Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 5 | Geoffrey West on Networks, Scaling, and the Pace of Life

Episode Date: July 16, 2018

If you scale up an animal to twice its height, keeping everything else proportionate, its volume and weight become eight times as much. Such a scaling relation was used by J.B.S. Haldane in his famous essay, "On Being the Right Size," to help explain certain features of living organisms. But scaling relations go much deeper than that, and they are often much more subtle than the volume going as the cube of the length. Geoffrey West is a particle physicist turned complexity theorist, who studies how features from metabolism to lifespan change as we adjust the size of an organism -- or of other complex systems, from cities to computer networks. His insights have important implications for innovation, sustainability, and the best ways to organize life here on Earth. [smart_track_player url="http://traffic.libsyn.com/seancarroll/geoffrey-west.mp3" social_gplus="false" social_linkedin="true" social_email="true" hashtag="mindscapepodcast" ] Geoffrey West received his Ph.D. in physics from Stanford University. He is currently a Distinguished Professor at the Santa Fe Institute, where he served as President from 2005 to 2009. He has been listed as one of Time magazine's 100 most influential people in the world. He is the author of Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. Home page Wikipedia page Amazon page TED talk on "The Surprising Math of Cities and Corporations" Google Scholar publications Download Episode

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Visit your nearby Lowe's on West Pico Boulevard in Los Angeles. Hello, everybody, and welcome to the Mindscape Podcast. I'm your host, Sean Carroll. And if you're familiar with my book, The Big Picture, or any of the various talks I've given on that book, you'll know that one of my favorite factoids is that the average human lifespan is 3 billion heartbeats. That's not a very profound fact

if you just take the fact that the average human being lives for about 75 years and do your dimensional analysis to convert from years to heartbeats. The number works out to be about $3 billion for a typical human being stretching from birth to death. But it's an interesting fact because it brings home in a slightly more vivid way how short our life is. $3 billion is a big number, but it's not unimaginably big. And unlike years, heartbeats are going by all the time. You've squandered several of your heartbeats already, listening to me talk right here. And I've learned this fact about the $3 billion heartbeats from today's guest, Dr. Jeffrey West,

who's a distinguished professor at the Santa Fe Institute, and it's in the context of a much more profound fact about biological organisms here on Earth. If you take any particular kind of organism, let's say mammals, because human beings are mammals, they come in all shapes and sizes. There's tiny little mice, there's big old blue whales or elephants, but there are relationships between the size of an animal,

in mass, for example, the number of kilograms the thing has, and other biological facts, such as how long it lives. Bigger animals, whales, and elephants live for much longer than tinier things, like mice or squirrels. Meanwhile, there's another relationship between your size and your heart rate. Big animals, like whales and elephants, have very slow heartbeats. Tiny animals have very rapid heartbeats. And you can see where this is going. These two facts exactly cancel out.

the average number of heartbeats for mammals is approximately the same for tiny little mice or big old blue whales or elephants. It's not an exact relationship. In fact, the number for a typical mammal works out not to be $3 billion, but $1.5 billion, which maybe you could argue is roughly what we had we human beings back in the state of nature before we had pasteurized milk in Obamacare and things like that. But the point is that something is going on that goes beyond mere by There's some reason why there's a relationship between how fast your heartbeats, how massive you are, and how long you live. That's what got today's guest Jeffrey West interested in biology, networks, and complex systems. He started his academic life as a particle physicist, but he read about these scaling relations, and he realized, from his physics point of view, no one understood them. No one

knew why things were like that. So he and his collaborators developed a thing. theory that explains why you get this relationship. As an animal gets bigger, it lives longer, but its heart slows down. The pace of life is slower for larger animals. These days, he's extending that analysis not just to individual animals, but to cities or cultures or other kinds of networks that fill our daily lives today. This kind of analysis is absolutely crucial for sustainability, for thinking about how we live in the world, for choosing how to make a manage our life here on Earth. Jeffrey West is the former president of the Santa Fe Institute,

and he's the author of a wonderful recent book called Scale, the Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. It's not the most elegant title, I'll give you that, but is an extremely important and hopefully influential book that helps us understand the world we live in. So let's go. Well, my dog Harvey needed a special joint supplement,

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Jeffrey West, welcome to the podcast. Hey, nice to be here, Sean. Thanks for inviting me. So I have to ask, when you're on an airplane, going to a conference traveling around the world, and you find yourself sitting next to an inquisitive type, and they say, so what do you do for a living? What is your answer to that kind of question? Oh, goodness me. That's a tough one. And it always stops me in my tracks, but, A, because I'm slightly misanthropic. But what do I say? I think I always lead off pretty much by saying I'm a physicist.

And then I quickly say, I'm a physicist, but I've worked now a lot in questions, challenges that are considered outside of usual physics. And I've done quite a lot of work in, you know, what I would consider fundamental questions in biology and now about cities, companies. and I'm particularly interested in the whole question of global sustainability. Right. So I sort of do it in that, I mean, maybe not quite as linearly as that, but effectively. But now in your youth, you were more or less mainstream theoretical physicists, right? Absolutely. So what kinds of things did you work on then and what happened?

So, yes, so most of my career, meaning the first 30-odd years, I did definitely what you would call mainstream. particle physics, even entering it when I would say that the kinds of things you work on and you've made your name on were considered way out, you know, they weren't questions. A real physicist should be asking. Cosmology dark. Yeah, yeah, it was like, come on, you know. And I think one of the, by the way, just tangentially, one of the great things that has

happened in physics is that we do. do ask those questions and we address them seriously and it's now developed into its own thing and it's had profound influence. But back then I was a mainstream physicist working on, well, I entered it very fortunately at a really opportune moment because these famous experiments which got the Nobel Prize at Stanford on, which eventually, which eventually, we interpreted as discovering quarks were taking place. The results were just coming out, and I had been sort of geared for that. Well, they were the late 60s, actually, in late 60s.

I got my PhD in 66, and the accelerator at Stanford was just being completed, and the first experiments were being performed, and the results that changed everything, which was, were results on an experiment that at the time was thought of as mundane and uninteresting appeared and they were quite surprising. And as I say, eventually they were interpreted as the evidence for quarks. And that was a very exciting period, as one tried to interpret those experiments, try to formulate models and then theories and ultimately the evolution towards

something called quantum chromodynamics, the theory of the PCD, the theory of the strong interactions. And that was very exciting. And did you feel that this is what life was going to be like? Every few years, you would discover a new layer of structure of matter, new fundamental laws? Absolutely. No, in fact, I would say during that period

from probably 6870 onwards through the 70s and into the early 80s, we were kind of spoiled because every, year there was a new fantastic discovery that, you know, either or confirmation of something. And it was just a marvelous period. And it culminated in the development of the so-called standard model where we see some sort of grand unification, or at least we saw that you could contemplate a grand unification of the electromagnetism with the strong forces and so on. And that was just immensely exciting.

And then that led to this naivete that it would only be a few more years when we could get gravity into the picture. And so, as I say, but it was a period of immense excitement, but a period of being spoiled as one does. You know, I mean, it reminds me a bit, it actually is sort of interesting when I look back on it because it also had some of the flavor of the 60s and 70s in society.

You know, the emergence from the 50s and early 60s and kind of the image of suburban America with 2.4 children and that image of everybody we all had to suddenly, the psychedelic revolution, the Vietnam War, and what the demonstrations and anti-war movement brought, the civil rights movement. But then we hit the Reagan era.

And then we hit exactly. And so it is that in physics we hit not because of a Ronald Reagan or because there was a vote, but because it turns out that nature was not quite as forthcoming as we thought or we weren't smart enough, despite what I do think was even though I've been quite critical of it I think an extraordinary development and that is of string theory I mean it was a marvelous leap

But you did mention discoveries and or confirmations and there was a shift so after the mid to late 70s there weren't a lot of more surprises coming our way. We were confirming things that we had models that explained and indeed it's sort of the dirty little secret of

particle physics, there haven't been any surprising. No, exactly. Exactly. There's been nothing that is sort of, you know, in fact, the big things, as you know, better than I, you know, gravitational waves of the Higgs particle and so on, are confirmations of things that in one case were predicted, you know, I don't know, whatever, 70 years ago or whatever, or longer actually. And then in the other 30, 40 years ago, whatever it's been. But a long time, I mean, very exciting that we have confirmed those. But also, especially in the case of the Higgs, it's, you know,

really in some ways it's like the end of something. That's right. Rather than, I mean, gravitational waves, I think you could see it is the end of something in a certain way. We don't, but it's also the beginning of something. For astronomy, it's certainly the beginning. And it is, exactly. the thing. It's now even so that we can use it as a tool again. Whereas the Higgs is sort of

putting a full stop, a period at the end of something. It's now done and the big questions that have been there since the mid to late 70s remain, A, how does gravity fit? But even within Grand Unified, we're left with all these parameters and you know, where do they come, we're unified in a certain sense, but in the sense that a physicist would like, ultimately, we're still a long way away. No one thinks it's the right final theory of everything what we have right now, but it's compatible with all the data. This presumably played a role in your choice to become a less mainstream physicist.

Yes. Well, it did in the following sense that it was frustrating as time went on, but also So, you know, we suddenly developed this idea of putting all our eggs into the superconducting super-collider basket. And with the idea that with this longer reach of energy, it would reveal something exciting, new. It would, in fact, confirm the Higgs, of course. And this was to be sort of the United States' version of the large Hadron Collider, but bigger and better and maybe even sooner, had it all come to be. Absolutely. And, in fact, I mean, one of the great.

irony is, again, maybe you use the word dirty little secret. This is also a sort of dirty little secret, I think, is that in the days in which people were promoting the SSC, you know, to get the money for it and just to drum up the general interest, of course, CERN, the Europeans, had responded by saying, we're going to make this large Hadron Collider, which would be, you know, almost than order of magnitude less in energy. And the dirty-litter secret was that the US physics community continually badmouthed the LHC. I mean, it was like, oh, it's useless. There's no reason to believe whatsoever they can discover anything.

The reach just isn't far enough. You've certainly got to go to, you know, whatever was, 12, 15 or even, ultimately 20 TV. And the dirty litter secret is in the context. So now that the LHC is the only game in town and we are involved in it and it discovered the Higgs, all we can do is talk how fantastic it is and what a great idea. And, you know, it's, it's so physicists are also prone to, you know, emotions and politics and all the rest. They're human beings. They're human beings.

So I don't, I'm not, you know, not blaming anyone. But it is, it's, but we sometimes, certainly in those days, I would say, I think the, the physics, the Heinzhi physics community was of course very arrogant because it felt it was doing the most fundamental and therefore the most important science on the planet, but also that we were immune from the human foibles such as this. And I think both of those things have been damaged and destroyed rightfully, I think. I think we were overly arrogant, overly self-involvely. self-centered, it ended with, you know, the calling string theory the theory of everything, which already connotes a certain attitude towards everything else, right? So, you know, and I think

that's been, in a certain sense, that's been healthy for the field, actually, to be honest. Sorry, that that is healthy. No, that meaning the, that this extraordinary intellectual narcissism coupled with arrogance, which I think, might have served us well for quite a long while, not saying that it was necessary, but not questioning yourself does motivate you to try to do things. And especially when you think you're doing the, you know, the most exciting fundamental physics that has universal applicability and is, you know, the meaning of life kind of thing. Yeah, that's right.

And that's very exciting. And that was, you know, mostly unsaid part of the culture. You thought a soft mattress meant comfort, but every morning your back tells a different story. This is the sound of support. The sound of air weave. Born from Japanese innovation, our air fiber technology provides the firm, even support your body has been craving. It's not about sinking in, it's about rising up. Air weave. The aha moment your back has been waiting for. Manufactured in Japan, air weave is truly supportive sleep. Discover more at Airweave

But when the Superconducting Super Collider was cancelled by Congress in the 1990s, so that affected your That certainly affected me as it did, yes. So I meant it affected me for obvious reasons, but it also because I was at Los Alamos at the time and we had some significant involvement in building one of the detectors, with in fact led by Barry Barish. Caltech, your Caltech colleague who of course got the Nobel Prize for the LIGO experiment but we were

heavily involved in that and I was a theorist I wasn't directly heavily involved but that was part of our program but also I go off on a slight tangent here there was another phenomenon so I was in my 50s at the time

and I come from a line of short live males. You know, my father died at 61, my grandfather at 57, and so on back. I mean, we're all, none of us, the male line doesn't live very long, it turns out, and for different reasons, by the way, which is interesting of itself. But so the reason I say that is because I grew up with the idea that. it was exceedingly unlikely, or rather, you expect to die at about each 60.

Wow. And here I was, you know, I don't know what it was, 53, 54, maybe. And I realized, my God, we've had the death of the SSC. I'm 54, 55, whatever it was at the time. And my God, I probably only have five years, maybe stretch it 10 years. And, wow, that's, you know, one, I should start thinking about what I'm going to do, I grow up kind of thing. And now the SSC has gone.

This has a big psychological effect. But another thing was happening. The death of the SSC was driven by, to some extent, by a big anti-science movement in the US, which comes up every once in a while. But in particular, it was an anti-physics one, and even more particular, it was an anti-high-energy physics one. So that it was a very peculiar situation that even our colleagues in other parts of physics were not supportive.

And in fact, they had this mistaken idea that if they killed the SSC, they would all be rich kind of image, which turned out to be completely untrue. To be fair, the SSC would have been very expensive. Of course. And had all that money gone to the rest of physics, they would have been rich. But that never is never what happens. No, it was a humongous. amount of money on the scale of a science experiment, no question. And, you know, there were, there were obviously questions about its viability, what it would actually do, and so on, you know,

all kinds of good questions. But nevertheless, okay, there was this anti-particular anti-Hingy physics. But one of the things I used to hear, both at Los Alamos, which after all is a national lab with huge numbers of activities going from the weapons program all the way through to multiple areas of the, particularly the physical sciences. But I also heard it in the halls of the Department of Energy, which were the funding agency for the SSE, was this famous statement. Physics was the science of the 19th and 20th century's. Biology is the science of the 21st century, and the corollary.

which was usually not said, but I did hear it say, said, was therefore no need to do any more fundamental physics. We know all the physics we need to know. That was, you know. It was said very explicitly. Very explicit. And that was, you know, that really threw me. And so I reacted emotionally to the idea that, well, first of all, on reflection, it was obvious. Biology was going to be a major of science in the 21st century. No one could doubt that. It was clear. It was poised for that. And if your question had been, do we know all the fundamental particle physics we need to do biology and chemistry, then the answer is yes. There are other kinds of physics. Absolutely. No, so string theory or grand unification will have no impact. No impact. So this is one of the,

we should discuss that because I think that's, it remains an issue. You know, why are we doing things that have in any way that we can immediately conceive anyway, any foreseeable influence on betterment of human life, on the planet, making America great again or whatever. In fact, I'd very much like to come back to that. But along more personal lines, I reacted A, to the idea that that's nonsense. you know, that's really crazy that you've got to support all the physics and you've got to have

people think about fundamental questions. If we are to address fundamental questions in biology, you know, that's sort of part of the culture. And more specifically, I reacted by saying that, you know, if biology were a real science, it would be, you know, you'd have equations and principles and you better mathematicize things. I mean, all very well. You know, we all know the principle of natural selection. Darwin's contribution was fantastic. But, you know, except in a limited way,

it doesn't predict the kinds of things we in physics would think you would need to predict if it were a complete theory. You would want a curve that you could fit data to and see how beautiful your predictions. Exactly. And in fact, I used to say very, you know, very sarcastically, you know, if you had a real theory, then you could predict that there should be human beings.

I mean, which is taking it to an extreme. But so I would push back with that at various meetings, you know, and discussions. And then I started to think, you know, my God, maybe I should take that seriously. You know, that biology really, you know, does need, what I would say is biology needs not just the techniques that physics uses, the mathematical techniques, but equally and possibly even more important, it needs to integrate in a way of thinking, a certain culture, which I, and I must tell you, this was out of this arrogance of the Heinzsche physicist and also out of ignorance of biology. So it was really emotionally, you know, I mean.

A perfect storm, really. A perfect storm for disaster. But then I started thinking about it, and I combined these two things. the fact that maybe I was going to die soon. The SSC is dead, and they're telling us that physics should be dead, the kind of physics I'm excited about, and we should all be doing biology. And I thought about that, and I thought, you know, I wonder if biology were a science like physics, there ought to be in a biology book, a calculation,

of why it is that my lifespan, even though I'm only going to live to 60 or 65, I thought at that time, even conceivably I wouldn't live to 100, 100. Where does the 100 years come from? You know, so I started reading biology books. So you worried not about why 60, but why not a second or a millennium? Yes, so where does the scale? Where does the overall scale? That's the physicist's way of thinking, not just the mechanism. What is the mechanism that's leading through aging to mortality? But what sets the scale of aging, of lifespan of the 100 years? So I started reading and I went to the libraries and I had a great time studying the literature

because it was easy to read because it turned out to my amazement that the question of mortality and even aging, is it was then a complete backwater, really, relative to most of the other things that you think about. And a metric of that is the following. I started looking at these big fat biology textbooks that they use in undergraduate biology classes. They cover all of biology. And, you know, they were very useful, by the way,

in my own education, just obviously learning things that I had. not been educated about. But I'd look in those and I quickly discovered, I must have had at least half a dozen, maybe more, and I discovered, sure, there's a chapter on metabolism, a chapter on growth, there's a chapter on reproduction, on genes, on even ecology and so on, nothing on mortality or aging. You could look in the index. Most of them wouldn't even mention it. And I thought, this is extraordinary. You know, when you think about it, it's the second most important event in an organism's life.

It's birth and death. And there's nothing about death. I was absolutely shocked at that. And then I discovered, as I say, reading that, and it was fairly easy to read the literature because there was not much technical stuff having been done. It was stuff that you only had to know

sort of freshman biology to be able to follow it and there was nothing quantitative, almost nothing. I mean, there were survival curves and so on, but... I remember I invited a biologist, Bonnie Bassler, to give a colloquium

in the Caltech physics department and she talked about bacteria and quorum sensing. And the students were blown away because, you know, they were pounding, our graduate students in physics were pounding their heads against these problems that they really needed to work hard just to ask a question that we didn't know the answer to.

And in biology, like, you don't know, the answer to half a dozen questions that you think up over the afternoon, right? Sure, exactly. So it was quite different. So anyway, I started in the evening, so to speak, or when I was stuck on my physics I was doing, which was very easy to be stuck on because I was still doing strings theory, or being slightly depressed about the SSC,

I would work on this problem. I'd think about it. And I read, as I said, I read a lot of the literature. And I started thinking about it. And that got me, and one of the things I quickly discovered in the literature were these remarkable scaling laws. And because in looking at the literature for longevity, I learned that there was an approximate scaling law for longevity for longevity as a function of size, the function of the mass of an organism. And it was a relatively simple one,

even though there was a lot of variation in the data, a lot of fluk noise in the data. Sure, of course. But how should we think about what a scaling law is? Yeah, so let's talk about that, because I want to, that's a good point of departure. So a scaling law, I mean, in its most simple form, is, you know, you take any system and you ask,

what happens if I increase all of its lengths by a certain parameter? double its size, triple its size, what happens. So the simplest obviously is if you take a rectangle and you double the size of each side, the area doesn't double, it goes up by a factor of two times two. It's fours. That's very trivial, and the volume of a similar object goes up by a factor of eight, two times two times two. So that's the simplest form of scaling, scaling law. It's in one case a square and the other case a cube. So, um, it's a So generalizing that, one can then ask about, let's just go immediately to biology, if I measure some characteristic of an organism, and it could be something as fundamental, which is what I want to get into, as metabolic rate, how much energy, how much food, if you like, does it need each day to stay alive? How did the 2,000 food calories, roughly speaking, that we need, how does that scale from the small?

mammal, let's just stay with mammals for the moment, the smallest mammal to the blue whale. How does it, you know, is there, is there some regularity? So that was the thing that I also learned about that these scaling laws, and in particular the scaling law for metabolic rate, had already been discovered, people had collected, well, a man named Max Kleiber originally, had collected this in the 30s, and discovered an extraordinary regularity, that it was incredibly simple scaling law, which we call a power law, which I'll say it in English, and then I'll say, I'll translate it. So the scaling law is, as a function of mass, it goes to the three-quarters power of its mass,

which means in English. What goes as the three-fews? I'm sorry, the metabolic rate. The metabolic rate. How much your heart rate? Well, the meta, no, just how much food you need each day. Let's stay with that first. That scales with the three-quarters power of mass.

That's the way you say mathematically. Three-quarters power of mass means that you cube the mass, that's the three, and the quarter means you take the square root twice. So it's a very bizarre law from that viewpoint. Well, at least it's saying that bigger things need more food, but less per gram. Less per gram. and systematically. So the thing that surprised me

was that it was so simple, mathematically simple, and also that there was a law anyway, that any law should exist because we have this notion that natural selection invokes the idea of random processes within an environment

and that any characteristic of an organism, whatever it is, including the organism itself, but also the nature of its cells, the nature of its genes, have a unique historical history. I mean, they're historically contingent. It depends on what's happened in the past. I mean, biologists love to show us the diversity of life. Absolutely.

So that's their big thing. That's Darwin. That's their big thing. And that's why they often argue physics and mathematics has actually nothing to do with biology because there aren't any simple things like that. And here was a law about maybe the most fundamental quantity of biology. How much food do you need to stay alive?

And it was showing extreme regularity. Whereas, as I say from a naive natural selection viewpoint, you would have expected, if you plot it on a graph, metabolic rate on the vertical axis versus size on the horizontal axis, the points would be sort of all over the graph reflecting the historical contingency of an evolutionary history of each organism. Yeah, why shouldn't two mammals of the same body mass have very different metabolisms because they had different mechanisms that evolution...

Exactly. So this was pretty amazing to me, but what was even more amazing was that if you looked at any physiological characteristic that could be measured or any life history events such as how long you live how long you take to mature the rate of which you grow but all these kinds of things they also had a very similar very simple regular behavior and in as i say just to emphasize again in marked contrast to the naivete that we have about natural selection furthermore and this was critical from a physicist viewpoint, the exponent, the three quarters in metabolic rate was repeated across all of these different characteristics.

So. For totally different quantities. For different quantities. Some as mundane, if you like, as heart rate. Some as, you know, as profound really as lifespan that we talked about earlier. but, you know, things like, you know, the length of your aorta, something like that, you know, or the number of leaves on a tree as a function. But so I learned, and I was very fortunate at the time, that a few years earlier,

four books had come out of pretty much the same time, summarizing all of this. So there must be anywhere from 50,000. 50 to 100 of such scaling laws, depending on what you want to call them. And it had been, before the Second World War, it had been an area, because they were discovered in the 30s, it was an area of significant concerns. So many famous biologists had spent time thinking about it. Huxley and Darcy Thompson and JBS Haldane and so on all, some of the big names in

in mid-century biology. But with the late 40s and 50s came the molecular revolution and the discovery of DNA and the double helix. And that completely changed everything. And these questions which are to do with

multicellular organisms, actually they're not just to do with multi-cellular organisms, but to do with sort of more macroscopic behavior. Everyday life scale kind of behavior. We're sort of swept under the rug and forgotten. And everything turned to molecules and genes, in particular genes,

so that that was the paradigm we should be thinking about biology. And in retrospect, it sort of parallels a little bit physics. That, you know, in my years as a hygiene physicist, I think that part of that culture was that, look, all we need to know is the fundamental laws, and that's why the theory of everything was so important, because once you've got the fundamental laws, we can calculate anything and everything, including biology. You know, all it is in some weird way is turning the crank from these equations,

and out will pop all these marvelous things. And I don't think that very many people said that explicitly, Nor would they even have defended it if you asked, but there was some sort of attitude that underlay what was important versus what was sort of a waste of time. Exactly. So, exactly. I don't think anybody ever said it. And you're exactly right.

If anyone had said that, people would have said, well, yes, of course, it's very important. Those fundamental laws are crucial and important and they will lead to all this. But it's true, you will need to develop other. the techniques. You know, it would have been, there would have been a very soft version. Yeah. But it was still there and it was in the culture.

And also in the culture was, in a certain sense, again, I did actually hear it stated, but it was not very common, was all the rest of those things were engineering. Engineering, yes. You know, as if that was pejorative, by the way. Yes, as if it were pejorative. And it was very much echoed, you know,

goes back a long way, of course, to the famous statement of Lord Rutherford, you know, the discoverer of the atom, you know, the famous one, all science can be divided into two physics and stamp collecting. Stamp collecting, yes. And I was always slightly embarrassed by that, and then one of my Caltech colleagues has a poster with that quote on his wall. Is that right? He's very proud, right?

So this was part of it. So we know it's there. And but anyway, so going back to the molecular revolution, and the genomics, what it had evolved was a similar kind of arrogance, I think, has evolved, that that is everything, so that we ended up, you know,

not so long ago with this idea that, you know, it was fantastic, we should map the human genome, which is phenomenal, and it's a fantastic achievement. But the hype about it was, once we've done that, we sort of solved everything.

Just engineering. engineering after that. You know, we'll have personalized medicine and all diseases and all syndromes are going to be understood and solved and so on. I mean, we're at the Santa Fe Institute, and one of my first visits to the Santa Fe Institute was by Leroy Hood, who was one of the initiators of the genome project, and he was here talking about it. And I listened to it was a very good talk. but he said this very explicitly. And I was amazed.

And he was giving that talk because he wanted to get computer scientists and people think about companies involved in doing the engineering part. Anyway, and I thought at the time, my God, this is really extreme. But we had that. So this area had migrated, this area to do with organisms in particular, and the more macroscopic thinking had migrated into ecology. Right.

So ecology and evolutionary, and what's called evolutionary biology, that became the home for some of this stuff. And I got involved because I had started working on this, as I said, on my own. And as I thought about it, I'd learned about and discovered, I discovered by reading.

I learned about all these marvelous scaling laws, and I said, this is unbelievable. I can't believe that there isn't a theory out there, that the biologists and that it's all been solved, and this is now, you know, a whole area, and I discovered there wasn't any. It had just stopped, literally stopped in its tracks. So I thought this is a marvelous problem to work on now in the context of,

does physics have any relevance from biology? Can we have a theory that would help explain this a phenomenon that has a curve and you fit the data? Yes. Can we derive, can we construct a principled theory from which these laws can be derived and we can also make further predictions? And just so the people in the audience have the direction which these things go, you've mentioned that larger animals live life at a slower pace.

Yes, so let me elaborate because I just said the scaling laws. So let me give you some other examples. So heart rates, for example, decrease with size, but they decrease in a very regular fashion so that it is that you, I said in the metabolic rate is you take the, you cube it and then take the square root twice. For heart rates, they decrease according to taking the square root twice. That's it. The minus one-fourth power.

The minus one-fourth in mathematical language. And so it is with many other things. I mean, the radius of your aorta scales with one that is the three-eighths is the analog to the one quarter. But you see the eighth has a four in there. You don't get five-sevenths. You don't exactly. So this number four permeated all of these scaling laws. So I thought this is fantastic for physicists.

There's a true universality. And that's what physicists concern themselves about at the deepest level. Are there systematic kinds of phenomena, and do they have kind of what we call universal behavior? And are the universal characteristics? And here was one saying something astonishing that this unbelievably complex and diverse phenomenon called life around us is constrained by the number four. Yeah. I mean, I thought, this is like magic.

Where in the hell does this number come from? So that was the state of mind I was in when I started thinking about it. And I did, and the first thing you think about is what is common. I mean, you realize that whatever the underlying mechanism is, it must transcend the evolved design because it's true for plants, trees, it's true for mammals, it's true for birds, insects, and so forth. In other words, there's some happy place

for an organism to live, and evolution gets you there. It can take different paths, because this is where you're allowed. Exactly, exactly. It can take many different paths, and it gets manifested in different ways, in the sense that, you know, we have a beating heart

and a respiratory system, a tree doesn't. In fact, we are a bunch of tubes. You know, we're like plumbing. But a tree is a bunch of fiber bundles joined together and sprays out like an electrical cable sprays out. So, you know, these are quite different engineered designs, but they satisfy the same scaling laws.

So you ask yourself, something has to transcend that. And then, you know, it doesn't take very long to think it through and realize. that look, the huge problem that an organism has is that it's made up of an enormous number of components, particularly cells. We have 10 trillion, 10 to the 14th. And they have to be sustained and serviced in a roughly speaking democratic and efficient fashion. And it's obvious what has happened. We've developed networks. These modern and

If you think in those terms, you realize you are a bunch of networks. Everything from your, as I said, circuitry, respiratory systems, your renal system, even your bones are a big network. A nervous system. Your nervous system, your neural system, the very thing that you think of as you, the white and gray matter in your brain, those are also branching systems. The synapses and neurons and axons are all networks. So, sorry, a network, of course, is not a particular TV station or a collection of TV stations.

You're thinking of a picture of, so I could draw up some dots on a whiteboard or a piece of paper and connect them with lines and that would be a network. That would be a network. You're thinking of a particular kind of hierarchical. Yes, most of these networks that you, it's not absolute, but most of these networks are hierarchical, like your circuitry system. There's a beating heart. There's an aorta that comes down, out, and then it branches and continues to branch all the way down, serving your organs, all the way down to capillaries that feed cells.

So it's this branching network. And, you know, most of the dominant networks in our bodies have that kind of characteristic. So I sort of had this idea that it must be some universal features of networks. there must be, well, in the language of physics, is a universality class of networks that have this feature that have somewhere in them,

this one quarter, this four. They know about the number four. They know somehow. What could that be? So what could that be, indeed. So what I did first was simply try to solve the question of how does our circular system work? Just to take that as a physics problem.

Simple warm-up problem. a warm-up. It turned out to be quite complicated. Who would have guessed? Who would have guessed? And it was a wonderful mathematical physics challenge, which I was quite frustrating at times, but I enjoyed immensely. I could use all of the classical mathematical methods that I'd been taught as a graduate student and I'd used throughout my career. And they all came to bear on this. and I eventually solved it. But I then, in solving it, I had to think,

what is the principle? What are the principles that are constraining this network? Sorry, just by solving it, you mean constructing a mathematical model that matched? The matched, I'm sorry, the matched, yes. What we know about our circuitry system. What a physicist means by solving a system, right? That's what, I'm sorry.

Yes, so I have mathematical equations. So you have to write the mathematical equations for the blood flow through your vessels. And that's complicated because it's a pulsatile flow. It's being beaten by the heart. It's being boom, boom, boom. And not only that, it goes through a vessel and then that vessel branches. So some goes down one tube and one down the other. And so you've got to deal with that.

And then it bifurcates again or trificates even. So, you know, you had to deal with all that. And so I did all that mathematics, and there's all kinds of formula you derive for that. But you have to constrain it. It's not some arbitrary network, and that's what I realized quite early on. And it turned out I put together three generic principles, but only in the context of circular systems to begin with. And then I realized later these were generalizable to all these networks, and they were the following. The first is the network has to be what we call space filling.

It has to go everywhere. Every cell in the body has to be supplied by blood. Has to be supplied by oxygen, of course. Blood, oxygen, yeah. So therefore the terminal unit, the capri, has to end near the cell so it can be fed. So that's called space filling. And we know how many cells there are, they fill the body. Sure, sure.

Second was, which was something to do with natural selection, as I thought about the scaling laws and our relationship to other animals, other mammals. And that is that, yes, we look quite different, but we obviously have a lot in common. But in particular, as different mammals evolved, natural selection kept certain things invariant. It had certain building blocks. It did not reinvent cells in order to make a dog. as distinct from an elephant. It kept those same building blocks.

So in terms of the network, the cells were essentially the same, but also were things like capuaries, the end of the network. You don't, you know, that's the thing that feeds the cells. So I... Well, natural selection is naturally very lazy, right? It takes what you already have and says, how can I improve things a little bit? Exactly.

So you don't reinvent things, April or I, every time, and you're very possemonious in that. So that was the second idea, the kind of invariance of terminal units. And the last was taking a real physicist viewpoint. And that is that something is being optimized. You know, physics operates almost entirely at almost all levels, particularly at a fundamental level, from optimization principles. You know, we, that's all our fundamental equations of motion are derivable from optimizing things we call an action.

If a beam of light goes from one point to another, it takes the shortest time. So this is fundamental to physics. And so I postulated, hypothesized that the, our circular results, system in particular has evolved so as to minimize the amount of energy our hearts have to do to pump blood through it. And I didn't have it quite formulated at that time, but I'll jump ahead. It was only later when I started a very intense collaboration with biologists and natural selection is what they think about all the time. And it was really this idea that the real reason for that from a natural selection viewpoint is that you minimize the amount of energy that is needed for the

organism to stay alive, to live, so that you can maximize the amount of energy you devote to reproduction and to raising of offspring. So to your Darwinian fitness, you want to maximize you maximize Darwinian fitness by minimizing the amount of energy that you need to support and sustain the system. So this was one very specific case, going back now to the circuitory system, that you minimize the amount of energy our hearts have to do to pump blood through it. And so we have all these equations. So that's it. You have three principles? Three principles. And you have the, you know the dynamic. and then it's a matter of solving those equations with those constraints.

And it's very similar to what we were doing in field theory in high-ngy physics. It's the same conceptual framework, that is. Anyway, out of that, popped to my absolute delight and amazement this one-quarter power. Now, I have to interrupt that, make it sounds. very straightforward, but it wasn't. I struggled a great deal, first of all, trying to solve them and understand the biology and so on. And a serendipitous event occurred, and that was that I got a call from the vice president of the Santa Fe Institute, who said, who called me up to say, Jeff, there's a biologist, very well-known biologist that is involved with the Santa Fe Institute,

and he is very interested in getting a physicist involved in trying to understand scaling laws in biology. And I said, I can't believe this. I said, I can't believe, I've actually, the last almost year, you know, as a kind of hobby, I've been working on that and I think I'm close to solving it. But I need a, it's fantastic, I need a biologist. He may need a physicist, I need a biologist. So that's got a very long story short. That began an extraordinary collaboration with a man named Jim Brown,

who had moved recently to the University of New Mexico and was involved with the Santa Fe Institute. And he had a marvelous student named Brian Inquist, who is himself now well-known. ecologist and a fantastic collaboration developed where we got all this straight and it took us almost a year from then to get everything straight and it was a huge commitment by the way because I was running Heinzsche physics at Los Alamos right still you have a day job I still had a day job I was running I was involved with the theory group and we were running we were still involved with many

the experiments. And I was the spokesperson or the connection with the DOE anyway. And Jim ran a big ecology lab out in the field and so on. And we both agreed to devote Fridays to this at the set of finish. And it was huge. It was an enormous commitment. That came after a few meetings. And we did. And it was, it, and it required that actually. So we would meet, we would meet here at the Institute, you know, between 9 and 10 on a Friday morning, and they would leave here about two or three in the afternoon. And we would, the three of us at the beginning, we'd just be at the blackboard. A lot of it was blackboard, and a lot of it was just bullshit, you know, going back and forth because I didn't know the biology. And how shall I put it? They were

mathematically challenged. So I had to take, you know, really things that we take so much for granted and really work hard at explaining them. And they were fantastic at explaining in very simple terms, biological mechanisms and and also very importantly, what is important. Because that in one's education, that is something we don't often recognize is having a mentor who really guides you. It's not actually teaching you. But what is important and this is the way you should be thinking about.

this. And to those out there who are not professional scientists, we should point out this idea of standing at the Blackboard and discussing things and learning things and having the feeling you're making real progress, it doesn't get better than that. No, absolutely. Thank you, Sean. Honestly, it was fantastic. And it doesn't get better that in a very curious way. Your ideas are going back and forth and you're writing equations and then you realize they're completely wrong, the idea's wrong, you're going to get to press, and you get to or many times you're depressed or you sit there, you know, and certainly with this, I would sit down and think, why am I involved with these guys? They can't, they, you know, they're not able to write an

equation and I'm, you know, and I'm sure they felt the same way about me. So there's all of that, but that's part of it. And I've often said, I know that it's sort of like a marriage, a good marriage. Yeah. Because that all comes with it, you know. And so it was a phenomenal period for me. And that collaboration lasted for about 15 years. And, of course, it evolved. We got postdocs and other people joined us and other senior people joined us. You know, we had a chemist and joined us, another ecologist. We had students from physics, students from biology.

It was marvelous, actually. And we, you know, created this body of work. Right. But now I don't want to switch topics to abruptly. but it wasn't too long before you went from explaining all the scaling laws in biology to saying, well, once I have my networks. Oh, actually, I'm sorry, we didn't say where the four came from, did we? No, so I should tell me.

So it is from the networks, but to put it in, you know, sort of simple layman's name, you have to do all the calculations and you have to do all. But it is roughly what the four turns out to be is three, the three of the four is a reflective. of the fact that we live in three-dimensional space. The space-filling that I talked about. If we were six-dimensional, if we lived in six dimensions, it would be that three would have been six.

And then there's a plus one. And that, so four is actually three plus one. And the plus one... I knew that. Very loosely speaking comes from the following. Those principles, and in particular the optimization, lead to these networks being fractal-like, self-similar. I mean, that is they repeat themselves as one goes down through the network.

The bird's eye view looks very similar to the... Exactly. Or if you cut a big branch of a tree and you take it away, it looks like a little tree. And that so-called self-similarity you can derive. and that is fed by the optimization. This optimization kind of a constraint leads to that. And it turns out one of the curious properties of fractal geometry is it increases

what we think of the dimensionality of the system. And you can increase it and it increases it maximally and that maximality happens to be one. You can't go more than one. And so biological organism, natural selection, has taken advantage of fractal geometry to increase the dimensionality and increase its efficiency and its way of interfacing with whatever the external environment is. Otherwise, large mammals would need enormously more caloric input every day to get through the day.

But these networks solve the problem of, With maximum efficiency, getting the oxygen to ourselves. Of it. On the average. Exactly. Exactly. So we are all manifestations of that. And there's networks all over the place. There's networks all over the place. And indeed, somewhere along the line, it started to occur to me that, you know, this is a paradigm for other problems. And in particular, social organizations, and in even more particular, to cities. And, of course, there's been a long history of thinking of cities as,

kind of super organisms, and we often use metaphors from biology, metabolism of city, DNA of a company, and so on. And so I was, I started at least thinking about it, and I thought, well, but again, a serendipitous event came along. Okay. And that is I was here at the Santa Fe Institute. I mean, I was not part of the Santa Fe Institute, but I was here on this once-a-week basis. And I gave a colloquium one time on my biology work. And in the audience were a couple of very good social scientists who came to me afterwards and said, you know, we're so intrigued by this. I'm sure this we can use this to start understanding things about cities and other social organizations.

And to cut a very long story short, we formed a new collaboration. You're giving us the impression that all great science happens by accident because you just happen in a way. Mine does in a way. Mine does and I don't know I feel it happens accident. I don't know if it's accidents but I often think that any new idea that I've had I often feel has come as not to do with me. I know this sounds weird but it's not much to do with me. But I don't think it does sound weird because one of your discoveries about cities is that they sort of provide the environment for these things to happen by letting the interactions. Exactly. And I think that is what is happening to all of us. And it's sort of

sort of the unconscious part of the city. Yeah. And it is, so I often say the, you know, I even said, and others I think have probably said it, in many ways the city is our greatest invention because it's a machine that brings us together and facilitates and enhances social interaction and provides positive feedback mechanisms for enhancing that to create ideas, to innovate and to create wealth. And, you know, I mean, it's certainly true that most of those ideas and most of those conversations and interactions that take place don't lead anywhere or there to do with personal life. I mean, they're not. But amazingly, that phenomenon occasionally leads to the theory of relativity, or to Google, or to, you know, Microsoft or to the same thing, whatever. But that's what a city is. It is that machine for doing that.

A friend of mine, a theoretical physicist, was, you know, we were having a late night conversation, and he says the following. He said, if you divide human history into the most recent 10,000 years and all the years before that, there were a lot more people in prehistory, right? There were a lot more people older, longer than 10,000 years ago. But all the good ideas seem to be in the recent 10,000 years. And we were wondering why that was. And there were hypotheses like, well, they were too busy hunting and gathering. And in fact, the data are that hunter-gatherers have a lot of leisure.

It's a time, yes. So in fact, things like the city, density. No, the city is the mechanism by which we do this. So anyway, we formed a collaboration. And unlike the biology, where I was extremely fortunate that the scaling laws had already been put together, develop, you know, the phenomenology, so to speak. had been organized in these four books that I mentioned earlier that had been published. By the way, I didn't say because these people who were quite senior people had worked on it earlier. This was the end of their careers.

Molecular revolution, genetics had taken over, and they were kind of summarizing. And the amazing thing about those, that's when I discovered that there was no theory. They just showed all this stuff. Anyway, the cities weren't not in that position. What I learned was that, surprisingly, no one had looked at the scaling of cities. So you want to know about how things like presumably energy use or roads or something like that change as the city gets bigger and bigger?

Exactly. So you could ask, I think the very first question we asked when this was still an incipient collaboration, It was with a man that's now a very well-known sort of social physicist. I don't know what you call them these days. It works in social organizations, came out of physics. And in Doug Hal being at the ETH in Zurich and a student. But we worked together first.

Was how does the number of gas stations scale with city size? It's a very mundane kind of question. But one would guess the number of people's proportional to the number of gas stations. That's what you would have thought, naively. Yes, no need gas. So when we looked at the data, we discovered amazingly that it looked just like biology. It was that it scaled in a very systematic way following this same mathematical power law scaling. But the exponent, the analog to the three quarters, the quarter powers I mentioned earlier, that was not a quarter power.

it was 0.85, meaning, to put it in English, that if you doubled the size of a city, instead of needing twice as many gas stations, very roughly speaking, you only need 85%. So there's this marvelous economy of scale, the bigger the city. And the original paper only looked at four European countries, and they all expressed the same scaling law. And somewhat later, when the collaboration was more formed, more formalized, and in particular, two people brought on board. One was a physicist, a very good physicist named Louis Bettencourt, who had been working,

came out again, came out of nuclear physics, actually. And a man named Jose Lobo, who was, had been associated with the Santa Fe Institute, was an urban economist. He was at, I think, Cornell at the time. But they were very good data analysts. And one of the things they did was they looked at data from around the globe, just first gas stations, and discovered the same scaling law everywhere. But even more amazing was that if you looked at any infrastructure, at least the ones we could get data for, like length of all the roads, length of electrical lines, water lines, blah, blah, blah. They all had the same scaling with this 15%, this 0.85. And that was sort of amazing. And it was just like biology,

except 0.85 instead of 0.75 kind of thing. Is that because cities are not truly three-dimensional space filling? Well, we wondered about it, yes. And in fact, I think that, yes, from that viewpoint, the answer is yes, the dimensionality plays a crucial role. and ultimately does, but I should say right up front now, I would say, oh, so let me back off a second. So the obvious thing is, again, even though, you know, New York doesn't look like Los Angeles, which doesn't look like Chicago, which doesn't look like Santa Fe, they're all network systems and they're all doing the same thing.

So just as the whale doesn't look much like an elephant, which doesn't look much like a human being, they're actually doing all the same things biologically. And so in that sense, not surprising their scaled versions of each other. So it is that cities within a given urban system are indeed scaled versions of one another, but following in terms of the infrastructure, similar laws as biology,

but with a exponent of 0.85, as you say, to do somewhat with their dimensionality. They're more two-dimensional, obviously. but also their network systems. That was the other point is clear, is that the light biology, their networks. They're road networks, electrical networks. Are they optimizing some equivalent of the heart grid?

What are they optimizing? And how much are they optimized? So in terms of those networks, I believe that in many ways, those networks are optimizing things like time to get from A to B and distances. You want to supply things as directly as possible, but you've still got to do it in a network way because you've got to supply. You know, it's a...

But is it safe to say we don't yet have the development of theory for cities that we have? So we don't. So one of the things, if we can make certain generic assumptions that are quite analogous to the ones in biology, but also ones to do with something that I haven't mentioned yet, which distinguishes social systems and in particular cities from biology, and that is social networks. This is the most important thing and is to do with what we talked about before

that cities are there to enhance social networking and it is the structure of social networks that are being reflected in the socioeconomic quantities. And if you measure socioeconomic quantities, we find something we don't see in biology and that is a reflection of this positive feedback loop that occurs in social interactions and that is that...

The bigger we are... Your social network is calling. It is indeed. The bigger we are, the bigger we are, the more we have per capita, rather than the bigger we are, the less per capita, which dominates biology. So our heart rate slows down as we have more mass. But I just love the fact that as we move into the city, the pace of life increases.

We literally walk faster. Exactly. So everything goes backwards in biology. Yeah. The instead of, as I said, the bigger you are, the less per capita, the bigger you are, the more per capita. The higher the wages per capita. More costly, the rent. And the rent, obviously, all these things, the higher, the amount of construction per capita goes up. The number of patents produced per capita, the innovation of a city increases.

The amount of crime increases per capita. The amount, the number of AIDS cases, increases per capita, all following the same systematic scaling law of an increase by about 15% with every doubling. So it follows again the same mathematical power law scaling. The exponent now is now bigger than one. It's 1.15, that's the 15%. And that's called superlinear. Is it possible that the physical proximity afforded by cities will someday be replaced by networks that are virtual? That are online? Well, that's a very interesting question.

I don't think replaced. So my, I used to, when I first did this work, I thought exactly along those lines. And then I thought, you know, we've already had that because the invention of the telephone already did that. It already made something virtual. Even though we know their lines going, we've gone one step further than that. of course, but you know, what it did was that it contracted time. I mean, up until that point, you could only have an interaction with no time delay if you were face-to-face. Otherwise, you had to

wait hours, days, and in some case months. And suddenly, you could have the equivalent to face-to-face right there in your home. And that had a profound. influence. So I think bigger than the IT revolution, which has taken it truly virtual because it's out there in cyberspace and so forth. And so I would say that the IT revolution has done what the telephone, and by the way, things like the steam engine did. Because by the way, the railroad did something sort of like the telephone. It contracted space. Shrunk the world.

Shrunk the world because until the railroad, most people didn't move more than 10 to 20 miles from their home in their entire life. Didn't I read from you that no matter what the transportation system is, people have an average commute time of an hour? About now. Yes. It's a standard number that urban geographers use.

Is that half an hour each way? Yes, half an hour each way. Yeah, I bet that that actually works. It's been expanded to an hour, you know, now in some places. But, yeah, so the size of a city, it's been suggested, the traditional size of a city, typically, this is very hand-waving, is given by how far you can move in actually now one hour by whatever the transport is. So it just expanded with the transportation.

And now you're not shy about having policy recommendations. on the basis of this point of view, right? I mean, we're making choices about how to live, how to govern ourselves, what to do for our infrastructure. What should we learn from these insights? Well, I have been, no, I have actually been quite, I interact quite a lot with the, you know, policy makers and the urban geographers and urban economists.

But I'm very loathe to make explicit, policy decisions or recommendations other than the following. This I'm passionate about. The urban planning in particular has a very checkered history. It is operated urban planning, both in terms of building new developments in enlarging cities, mitigating problems, building new cities. roughly speaking is work by rule of thumb. This is what we've done in the past. This is what we do. It's very truly phenomenal logical.

Not what would make a theoretical business. So it worked, you know, this is where we did it. And I've learned that, not just from reading the literature, but I've given talks at big conferences, and people have come up to me and said, listen, I run a company and we're building a small town of 15,000 people. and I had no idea any of this existed and we just, you know, the way we'd be, well, you know, we should have a park here.

I mean, I'm exaggerating a little bit, but that's what it is. And it's very much rules of thumb. And it is no accident that almost all synthetic cities have been failures. Brasilia, Canberra, Washington, D.C., it's taken a long time from Washington, D.C., become a real city and so on. Islamabad, they've all been essential disasters. And we're seeing it repeated tragically, I think, in China, because China has this pressure to build several hundred new cities in the next 25 years. And, you know, they're just building them willy-nilly,

just buildings and soulless, and it's going to have horrible repercussions. And it's already, actually. Well, I'm very sad because we are running out of time, but let's circle back to where we started, because you mentioned this question of, I don't know whether it's a responsibility or at least the question of how scientists relate to the usefulness of what they do. Some of us, myself included, have rarely done a piece of science that directly improves the life of anyone but me and my friends and other fans of theoretical physics and cosmology. Other scientists, of course, have changed the world. dramatic ways. How should we think about this? Yes, so I, of course, like you, most of my career, as I said, was in high-ngy physics, and I'd never done a practical thing in my life. However, and I had a faith, and it is a faith, that, you know, understanding more and more deeply eventually has profound implications not just for, you know, because of, for its own sake, but actually propagates through and in some way we don't fully understand,

but in some diffusive way actually influences the world we live in. And so let me give you an example that influenced me tremendously in my, when I was a graduate student. So when I was a graduate student, I was, I'm a terrible experimentalist. It's terrible. I almost failed every time, both as an undergraduate and graduate student getting a degree because I couldn't do the bloody experiments. But I always felt, one thing I did feel was that physics was sort of at some level in experimental science, because that's its power that you can not only observe, but you can test. And, you know, there's an iterative scientific methodology. And so I thought, you know, it would be good to go into a lab and really see how it all works.

And at that time, I was a graduate student at Stanford, and my name Arthur Shalow had joined the faculty. He'd been at Bell Labs and was the co-inventor of the Laser, got the Nobel Prize for it a little bit later. And he was just setting up a lab, and I was his third graduate student, and I joined him, and I told him exactly what I just said. But I'd like to do it and see him. I said, maybe I'll stay and be with him.

Anyway, I was a terrible experimentalist, and I did. And art, by the way, was a terrible theorist, And so it was very good because I was sort of the house theorist and I would explain, you know, papers, theory papers to him. So we had a nice symbiotic relationship. But one day at night in the lab, we had, Art had in his lab, the biggest, we had this biggest laser in the world, actually. And I was talking to him about being a scientist, I want to be a scientist. And I said, but I want to do hygiene physics and it's so useless. And you've done, you know, you were Bell Labs at least.

And I said like this, now you're doing work that is obviously useless. I said, you know, I mean, you know, this has got no do with anything, obviously. It's marvelous. It's marvelous atomic physics. You're resurrecting atomic physics from the ashes. And it's marvelous. But it's obviously got nothing to do with anything. And he said, Jeffrey, you're completely wrong.

He said, this is going to revolutionize the world because he said, you know, we always show when we have visitors to the land, we take this big block of wood, we fire the laser, and we drill a hole through it. Do you realize that we're going to have lasers powerful enough that we're able to drill holes through steel? Not only that, we're able to cut steel to extraordinary accuracy. And this is going to revolutionize the entire planet. he was completely wrong. It did not revolutionize the planet. It did revolutionize a teeny bit of industry, of course. And it's allowed laser surgery.

But it did not. He was wrong. But he was also right that it did something, inventing the laser revolutionized in a serendipitous, totally non-predictable way. And that is the nature, of course, of basic research. We have no idea. You know, who in the hell knows?

It could be, well, I don't want to say, even speculate. But it does. So that influenced me tremendously. I mean, I'd like to think that this influence propagates forward, but I do worry that I just have a motivation for thinking that. But then the second event. And that was, much

later, so I was at a postdoc at Cornell, and I got to know a man named Robert Wilson, who was an extraordinary man, and he became director of Fermilab, which was that period, you know, the biggest accelerator, before we

had the LHC and so on. And he had to go through some rough times in Congress being attacked for exactly this question. And there is a famous occasion when he was being strongly attacked about the role of this kind of research. And the question came from a senator saying, you know, Mr. Wilson, this is all wonderful stuff. You know, it's marvelous to know these laws. But what has it got to do with anything? For example, he said, what has he got to do with the defense of this country? You know, it's coming from the Department of Energy where we make bombs and so on.

What the hell does learning about these elementary particles and the symmetries and all the things you've told us about got to do with the defense of the country? And Bob Wilson said, Senator, I have absolutely no idea how this will help the defense of the United States, but it will make the United States worth defending. And there was spontaneous after all. And that is, you know, that's the spirit in which we do this. I think that's right.

I think, as you know as well as anyone else, average human being has three billion heartbeats. And it's about what we're going to do with them. You know, it's fun to try to extend our lives, but also we're living our lives and discovering new things about the universe. It's one of the best ways we can. And that's what being a human being is. think that is the highest level to which we should all attain, actually, we should all try to

aspire to in somewhere or another. And, you know, science has that characteristic. And working on problems that seem to be, have no practical, perceived influence on the world around us is, you know, is one of the things that should be supported. And one of the sad things that that has happened in the intervening years is more and more the funding agencies are focusing on what have you done for me lately and how is this gonna help immediately. Well, hopefully our little podcast can do a tiny bit.

Well, I hope so, because I mean it's ironic that I've ended up working on things that could be perceived of being very practical. And I hope have us again, but with a physicist big picture worldview of thinking about these, systems. Well, Jeffrey West, thank you so much for being on the podcast. Thank you, Sean, for having me. Thanks for the conversation.