick some low hanging fruit. And then the next, the next fruit up there is it's pretty hard to get. And it isn't default of, it isn't an institutional failure or a systemic failure or, you know, problem with capitalism, right?

That's very possible in my mind. So I'm not saying I know the answer to this stuff, but no one's really fully excluded that line of thinking. And until you exclude that line of thinking, then it's just like we live on an innovation surface. In certain directions of the innovation surface, you can climb up easily and you get to a certain point, but then it plateaus.

And there's nothing anybody can do about that. Right. And in which case, then like trying to blame like the NSF for something or NIH is just the right year, like just barking up the wrong tree. It's it's possible, despite the fact that there are problems with our systems and incentives and the way academic research works. There are problems, but that might not be the main reason why energy production per capita in the U.S. has plateaued.

Right. So you're agnostic about even the whole framing of the idea that progress has slowed down because you point to like all this progress in the digital space. And then I guess you come down on like, it's probably caused by just like low hanging fruit being exhausted and not any kind of like failure of like how our society is organized. I think it could be both. Like it could be there are problems with the way society is organized and there's a low hanging fruit effect. Now,

When I'm at Silicon Valley confabs with like a lot of like engineers and when someone just says to me, yeah, what about that stagnation problem? You know, like not knowing that I've spent hours talking to Tyler and those guys about it. And maybe I would flatter myself and say, like, even thought much more deeply about it than they have.

I turn to the kid and I just say like, well, let's see, let's hypothetically, we might be literally one generation from being able to fully edit and control the human genome. And, um, also like we might be within one generation of a G I and a S I were a, it's super intelligent. Okay. Like way beyond human. Like if,

If that's true, what fucking slowdown are you talking about? Like, if we come back and have this conversation in 20 years and 90% of scientific research has been wrested from our hands by artificial minds that we created, like, in what way was there a slowdown? Like, I'm not sure what, you know. So, that's my view. That's one way in which, like, the digital progress can just spill over into, like, the physical world all of a sudden. Well, it could be that we have...

advanced super intelligences and stuff but it's still not easy to build little vehicles that let me zip around the planet at the speed of light you know because like gee aren't i going to burn up burn to a crisp try to move that fast through an atmosphere

There's another thing with people who really believe in the singularity. They're like usually miscalibrated about exactly what's going to happen. The moment after we get to the singularity, which things is the super intelligence just going to quote solve and like improve by a thousand X and which things is even it going to find hard because they're just basic physical constraints for it to change. Right. So it's not clear. Right. So all the recent technological progress in the digital space has been enabled by Moore's law.

Could you explain for the audience what Moore's Law is? And then, like, why have we been able to sustain so much progress in this very specific domain for such a long amount of time? And who do we have to thank for it? Right. So, this is one of my hobby horses. You might be asking me this question because you've detected this in stuff I've written, but...

So Moore's law is really our ability to continuously further miniaturize the components that we use for information processing devices like computer memories or computer microprocessors. And we've gotten factors of a million in the last few decades, right? And so in a way, it's like the most dramatic progress ever, like in any human activity, right? And so the funny part is like,

Again, like I don't want to I don't want to piss off Tyler and this whole class of people called economists who purport to study the economy or whatever they are. But again,

It's literally the fact that they didn't continue their studies in physics. So they literally don't understand physics. And so they course screen over this whole activity. This is a huge activity, right? The leading countries, Japan, China, Taiwan, the US, in some sense, Europe, everybody is funneling a significant amount of brainpower into this activity.

So if you ask the class of people who are smart enough to do a PhD in physics or PhD in engineering, well, what do they do in the economy? Well, a very large subset of those people are involved in the semiconductor industry. It's just a very big industry, right? If you set the threshold of brainpower, you know, set a threshold and then look at the people above that threshold, very big chunk of super smart, highly diligent, capital intensive activity is there.

And if you don't understand physics, like how could you possibly expect to understand that whole segment of human activity, which I would argue is driving, but it's caused an order one change in the way people spend their time. Literally the amount of what my kid when she was 12 was doing all day long has changed by order one, not by 1% or 10%, but compared to what I did all day long when I was 12,

because I'm a bad parent, so I got her a phone, right? So if I let her, she would spend half the day on her phone. So we invented something, and it's not like a trivial thing, like, oh, I got her addicted to opium, and she just takes the opium for half the day. No, it's like a thing, which is like a supercomputer that people in the 70s could never have imagined, right? And it

And it connects her to every library in the world and archive. So we built this non-trivial thing and we built it on top of this mastery of basically nanophysics, right? Everything that goes into making these components in the phone is nanophysics, right? You know, even the display. Okay. So...

I just think these guys don't know what the fuck they're talking about. They're literally not educated. So imagine like, imagine like some guy just never learned algebra. Right. And then like, he comes along and he's, he's like critiquing, like, wow,

Wow. I don't understand why it took them so long to solve for Modweiss theorem. It's, it's, there must be a problem in math departments worldwide because it took them. And this guy literally doesn't like know how to solve it. The guy who's saying this literally doesn't know how to solve a quadratic equation, but he's, he's critiquing like how long it took to solve the Poincaré conjecture or something. Right. That's,

I'm obviously exaggerating for effect, but that's kind of how I feel when I talk to these economists. They just don't. So basically, the economists are just underestimating what an incredible achievement it was to like miniaturize all of these silicon devices. To say it in a way which I think probably you would understand, but they would not understand is like there isn't a natural metric.

What is the right metric to measure progress? Right. And there isn't a natural one. They might use dollars, but dollars are affected by supplying demand. Right. And another thing, the one I proposed a moment ago was like, what kind of order one is change in the way people live, the way people live was created. And clearly there was one. So therefore, you can't say it was a small, you can't say we were stagnant. Right. Right.

There isn't a natural metric. These guys don't, I think, really have an abstract understanding of what that means. They just adopt their metric and then they notice in their metric things are not changing as fast as they want or as they expect. But maybe that isn't the natural metric to use. Right, right. Yeah.

But I also feel like really frustrating with economists is that I just don't think they understand innovation. Like, how does innovation actually happen? Like, how do we get better ideas about how physical systems work? How do we then apply that to make better devices? How do we bring those devices to the market in an affordable way so that everybody has one? Like, I don't know how many billions of cell phones there are now, smartphones. But they underestimate all of that stuff, all the complexity of that stuff.

So speaking of kind of like the pipeline from science, the technology, innovation to the economy, I want to talk about your experience as a university administrator. So in 2012, you were tapped to be the vice president of research at Michigan State University, where you managed hundreds of millions of dollars in research expenditures.

So you had an unconventional path to that point. You had been a theoretical physicist and a Silicon Valley entrepreneur, and suddenly you're in charge of a $400 million research enterprise. So first of all, how surprised were you when you found out you were being considered for the position? I was very surprised. I got a phone call from a headhunter. I was a very academic physics conference on black hole information. And I get this

this headhunter asking me if I'm interested in this job. And the first thing I asked her was, are you sure you have the right person? I thought maybe she had mistaken me for like Steve Chu, the guy who had been the energy secretary. Obviously Steve Chu wouldn't take this job, but, but somebody, some mistake like that. I sometimes people mistake me for Steve Chu, but yeah,

The first thing I asked her was like, are you like, do you have the right guy? Like, are you talking to the right person? It turns out when I, after I became a VPR and there are annual meetings, like, so there's the top US universities are all part of the AAU, American Association of Universities. And each of the key executive positions at the universities, whether it's provost or president or vice president for research or whatever, they have an annual meeting where you meet all your peers. So you're in a room, I'm in a room with like 64 other VPRs from all the other top universities.

top schools and you know a fair number of them are theoretical physicists so it's not it's not that unusual um it is a a job where you it helps to have a ton of breadth because it's not uncommon for someone it's like i'm say i'm a biomedical researcher and my main field is like you know

artificial heart pumps or something, right? It's very possible that guy doesn't really know anything about polymer chemistry, doesn't know what a quark is, doesn't know what P versus NP is, but they have to do this job. And the people at their university are working on all those things and more, right? So it is, in a way, one of the more intellectually interesting jobs at the university because it's

In my time, I had to make decisions about supercomputing center, gene editing lab, BSL for, you know, secure facility for researchers studying spread of malaria via mosquitoes. You know, are we going to have a primary facility on campus? You know, you can't you can't like there's no way to capture the complex, the set of possible things that can arise here.

If you're at a big 10, you know, R1 research university, you have all these professors trying to do different things and you're sitting on top of all that activity. You know, it is a very interesting job or potentially very interesting job.

So could you walk me through like how research funding actually flows through a major university like MSU, like just from like a high level, like where does the money come from? Like how is it allocated? How do you make those decisions about where it goes? So that number that you mentioned, I think when I came in, our annual research expenditure number was a little over 400 million. And when I left, it was about 700 million. So for the very top schools in the country,

It could be over a billion a year, especially if they have a big, a major medical center. The money typically comes from funding agencies like NIH, NSF, Department of Energy, Department of Education. But some of it comes from private foundations like Ford Foundation or MacArthur Foundation. And it's allocated through competitive grants. So your faculty are always writing grant proposals to try to get money to fund their research. And they're

has to be some kind of accounting procedure where dollars come into the university, they're held by the university, like the university becomes literally a kind of bank. And then like individual researchers can draw from their research account to order a computer, order a supercomputer, you know, pay some grad student their salary, hire a staff scientist, you know, buy a monkey and put it in a cage over here. All of that in this crazy system

These grants are awarded typically through peer review. So typically each funding agency is appointing a committee of people to oversee a particular grant process and then they're rating the proposals from the professors and the top rated proposals will get funded by the funding agency.

So then how much leeway do you have to, you know, as the administrator to like redistribute funds like among the different researchers, right? Because I understand there's like some element of overhead that's like taken by the university and then they have more leeway over that. Yes. So every university charges a certain amount of overhead on these grants. So if a dollar comes from NSF and goes to Professor X, our overhead rate might be 63%. So then an additional 63 cents is charged to NSF.

So if the researcher gets a million dollar grant, hundreds of thousands of dollars of that grant are going to go to overhead. That goes into my office. Maybe there's a split with the provost office. I then have some leeway to say, like, we have some funds that we set aside.

to, for example, hire new faculty. And so a big part of this is like money ball. It's just like major league teams trying to like get a particular athlete and you have to put together the package, the comp package.

In this case, it's not necessarily comp, but it could be like, she needs a million, she needs $1.5 million startup package to set up her lab, hire the first set of grad students and postdocs, do these renovations, buy that laser. But then, yeah, go ahead. How do you even decide in the first place that this is, you know, because obviously I'm sure everybody has their own priorities and the amount of demand like exceeds the supply of like money you have sloshing around. So like,

You know, you have to make some like prioritization, like you have to decide, do I give money to physics or ag science or medical research? Like, how do you even begin to prioritize those different things? Right. It sounds like something like a central planner in the Soviet Union would have to deal with. What kind of frameworks did you use to think about relative value? That's what makes it a super hard job.

There are some systems where things are very determined almost by formulas. There's no judgment involved. We were more at the other limit because the president here had a lot of confidence in me, where my office had a lot of leeway in terms of what strategic initiatives were we going to go after, where the money was going to go. And consequently, we made a lot of enemies. So there were fields where they just kind of knew they weren't going to get any money from us. And there were other fields where we built up Michigan State's

substantially. We built a billion dollar DOE nuclear accelerator lab on our campus. So it's a DOE lab, but it's on our campus and we run it for them. And it's a billion dollar project. So we pick winners. Not all BPR offices or even provosts or presidents will pick winners on their campus. And maybe

Maybe they should have because they're not qualified. You're saying some people are just on autopilot, but you had a lot of discretion. So like what were the criteria you actually use, right? Like for that specific project, how did you decide it was more valuable than the others that could have been funded? There are like several criteria. One could just be money. Okay. So one thing VPRs are measured by, like I said, we grew our resourcefulness.

research expenditures from a little over 400 million to 700 million during my time. So just fund people who are able to come up with more money the more you fund them. Yeah. So it's like, oh, this is a hot field. NIH really wants to fund this field. We already have a strong group here. So the new person, the N plus one person can thrive.

Okay. There's a good argument for that, right? Oh, this field gets no money. They're like federal, like they're, like they're tuning down the support for this area of polymer physics or something. Why should we hire another person in that area? So there's just that. But then the other part of it is like actual intellectual depth. Is this really an interesting area of, you know, science or whatever engineering. And for that, you need, you need actually an understanding you like the person who's a

affecting that part of the calculation needs to really understand. Like, is this non-trivial or is this bullshit? For example, there are whole areas of biology where they take imaging technology that comes mainly from physics or engineering. It

It comes into biology and then all of a sudden they're able to make really beautiful pictures. Like, oh my God, look at this electron microscope image of this ant's face or this bee's eye. Those can get on the cover of nature and the biologists seem to love this. But you have to decide like, is this a real advance or is this just like kind of a superficial thing? And is it really going to, what's it going to lead to? Like, do they actually understand the ant better? Correct.

of this picture of his face. So there were times when I was like saying to like the people on my team or the dean or the relevant people in the university, like,

I understand this is a hot area, but I think it's bullshit. So I could be in that position. Or I could be like, I had a whole thing while I was here where I was trying to emphasize to the professors on campus that reproducibility or replication of research was a real concern. Now it's well known that like a lot of work in certain fields just doesn't replicate. But at the time, I was very early in saying to professors,

if psychology wanted to hire a social psychologist. And I was like, yeah, but I, I don't think that stuff replicates. Is that stuff even real? You know? And they were saying like, well, this is the hottest stuff, man. You can, you can be like in a Malcolm Gladwell book and, and, uh, NSF actually likes to fund it. And you can be on the cover. You can be in the New York times if you, and I'm like, yeah, but is it real? Like, so. Yeah. Yeah. It's pretty shocking. Like how little, how little social science research is, is able to be replicated. Right. And then, uh,

You know, last year, Mark Tessier-Levine, you know, this prominent neuroscientist, he's actually the president of Stanford. He had to resign his position because basically allegations that his entire research about Alzheimer's was fabricated. How surprised were you? I was not shocked. I spent literally eight years trying to educate my faculty at my university that this was the case.

So there's a researcher at Stanford that what was the case that people were just outright engaging in fraud or that things are fraud or wishful thinking and poor statistical sophistication leads to people believing in results that really the evidence for those results is not very strong.

So it's a mix of those things. I actually think the fraud part, well, at the time, I thought the fraud part was the minor component. The bigger component is you have whole sets of researchers who barely passed their watered down statistics course in grad school. So there are whole fields. You can look and see like which PhD programs...

do the grad students, can they actually take the real stats course in the math department or in the stats department? Or do they have to like kind of make their own stats course, which is much easier. And the students still regard that as like one of the hardest courses in the psychology department in order to get their PhD, right? If you have a population of people that's like adversely selected for ability to do statistical reasoning, then they're very, very susceptible to believing results that just sound good

But the confidence level they should get from the evidence is not as high as the narrative

creates in their mind so my contention is there are whole populations like that even in the quote science professorial including in biology and so and especially biomedical science like a lot of stuff that people think they know in biomedical science is not actually true and and actually if you if you were sophisticated both in statistics and in the details of the studies you would look at and say like yeah 50 50 that's true

Okay. Right. So when I was here, there's a researcher at Stanford named Ioannidis, who's quite famous. He's one of the first people who started to point the finger at the replication crisis.

And so he would do these look back studies where he would look at the most prominent articles in like nature from 1997. And then 10 or 15 years later, he would do a look back and he would say, okay, these 10 papers, which are the highest cited or they were on the cover of nature or whatever, we'll do a look back and we'll see like how many much higher quality, better powered follow-up studies were done on this topic that either were

confirm or dispute the results in that paper. And he comes up with a number like 50% reproducibility for the most

the most like prestigious results yeah okay so it's something i think the title something like 50 percent of all published research findings are false yeah yeah and and nobody knows which 50 percent so anyway so eonidas and i so i invited him to mtu to give a lecture to our whole faculty in the big auditorium right and uh you know so this was me trying to say to you guys like hey you should be a little more skeptical about results you feel but i think it had no effect

Yeah, yeah. On these professors. So, you know. The funny thing is people intellectually, like Ioannidis is famous. He's very highly cited. He's one of the most highly cited researchers actually in the world. So his work is cited a lot. But then when you in private talk to an actual, say, biomedical researcher, they've not done an update as if they believed Ioannidis. Because then when I say like,

just in some complete different setting, like they're saying like, oh, we could maybe steal this guy from Mount Sinai graduate school, you know, blah, blah, blah. He's done some really great work on targeted cancer, blah, blah, blah. And I'm like, well, what are the chances this is right? And they don't, they don't like get, they don't view it from the Ioannidis lens. They're just like, oh, this is excellent work. This is fantastic. It's definitely, it's definitely kind of like, okay. Yeah.

So I think people in general have a very hard time assimilating like theoretical knowledge, right? Like, you know, if they read a book, it's kind of, you know, it makes some kind of impression on them, but it's not going to change the course of their, it's not going to change their behavior. One of the benefits of being in frontier theoretical physics is that we are really concerned about the fundamental laws of physics. We are constantly doing huge experiments to probe those fundamental laws.

And those experiments, because they're at the technological edge of what people can do, are themselves noisy. And so every month or few months when you're a grad student in a PhD program in theoretical physics, if you're in frontier physics, you go into a seminar room or a colloquium and somebody's like announcing this amazing discovery at CERN of this neutrino or this kind of like galaxy, you know, this gamma ray burster in this galaxy. And

You get to see, you've seen several reps of the field going crazy over some new experimental result and then better powered experiment from along and their statistical significance of the result goes to zero. So you've seen that movie many times in our field. And so it's very easy. People from our field understand this thing. And I would say to most venture capitalists, you should

understand this too because a lot of what you hear from founders is bullshit and especially if it's deep tech like it's actually never going to work but until you've seen that movie a few times you don't you're not well calibrated on the level of skepticism you should have and people in some fields like biomedicine they've never seen the movie so like they just they just all continue to believe that like you know x is true maybe it's only happened once right yeah there's only been one reckoning yeah so they're not they haven't they haven't updated

All right. So Steve, you mentioned that there's kind of like a reproducibility crisis in science or academia. So what's your mental model? Like what's your map of how this varies among like the different disciplines? Is there a pattern like which disciplines are susceptible to this or is it just a matter of kind of like the internal culture of each of these disciplines, like a path contingent kind of thing? I think there are at least two independent factors that I think influence reproducibility.

how well calibrated people are in a certain field, you know, in their own specialty. How well is their confidence level in a particular claim correlated to the actual probability that that claim is true? One is just some basic ability in thinking probabilistically and thinking statistically. Some people are just good

good at it. And those people tend to go into fields where there's a little more math. It's a little more likely that those fields are well calibrated. But another independent factor is, is the field basically siloed, very, very siloed, where people like specialize like crazy, and they just work in this little silo? Or is there a lot of like cross pollination across the discipline? And if there's more, I think generally, people tend to be better calibrated.

So I wonder where macroeconomics would fit in that framework of yours, right? Because those people are pretty mathematical, but it doesn't seem to me that they've been producing anything that's been very empirically validated. Yeah. So I think on the first metric, they're fine. They have no trouble understanding. In fact, they use quite sophisticated statistics. But there, I think it's a little bit related to the second thing, or maybe more specifically, I could call it selection effect. Yeah.

Because every field, the people that are going to end up being like having a career in that field, so they really matter, like they're going to be in the field for 20, 30 years and they kind of determine what's accepted fact in that field. That's a very small fraction of the people who at least initially look into that field.

So if you look at all the grad students in economics or even like more broadly, like I might have gone into economics, but I didn't go into economics. Right. So so you have like I might have gone into economics, but I did it or I went into economics. I looked at macro and I realized these guys are just telling stories to each other. So people sort of help to let out.

I think that's kind of more the problem in- It's just like a self-perpetuating kind of like bad culture among any given discipline. Yeah. And I think- That's pretty depressing. That's pretty depressing. A lot of economists are cynical enough and they understand group dynamics enough that they could even build like toy models of like how their own field works.

So like, I've seen those actually people talking about where we're basically have a model where like, yeah, all the people that are susceptible to this kind of thing, they all end up here and they, they, they form their own little silo where they do their thing.

And then other people are looking in from the outside and just like, I don't believe it. But the main problem with macro is those guys actually have influence over central bank and to less degree, lesser degree hedge funds and stuff like that. But they do influence central banks. So I think lots of stories we tell each other, tell ourselves about macroeconomics, like the Taylor rule for interest rates or stuff like this, you know, not clear to me how ultimately true some of these things are. Right, right.

So zooming out in terms of, you know, basic research in general, right? I guess the, like, what is the point of all this, this basic research, like society spends billions of dollars on it, like the US federal government definitely spends that much. I think the standard story is that basic research is upstream of technology, and then technology is upstream of economic progress. So there's this idea, like for every dollar you spend on basic research, there's a very high ROI for society.

And then there's this countervailing idea that actually a lot of technological progress is completely independent, right? Like the Wright brothers, they invented airplanes without knowing anything about Bernoulli's law. And it was only later that people created the math.

So do you feel like there is a payoff, you know, going from basic research to technology or is that overstated? So a couple of comments. So number one, you know, whatever our weird mix of like research investment collectively as a society is or has been in the last hundred years, I think properly calculated the ROI is super high.

So not saying it isn't really fucked up and you couldn't improve it a lot, like by not funding certain things or changing the way you fund certain things or removing tenure, whatever it is, regardless of all those possible potential improvements, whatever system we had in place, I do believe delivers huge ROI. So that's one observation I'd make. The other thing I would say is that sometimes basic research is upstream research.

of technology. Sometimes it's not. Okay. So sometimes you can have technologists who like get the steam engine working, even though they don't know what the second law of thermodynamics is or entropy, right? So, or the Wright brothers. So we should fund technology, just kind of like tinkering and innovation all by itself, even if there's no kind of fancy theoretical basis for it.

But I can give you many, many examples of like, well, no nuclear bomb without some detailed understanding of quantum mechanics and nuclear physics, right? No gene sequencing without some really detailed understanding of the physics of, you know, how to like, you know, cause some amino acid to attach to some probe or something. So it doesn't have to be the case that all technology, all technology, technological progress is downstream of some fundamental science.

But if enough of it is, it still could be that the ROI is just from that little tiny subset of the fundamental research that does have downstream useful consequences. Right. So that's kind of how I view it. But that's how I had been viewing it for most of my life. And then, you know, recently I was hanging out with my friend who's like a professor of physics. He said something that just blew my mind. He was he was basically saying like, oh, you know, we have these graduate students who

And they're not the best researchers, right? They're actually like the worst possible researchers because they're just learning how to do things. And I don't think my lab is going to, you know, I don't see the purpose of my lab is like coming out with these amazing like advancements that will, uh, you know, create like societal progress. Like I actually just think my main output is just increasing the human capital of these, uh,

These kids. And so that kind of blew my mind. I was like, I didn't even consider that like you, you didn't believe in this, like that anyone didn't believe in like the, the, you know, the mission of like creating basic research. I think that's a minority view. What field is your friend in? Like what area of the VIX? He, he does like optical, optical stuff. His background is in chemistry. So, so I think, you know, he did a lot of like laser stuff during his time in the chemistry lab. Right. So in, in optics, you know, a lot, there are a lot of industrial, industrial,

applications for what some student would learn in a PhD in that field. So I think that view is not completely crazy, but I think it's a minority view. Like I think most people in

Like in particular, say quantum optics, they are actually trying to make scientific advances. And they would probably view it as a blend of, yeah, I'm creating human capital by training these kids up in these important technologies. But we're also trying to write published papers that push the frontier of knowledge forward. So that's, I think, what most professors think.

Right, right. You seem pretty optimistic about kind of the returns on basic research, but obviously, like there's some inefficiencies in the system. I don't know if you're familiar with this writer named Alexei Guzei.

No, I, well, maybe tell me, tell me what he says. He's been kind of looking into biomedical research for a very long time. And he recently caused a stir. He published this article called Abolish the NIH, National Institutes of Health. Yeah. And so he wrote in that article, the NIH is a quote, tyrannical, capricious organization.

self-serving $50 billion Kafkaesque Leviathan. So having seen the system from the inside, how would you react to that characterization? And then how would you... If you were to scan across the range of funding institutions that we have federally, would you look at any of them and say, oh, that's completely mismanaged or we need to abolish that specific institution? So I do know who you're talking about. And is he funded by Tyler? Yeah. Yeah.

I think so, yeah. So I think I even read this article when it came out. Well, I would say this about the NIH. People don't realize it's by far the biggest chunk of our basic science budget. It's way, way bigger than NSF or DOE or the other major funding agencies. And this will get me into huge trouble, but I'm not a VPR anymore, so I can just say it. I would say that

And NIH produces a lot of low quality. Biomedicine in general just produces a ton of low quality research. And even though that'll piss a lot of people off, I could just point them to Ioannidis' findings and just say, look, it doesn't replicate.

Actually, gosh, I don't know if I can say this out loud. It's been many years, so maybe I can say it. So when I had Ioannidis here, he told me something confidential that he and his research team at Stanford had really perfected this look back method where you define a set of results from year X.

You wait to, you know, another 10, 20 years beyond that, and then you do a look back to see how good those results were. It's a well-defined methodology that he and his team pioneered. He actually did it. So every year, the NIH director publishes the big breakthroughs from NIH-funded research that happened this year.

So they did. I don't know if they still do it, but they did it for a long time. So Ioannidis' team just took some of those. And so he did a look back on that and it was not pretty. So what did that tell you? Like director of NIH is supposed to be some kind of expert in science and they're spending $50 billion and they've cherry picked the best stuff.

once a year they do it right so yeah the the specific proposal is just to like sunset um all the existing grants let them run out and then just reallocate that money to to different um to other institutions like so it seems like maybe you're open to the idea of abolishing it would cause incredible chaos but of course i i am open to it um

Here's a joke. Again, I would never have said this when I was a VPR, but I said this many, many times privately, but I would never have said it publicly, but I can say it publicly now. Yeah.

If you took the money from NIH and you gave it to computer scientists, physicists, engineers, but you gave it to them to do things which might be useful in biomedicine, right? So like AI for biomedicine or some better like imaging capability or some better way, you know, column x-rays or something. You know, I used to joke to these biomedical guys, like we would get more bang

from that buck if we gave the money to these other types of scientists but said they have to do something which is relevant for biomedicine? Because it looked to me like every time there's a big breakthrough in biomedicine, it's because of some alien technology. Like some aliens developed the laser and gave it to you. You don't actually know how a laser works, people at NIH, but people outside invented this laser and gave it to you and now all of a sudden you can do a bunch of stuff, right? Or these other guys figured out how to sequence genomes and we gave that to you. So

My joke was we should just give the $50 billion to these other scientists that are outside of NIH, not NIH-funded people. And you'd actually get more biomedicine progress out of that than those guys do these. Of course, they would get extremely mad. If somebody were sitting next to me and I just said that, and that guy was an NIH-funded researcher, they would just blow their top. But I still – Yeah, I think the reactions to the piece were pretty angry as well, like you could see on Twitter. Yeah, I believe it.

All right. So I have a bunch of random questions as we're wrapping up. You know, we're recording this November 2024. And about a week ago, Donald Trump was reelected as president. So what was your experience finding out about Trump's election? What happened? I was in China.

On a trip, mostly scientific trip, I gave scientific talks on physics, like at the Chinese Academy of Sciences and other universities. But that particular day, I was climbing on the side of a mountain at almost 5,000 meters. It's called Jade Mountain in Yunnan province, which is the far southwest. It's kind of near the Tibetan plateau.

And I was climbing up and I had access to the Internet because of Huawei 5G through my phone. And then I have Google Fi as my telecoms provider in the U.S. and that has roaming in China. So you just get unfettered access through that while you're in China, no firewall or anything like that.

And so I was able to follow the election in real time. There's a 13 hour time difference. So, you know, midnight on election night on the East Coast is like one in the afternoon when we're doing our climb. And I'm following the election. I'm like, oh, my God, Trump is going to win in a Lance. Like I was kind of expecting him to win, but I didn't think I thought it was going to be close. I thought it might be a nail biter. I was like kind of glad that I wasn't in the U.S. because I didn't want to like.

follow it that closely, but it looked like he's going to win by a landslide. So I actually was screaming out like, you know, you know, Trump, Trump, Trump is going to win. You know, it's a landslide. And these Chinese people found it, you know, they were like, yeah, they kind of straight. But which I think one other person fist bump too, that might've been another like tourist or foreigner, but they were maybe just some Chinese guy who understood. But anyway, it was, it was a peak little pun intended here. It was a peak experience.

to follow Donald Trump's, some people say, third triumph in a row while climbing a mountain at 5,000 meters. So what made you so happy about it? What made me happy was that I almost entered the...

Trump administration, uh, you're breaking news here. Cause I don't think I've ever said this publicly. So in 2016, when Peter Thiel was playing a big role in the transition because he had supported Trump during that election. Um, and he had brought entire binder full of Nate names of talented people who were willing to serve to the white house. Um, so I almost joined the Trump administration in 2016. Um,

I don't want to say too much more about it. It was at a very senior level that would have required Senate confirmation. But as a result of that, I got an inside view of our intelligence services spying on Trump, as well as Peter Thiel and other people in that circle. I think this is kind of well documented now, though it will never be fully directly addressed like in a New York Times article, but they've kind of admitted it.

that they did get a FISA warrant and they did monitor everyone within two hops in the network, two hops in the social network around Trump. So that would have included a lot of people like Peter Thiel and myself. So we were being spied on, potentially, by the U.S. intelligence services, which did everything they could to undermine Trump. And so he did fight the deep state.

uh, during his first term. I just think it's incontrovertible. Now, now plenty of people who are like on the left or just believe what the New York time print is generally true, just won't accept what I'm saying to you. But if you care, if you actually care about what I'm saying, like you can just brush it off, but if you actually care and you do your own research, you will easily convince yourself that, that stuff like this happened. There's an inspector general report, uh, within the part, I think department of justice on specifically on like the FBI activities, um,

I think the inspector general's name Horowitz. You can go look this up. Of course, like this is the kind of thing that like it's a multi hundred fake report and it's completely buried by the legacy media. But if you want to go read it, you can see there's lots of interesting stuff in there. So deep state tried to undermine Trump. Unbelievable amounts of lawfare, just crazy things meant to take him out of the race and murder.

I'm totally okay. Like, I have very close friends who hate Trump, who don't want to vote for Trump, would never vote for Trump, who think he's a disaster. We're still friends. It's not a big deal. But if you say it's okay for us to bend the law because he's so bad, that we can do these things for which there's no precedent, but we can use these tools to try to have lawfare or whatever to try to attack him and take him out of the race. Yeah.

We can, you know, totally co-opt the media, censor information like about the Biden laptop. That's all OK. I disagree. So I feel like Trump overcame so much in this third election. That's why I felt exhilarated. Not so much because I'm like the biggest Trump fan in the world. I see a lot of faults in Trump. And I saw how dysfunctional his first term was. Peter Thiel actually kind of got...

disenchanted with Trump and just kind of after a while stopped, you know, really playing an active role in the administration. So there's lots of negative. How did that relationship, how did that relationship break up? Because, you know, from the, from the beginning, it seemed like he was, he was going to play like a pretty big role. Then kind of silently, he just disappeared from the scene. I, I don't, shouldn't comment too much about this, but one thing that is, you know, if you just ask in Silicon Valley circles, a lot of people just say like, well, Trump is too mercurial.

And if you're a Silicon Valley guy and you're used to dealing with like, in a way, Silicon Valley is like a super high trust environment. Like people are competing against each other. But once we decide we're on the same team, like we're in the same startup or we're in the same cap table of the startup, there's a way like we collaborate. We push things forward and we act kind of professionally with relative to each other.

I'm not sure any of that exists in the Trump world where like he could just pass you aside or, you know, like, so I don't want to make specific comments about it, but I think lots of super capable people who tried to work with Trump with the best intentions came away maybe with a negative opinion. Okay. And it could happen again. Okay. This time around it's Elon who has the, the big influence on Trump and,

Or vice versa, but let's see how long that lasts as well. Elon now is a different situation because Thiel's business interests weren't that tightly bound up with his political influence with Trump, whereas I think Elon stands to gain and lose a lot depending on how much influence he has with Trump. So I expect to see him stick it out a lot longer than maybe Thiel did. Also, Elon's much more comfortable in the spotlight. I don't think

Peter was, Teal is that interested in being in the spotlight. Are you going to have a role in the next Trump administration? No comment. So I tweeted something asking for people to send resumes if they wanted to contribute to, specifically I said, restoring meritocracy and

competitiveness to U.S. institutions, especially like the scientific funding agencies and things like that that we've been discussing. And I've been just overwhelmed with resumes. I've gotten hundreds of people sending me stuff. I'm not an agent of the Chinese government. I'm not sending this information to China. I'm only sending it to my contacts on the Trump team. Believe it or not, I was accused of that. That's the great thing about Twitter is that all segments of society are participating. So...

Whether I would participate or not, no comment. So, yeah, I guess that brings me to the final question. The 20th century was a great time for America. There was a lot of scientific progress. We had these wonderful institutions. We hoovered up all the best scientists around.

Yeah.

And, you know, for someone who's as cosmopolitan as you, you seem to care deeply about this particular country's future. For you, what is at stake? Like, what is it that makes America exceptional? And, you know, what makes it worth fighting for? Well, I feel very blessed because I grew up, you know, I had an idyllic childhood in Iowa. I had great friends. I grew up in a very high trust, nurturing environment in the Midwest.

And so I hope for the best for America. And even the fact that like my parents who are immigrants from outside the US could come here and be accepted and have close friends and live a wonderful life in America. Like, I would like to see that continue. I would like to see the best of America continue into the future.

I'm worried that that won't be the case. We could easily be in for a hard landing with the huge debt that we've accumulated as a country. We don't seem to be able to produce things very efficiently in this country anymore, with some exceptions, obviously. So I am worried about the overall competitiveness of America. I'm worried about the erosion of meritocracy in American society. Lots of those things.

I do think, though, that for the class of people that are so talented that, you know, they can they can go wherever they want and work wherever they want in the world. I think America will still be an attractive place for that class of person, at least for another 10, 20, 30 years. I mean, unless there's some unless, you know, our politicians screw things up so much that they, you know, cause a dollar crisis or debt crisis like the Treasury has an auction. They can't sell their bill, right?

Um, you know, unless we hit some really sharp inflection point, I think America has a long way to go where it's still like a super desirable place for elites to be. For the average American, I feel like the average American is maybe less well off than they were, you know, when I was growing up. Now, someone like Tyler would say like, oh, no, they're actually, they've got lots more, lots more money or incomes are much higher or whatever. But

When I grew up, it was unusual for mom to be working. You had intact families, at least in Iowa, and usually only dad had to work. Mom could spend all her time nurturing the kids, cooking dinner or whatever. Not saying that's the best thing, the best use for her talent, but it was a very different situation. And people did not seem, their existence didn't seem as precarious as the way I perceive life.

you know, the 50th percentile, 35th percentile American to be living. So I'm worried about those people. Those are the people that elected Donald Trump. I'm not, you know, I'm not naive. Maybe Trump doesn't care about them. Maybe he just is using them to gain power. But I think someone should be concerned about those people and be fighting for those people. Well, Steve, the fact that people like you are out there and potentially staffing the next administration gives me a lot of hope.

I really enjoyed this conversation today. I've really enjoyed the conversation and hope your podcast is a big success.