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Ep #12 Transcript | Geoffrey West: Organisms, Cities, Companies & the Science of Scale

Full episode transcript below. Beware of typos!

Nick Jikomes 0:27

Geoffrey West, thank you for joining me. Thanks,

Geoffrey West 2:23

Nick. Pleasure to be on your show, so to speak, and thanks for inviting me.

Nick Jikomes 2:29

Yeah, it. It's a pleasure. I was really excited that I was able to get a hold of you. You wrote a book called scale A number of years ago, and I read it Oh, I don't know, perhaps three years ago now. And I don't even remember how I found it. It was serendipitous, I think. And I'm glad I found it because it was just, it was so eye opening and delightful to read. And I want to get into all the stuff in the book. I don't want to give away too much at the beginning. But it was one of those books where it almost, especially in the beginning, there was many seemingly ordinary, and you might even say mundane questions that everyone, you know, at least crosses their mind that they don't actually have answers to, why do we grow? Why do we die? Why do elephants and whales live longer than mice and birds? All of these seemingly ordinary questions that actually have really deep and exciting answers, I think. And so I want to start off by talking about organisms and metabolism and size and all of the things you get into in the book. And I want to start by asking a question that was related to a story that's in that first part of the book to get us talking about metabolism and animal size. So the question is, if for some reason you had to give an elephant a dose of the drug, LSD? How would you give it the proper dose?

Geoffrey West 3:51

Yes. Good. Good question, of course. And indeed, it is a story in the book, an anecdote in the book, a true story. And maybe I should tell the story first so that people know the context. But so this was some work done, I think was around the year 1960, when LSD was just beginning to sort of come to the consciousness of people in pharmaceuticals and in psychiatry and so on. And everybody knew it had this very strong effect on mostly that time was actually on cats but also even a little bit on human beings. But the some people I forget where it was exactly and UCLA that's why the UCLA were very interested had gotten interested in LSD as a, as a therapeutic drug. And for various reasons, they decided that it would be Interesting to see what effect it would have on a very large animal. And they hooked up with someone in the zoo in Norman no in Oklahoma City. And they got permission to give an elephant whose name was tosco a dose of LSD. And it brings up exactly the question you just asked how much LSD you give an elephant. So what was known at that time was they say mostly about cats. And so what they did, which may be is the most naive thing you would do is simply take the weight of an elephant, divided by the weight of the cat, and multiplied by the dosage of a cat. That came out to be several 100 milligrams of LSD. And they injected into parola Tesco. And within an hour, well, very quickly, he sort of went completely dysfunctional, and trumpeting, and all the rest and within an hour died. And the conclusion of the, of the experiment, so to speak, was that elephants have an anomalous reaction to LSD. And lest you think this was some kooky piece of work, the person that did the work that the original psychiatrist is very well known psychiatrist, and it was published in the very distinguished scientific journal called science. So it has great pedigree. And, you know, if you think about it a bit, you realize that the extraordinary naivete which was used in coming to the amount that they injected Tesco, but more generally, of course, it does bring up the question of how do you scale up when you know something at one size to another size and permeates all of society? I mean, after all, you know, the design of airplanes and ships, and or even automobiles, often done first, some parts have been done on scaled models, which then scale up. And the fundamental question is that how to scale up. But in particular, one that is even of relevance right now as we speak, is many of the experiments done with drugs? new drugs, of course, are done on mice, sometimes on rats, but most in mice? And the question is, how do you scale up? And maybe later, we can even talk about this. When people are looking at cancer? Almost all the Research on Cancer is done on mice. And the question then is, you know, you've done something on wise, you observe something in mice? How do you scale it up in terms of understanding cancer and therapeutic interventions in human beings? So going back to tosco, man, injection of LSD, how much LSD should you have given for all tasco?

The first thing you realize is that, when you give a drug, to when we intake a drug, that drug, first of all gets many drugs get distributed throughout the body, if they're not localized, first of all, through our circulatory system, and and most importantly, though, that when they have to cross surfaces, there's lots of membranes and surfaces that cross surfaces, and as distinct from filling a volume. And so what you should really ask is not how does the weight of an elephant vary, or what is its relationship to the weight of a cat, but rather, sort of what the areas in particular even the surface area of an elephant, how does that scale relative to that of a cat. And this is a well known problem, but goes back to Galileo, Galileo was the first to realize that there was this fundamental difference between how volumes scale versus how areas scale and he realized Not only that, he realized the extraordinary implications of it. And one of them in fact is this one, because just think of, of a queue of size one inch one by one by One. And of course, the No, no, I'm sorry, yes, one by one by one, the volume of that is one cubic inch, now double the size, it's two by two by two, that's eight. So it's eight cubic inches. But think of the area, think of one face of it, the face of the one that's one by one is one square inch, but the face of the two by two is just four. So whereas the volume has increased by a factor of eight, the area has only increased by a factor of four on this much. And in general, it's clear that the volume increase is like the cue of a typical length. So the height of an organism of an animal, whereas surface areas increased by the square. Now, of course, if you have a huge difference in height, that's an enormous number. I mean, if you increase it, in other words, so if something is 10 times longer, or higher than something else, and you just scale it up, keeping the geometry basically the same, then the volume is increased by 10 by 10, by 10, which is 1000. But the area, each area is only increased by 10 by 10 100. So there's an enormous difference. And lead, instead of having using having to use if you do that calculation for Elephants versus cats, instead of a few 100 milligrams, it's more like two or three,

Nick Jikomes 11:33


Geoffrey West 11:35

Which is a perfectly reasonable dose. And, and so this is a fun, fundamental error that was made by you know, extremely good researchers who obviously didn't appreciate this phenomenon. And of course, it is, by the way, in the pharmaceutical industry, when they develop drugs, what you pay for a drug, a huge amount has gone into the question of how does it scale. And the simplest thing is to do what I just said. And, and one of the things I talked about in the book, by the way, was, at the time, when I learned about this story, I still had young babies actually. And and you know, you had these little baby Tylenol bottles, and I looked at the back of the Tylenol bottle, to see how they scaled up with weight. And the thing that shocked me was that so they tell you, you know, if it's if it weighs 10 pounds, so much, you know, half a tablet, 20 pounds, 30 pounds, and so on. And it just gives you the numbers. And it goes all the way up. I think at that time anyway, it started with a baby a newborn baby, basically, and went all the way up to like a 10 or 11 year old. And it went up with their corresponding weights. And to my horror, he went up linear, just as those researchers have done with Tesco, the elephant, and you can calculate and you will see that is there's a huge difference actually, between scaling up in this naive way, versus scaling up in in at least, you know, some semblance of paying attention to what is actually happening with that drug, namely, it has to diffuse across surfaces, and sort of surface area plays a dominant role. And which would give very different doses, dosages, and I asked various pharmaceutical people, if people were aware of this, and of course they are, but somehow the company made Tylenol at that time, and seemed to be or didn't care. But I have noticed more recently, if I when I wrote the book, I went to I went to the CVS and looked at, you know, the Tylenol bottles of baby Tylenol, and found that they take this off this note. So but it's fundamental and and that little example of what you've introduced, our compensation is actually fundamental across all of science and technology, and social life, that asking those questions of how do we scale, whether we scale up a drug, whether we scale up any physiological quantity, whether we scale up aspects of life in a city, and so on a company, how does the company scale from something small to something big? As I say, how do you scale up when you design machinery? and so on and so forth. So it's fundamental, and that's what this book was about.

Nick Jikomes 14:59

Yeah. And one, you know, one thing that comes through in the book is that, you know, the naive, very natural interpretation is, is actually the wrong one, when something gets 10 times larger, you don't just increase everything by 10. And it was fascinating to see all of the examples there in one of the things I want to talk about is metabolism. And so similar to the example with the elephant, I had a guest previously, and we were talking about experiments to do with the drug psilocybin, which is being used for therapeutic applications recently. And it was described to me that, you know, if you give a human being a dose of psilocybin, it's going to have effects that persist for hours on the scale of several hours, if you give a comparable dose scaled, even appropriately scaled, scaled to a mouse, it has effects for minutes, 10s of minutes. So the rate at which these drugs are metabolized is different. So can you describe for listeners, what is metabolism? And how does metabolic rate vary, as you scale up from a smaller to a bigger animal? Sure.

Geoffrey West 16:02

And indeed, it was the whole one question about metabolism. And its various implications, that sort of got me into all the biological applications of this, and sort of turned me into a kind of pseudo biologist as part of my career. And so metabolism, of course, just superficially is the process by which one takes in food, whatever that may be, and turns it into something that can be used to dynamically move things, act on things, and so on. And so for us what that is literally, we take in food, we plants and animals, and so forth. And then we have this extraordinary process that takes place within ourselves, that turns that into metabolic energy in the form of a chemical, highly complex biochemical process producing a chemical that's called ATP, which is basically your currency of energy, there are these molecules. And there's a process, there's a biochemical process, which I don't think we should spend time on. That actually, is the way in which you use that biochemistry, the cycling of that biochemistry, to provide energy to your cells, and then by the coherent behavior of cells, of course, provide energy to your body, and do all the things that we do. And, but integral to that process, so that there's this fundamental level of biochemistry, but integral to that process, of course, okay, so you produce this, this molecule of ATP is sort of currency, your dollars, so to speak, inside cells, and by the way, you produce them in little things called respiratory complexes, which sit inside, little potato looking so called organelles called mitochondria, which I'm sure most of your listeners have heard of, and they sit inside the cell. And just to give you the scale of things that are in in most of your active or organs, the cells contain 500 to 1000, of mitochondria. And inside each mitochondrion, there are maybe 500 to 1000 of these recipe complexes. So each cell has up to a million of these little engines producing ATP. So it's sort of extraordinary actually. But you know, so you produce it, it is highly localized level. And then you have to distribute it, of course, throughout the body, not only you have to supply it, I mean, you have to the the way you produce ATP, is by so called oxidative process. That's why you breathe, you breathe in oxygen, that supplies fuel for producing ATP. But of course, that's highly macroscopic you breathe in through your mouth or nose, goes through your lungs, your lungs, trance to get that energy of that oxygen and then gets transferred to your cardiovascular system into your bloodstream, which then delivers it down at the microscopic level to the cell. So that's sort of the system. And so there are two parts as I say, there's the fundamental biochemistry. And then there's these extraordinary networks. And the one of which you're most familiar with, being your to really the most familiar with your cardiovascular system, which is delivering oxygen to the cells. And then there's your respiratory system, your lungs, which are taking oxygen out The air and delivering it into the cardiovascular system. So, you know, at first you might think, well look, since it all happens inside cells, the actual production of your energy, if you double the size of an organism, you therefore double the size of your cells, you have twice as many cells. So you have twice as many of these little engines. So you would have twice the metabolic rate, that is, you would require twice as much food to feed an animal twice the size, because you have twice as many cells.

That's not the case, it's the thing that is extraordinary is that if you double the size of a mammal, let's just say mammals, woman, then instead of eating twice the amount of food twice the amount of incoming energy, you only need roughly speaking 75% as much anytime you double. So if you go from two grams to four grams, you double, I'm sorry, you only need 75% to double the size, you only need 75%. But if you went from two kilograms to four kilograms, or from 200 kilograms to 400 kilograms doesn't matter, you only need 75%. So each, each doubling only requires 75%, which means in English, so to speak, that the bigger you are, the more efficient you are, because you need less energy to support the same mass of tissue. And and the question then is, why is that? Where does that come from. And that comes from the constraints of the delivery system of these transport systems that are taking blood to your cells, and then delivering energy to your cells. So the your circulatory system and so on. So, and you're we're all familiar with them. And we know that they look sort of you look out the window, and you're looking at a tree, it sort of looks like a tree inside you. It starts with your you know, you have your heart pump blood through your aorta, and then it goes through this multiple branch network delivering down to capillaries, capillaries then transfer the oxygen to the cells. And the work that I got involved with was in fact trying to understand where this extraordinary economy of scale is savings, every time you double the size, or just simply saying the bigger you are, the less energy you need, per cell or per gram of tissue was proposing that this this theoretical idea that it is because of the constraints of the network. And so without going into any details, it was that the various constraints of the network such as something that seems so simple, that first of all, the network has to go everywhere every cell has to be fed. Another one was that the as you change sizes, among organisms, or as you grow, from a baby to an adult, even though everything is getting bigger, you go from a mouse to a whale, of my baby to an adult, or even though everything's getting bigger, actually, the size of your capillaries, or the size of your cells do not change. The same you look at a cell or a capillary of a whale, it looks just like yours or mine. And the point of that is that when natural selection evolved new species, it didn't in reinvent the basic fundamental units. It built from the same fundamental units like capillaries, and cells and so on of genes. It used the same thing over and over again, to make you know, horses and elephants and so on. And so that's, that's another fundamental constraint on the network, that the the terminal units are sort of fixed. And the last constraint on these kinds of networks is also coming from natural selection. And that is that the continuous feedback processes are mechanisms that are implicit in natural selection, the continuing honing of the system, through survival of the fittest, to leads to a kind of optimized situation that is that the circulatory system that we have, we meaning not just you and me, not just every human being that's on this planet, but every human being that's ever lived. Not only every human being that separately, every mammal that's ever lived. We all share this. Same cardiovascular system. And it is the one that has evolved to minimize the amount of energy our hearts have to do to pump blood through the system to supply oxygen to the cells, you want to minimize that, in order to maximize the amount of energy leftover to have sex, reproduce, and make children and put forward your genes. So that's the idea, it's sort of,

to increase in technical terms, to maximize fitness, minimize the amount of energy you need in keeping organisms alive. And that means optimizing the network. And just to finish off that story, if you do that, and you put all those words, which I said in English into mathematics, which is non trivial, but you can do it, if you put it into and you grind the machine, so to speak, outcomes, these extraordinary scaling laws, and this 75% savings. And indeed, the other thing that comes out of it is that any physiological quantity that we have, which we haven't talked about, or the other ones, one of which is indeed, what you just wrote, you started out the question with, and that is that the time taken to metabolize a drug is much faster in a mouse than it is in a human being, we can calculate that. And again, it satisfies a very simple scaling law, which has built into it, this kind of 25%, this one quarter kind of quantity. So there's a lot there. Let me know if there was a lot there. And I'm sorry, I talked too much.

Nick Jikomes 26:49

Let me repeat some of that to make sure I'm following. And then I have some follow up questions that I think will make this more even more clear to people. So you mentioned the concept of economies of scale, which many people have heard about in another context, you typically hear about that in the context of an economics course, or a company becoming more efficient as it grows. And we are going to talk about companies at the end. But something similar seems to be true with animals, as the animal gets bigger and bigger, the animal is more metabolically efficient. And what you're saying is, if you double the size of an animal, if you increase it by 100%, you don't need 100%, more fuel, fuel food to power that animal, you only need a 75% increase. So there's this economy of scale. And ultimately, that very specific and quantifiably elegant way of describing that is due to the fact that all animals are a made out of the same basic building blocks, evolution is playing with the same basic building blocks in the form of ourselves and what they do. And at the end of the day, animals are simply being optimized by natural selection forces, so that they're putting as little effort and energy into running their bodies as possible, such that they can put as much energy as possible into reproduction. So your body wants, if I'm going to anthropomorphize your body wants to spend as little effort as possible, beating your heart and pumping your blood and spend as much of that energy that you have from your food as possible on ultimately finding a mate.

Geoffrey West 28:29

Well, that's the idea. That's the conceptual framework. And as I said, and you've said, it's so much more articularly, the my did I thank you.

Nick Jikomes 28:39

I've read the book a couple of times.

Geoffrey West 28:43

So yes, the only thing I would add to that is that that also implies in terms of the concept of economies of scale, is that that means that per gram, or per cell, you need less energy, therefore, your cells are working less hard than your dogs or your cats. But your horses cells are working less hard than yours, in a very systematic, predictable quantitative fashion. And, and, and that has extraordinary implications. You know, in terms of, you know, lifespans, and the amount of damage that is being done by metabolizing, and so on, how long you sleep, and so on, and we can discuss those if you wish, but it's fundamental to life. And it is in that sense, that I mean economy of scale. Bigger you are, the less you need per gram of tissue, or per cell.

Nick Jikomes 29:50

Yes, and I was about to ask about some of the things you just touched on. So as animals get bigger, their cells have to work less hard, basically. And how does this tie in to things like lifespan? So bigger animals tend to live a long time, little animals, like mice tend to live for a very short period of time. How does that tie into this? And how does it tie into, you know, seemingly, you know, seemingly everyday things like, you know, why do little babies sleep for most of the day, but as we grow, we don't have to sleep as much, and why do we even grow at all, and then stop growing,

Geoffrey West 30:26

all of the above come from this. So indeed, so let's first talk about aging and death. mortality. So, you know, but the fact that you're metabolizing, the fact that your cells are doing work, and the fact that the that work is primarily being done, in order to combat the the the wear and tear that is naturally occurring. So for example, in, in this process of metabolism, you are generally dissipating energy that is making energy that is not useful. by two processes. One is, if we think of that network, your cardiovascular system, there's blood flowing in it, it's being pushed through your arteries, and capillaries and so forth. And it's wearing you will actually vary just like, you know, trucks driving on the highway, or waterfront flowing through the pipes in your house, they eventually actually do wear. And that's what's happening inside you. And of course, you are repairing yourself as well. And there's a trade off, because repair is very expensive. to continually repair. And so the, but that eventually, that production of wear and tear, which is in physics is called entropy, the production of entropy. And is has a much more profound origin in terms of the so called second law of thermodynamics, which simply says that if you create order, if you spend your energy to create order, which is what we're trying to do, when we eat, to keep us whole and keep us healthy, you necessarily create disorder, that creation of disorders called entropy. And so it's inevitable that you do create wear and tear. That's why you know, we have problems with the environment, I mean, we can come back to that later when we talk about cities and so on. But in the context of our bodies, in terms of biology, and life and death, we create disorder in the form of wear and tear, first of all, in the networks, but also in the production of this of our fundamental energy in terms of this molecule, ATP, because in so doing, there are sort of biochemical networks. And one of the products there is something that are called oxygen radicals, which many people heard about. That's one of the products of the production of ATP. And those oxygen radicals, what all that means is that it's the difference between an oxygen radical and the oxygen we're breathing is that it's stripped of an electron. And that means it's charged, which means it's, it attracts other things, and can be highly disruptive. And so we have mechanisms inside us to try to combat that. And of course, there's now been a whole industry created in terms of antioxidants, and so on, partly, hopefully to help combat that. But it is inevitably creating damage inside ourselves. And as I say, we do repair ourselves. So, but the crucial point here is two things. One is there's this inextricable continuous degradation of the system, which has happens to us. And natural selection has evolved, so that we repair enough of ourselves so that we live if we weren't out so called natural state, till about 35 or 40. And in that time, we'll have had maybe 10 to 15 children of spring, which may be half to two thirds will have survived. And that was our natural state. And by the time we're 35, or 40, natural selection doesn't care if we've done our job. We produced our children, and then we would die and indeed, the natural lifespan of a human being. If you You know, prior to about, I don't know, 1900. Very recently, worldwide, the expected lifespan of a human being was between 35 and 40. It's only in the last 150 years that it has changed. And we'll come back, we can come back to that later because it's actually to do with cities, and health, and so on health care and so forth. But

so we have this degradation that's going on. And but we looked just a little while ago, that we have this economy of scale, meaning there's, we require less energy to support a cell in a large animal than a smaller one, therefore, there's less damage is being done in a larger animal rather than a smaller one. Therefore, a larger animal will live longer in a predictable way, in a way that you can actually calculate. And that's why large animals live much longer than small ones, a mouse lives maybe two to three years, a shrew, which is the smallest mammal, there's maybe one to two, and a blue whale, there's about 125 years. And so you know, that their rate of aging is extremely slow. And so the, you know, this the this economy of scale, and this hedge money, if you like, of the network, controlling the distribution of energy has extraordinary implications for not just our physiology, but our whole life history, from birth to death. And going back to the second part of your question, namely, concerning babies to adults, it's similar thing we do much more, we do much more damage, potentially, when we're smaller than larger than when we're larger. And in fact, if we could, if you, maybe I should talk about sleep a little bit, because it's closely related to aging, surprisingly, I'm going to first we don't think in those terms. But aging and sleep are very closely connected, because the reason we sleep is that we are, as I say, continually damaging ourselves, so to speak, and thereby aging, but also repairing ourselves, as I'm sitting here, my body is not just creating damage, but it's repairing my liver and my pancreas and so forth. But it's very hard for it to repair my brain. Because I'm also trying to be articulate, and think and have a conversation with you. At the same time, very hard. If I spend a lot of my time and energy trying to fix all those little damages that are being done by neurons.

Nick Jikomes 38:04

It's like an auto mechanic, right, your your auto mechanics gonna turn off your car and park it in the garage, before it fixes the

Geoffrey West 38:11

engine zactly, you do not, you do not repair your car while driving. You stop it, you either get out repair it yourself, or you have a dedicated time you take it to a garage and give it to a mechanic who has dedicated time repairing it. The same with the something like the internet, when they want to do something to the internet repair something, or your local server or whatever, they try to do it at night or on weekends, when there's less activity. And so it is with our brains, that we have to have a dedicated time because we we need to be active, we need to be in control. And so we need to shut the system down in order to do that. And here's really the other crucial part about the brain. And that is we need to repair it faithfully. Meaning that you know, it doesn't really matter very much if you don't get things exactly right, repairing your pancreas or your liver. And in fact, you know, it just it does Age Of course, but it lasts the can last 100 years. And if it's a little bit different than my age when he was at your age, you know, okay, I'm not as healthy as you but you know, I'm still functioning. But if I don't do that to my brain, if I don't repair, make sure that it's very carefully and in a detailed way repaired. Very soon, I will not be me. I will start to become dysfunctional. I will start to have all kinds of serious psychological, mental issues and I will No doubt, don't become demented. And in fact will die. Let me just tell you tell your listeners if then if they're not familiar with it, it's quite extraordinary that this was first done, right at the turn of the 18th 19th century about 1900, by a Russian biologist named man, RCN, who did the following, she took puppies to a bunch of puppies. And it's kind of a horrible experiment that way, and would not let them sleep. They would simply never allowed to sleep. And within less than a week or two, they all died. She took another bunch of puppies, and starved them for over 20 days, nearly a month. And they lived. And once you gave them food, they came back to the norm. And so the brain, I mean, requires detailed repair mechanisms, which is what we have. So we spend even though the brain is only 20 is is only 2% of our weight, 2% of our tissue is brain, it takes 20% of the food you eat, goes to to support your brain. And almost all of that goes to keeping it in this faithfully repaired state.

Nick Jikomes 41:36

So the reason that we're mortal, and that we're finite, and we die is because as a natural consequence of being alive, and having metabolism, we're accumulating wear and tear that will eventually kill us. Yes, and the reason that sleep is so important is because it's it's fixing the wear and tear that's accumulating. And the reason that bigger animals need to sleep less and smaller animals sleep more human babies sleep more than adults, mice sleep 20 plus hours a day. It's because they're chugging along so quickly, in a metabolic sense that they accumulate damage faster than they actually need to dedicate more time to fixing the damage.

Geoffrey West 42:16

Exactly, that was the punchline of what I was saying exactly. Interesting. But neither one of those, it's kind of a secondary implication of this extraordinary systematic economy of scale, the bigger you are, the less energy is needed to support a cell. And therefore less damage. And just as you said, therefore, less sleep. And it's kind of amazing, most people don't know that an elephant only sleeps three or four hours a night. And whereas the mouth sleeps 16 or so. And a blue whale, by the way, only sleeps probably about two hours if that. And as you well know, as you already said, a baby sleeps 16 to 18 hours a night when it's newborn. So once upon a time, we slept much longer than we did now. You know, and in our individual life, live lives. But we settled down at eight. And that's what we need to keep us you know, our brain functioning. And we all know the symptoms if you have a lousy night's sleep. And if you you know, especially when you're a student, or you're having some huge project, you've got to get done, and you only get, you know, five or six hours a night, after a week of that you're in pretty bad shape.

Nick Jikomes 43:42

So, one of the last questions I want to ask about organisms before we move on to cities, is I don't want to get too deep into the math here. But and for those that don't know, Jeffrey is a physicist in terms of his background. So there's a lot of math in the book. That's fascinating. But what's sort of funny is, a lot of people have heard of The Hitchhiker's Guide to the Galaxy than the famous scene in that book where someone says the answer to life is 42. When you read Jeffrey's books scale, you might say that the the, the answer to life is the number four. So where does this number four come up? We've sort of touched on it very briefly so far. But where does four come from? And how does that tie to concepts from fractal geometry?

Geoffrey West 44:30

Thank you. Yeah. So we, I already remarked and we discussed the scaling of metabolic rate, that's the most fundamental, because it's energy, it's fundamental. And that has this sort of 25% savings with each doubling. So there's a number for one quarter 25 45%. But what is extraordinary and what I didn't say then, is that if you look at any physiological quantity that you can measure something as mundane as the length of your aorta, and so on. But, and something as sophisticated as lifespan or, or so anything to do with your physiology or life history that can be measured, then it scales with somehow dominated by this number one quarter. So you know, things like your growth rate also increases in the same way as your metabolic rate, not surprisingly, maybe, but again, with a 75% savings with each doubling, and so forth. So there's, there's all these, these multiple quantities all, when you look at how they scale are dominated by this number one quarter, there's number four. And indeed, where does it come from? Well, it does come from the the properties properties that I tried to articulate about these the multiple networks that sustain our lives. The one I we've concentrated on is the circulatory system. But it's true of all our multiple networks that transport energy and information, that they all have these kinds of properties that they have to be, they have to go everywhere, they have to be space filling, they have the terminal units that are invariant or change with size. And there's this kind of optimization. And out of that comes this number four from mathematics. But then number four has an interesting interpretation. That comes also from the mathematics the mathematics also says that the optimal configuration of these networks is that they're fractal like that is the you know, if you it's like a tree, if you cut out any piece of a tree, it looks like a little tree. And then if you take another piece of that looks like an even smaller tree, and that's true of a vast majority of the networks inside us. And that fractal behavior is a reflection of the sort of spring optimization that we're striving towards. And and then number four, gets reflected or is a reflection of two things, actually. One is this fractal nature. But the other is that the number four is really actually from the mathematics three plus one, it sounds Zen like, but it the three is the dimensionality of space in which we live. You said like space filling, it has to go everywhere, that means it's sensitive to the dimensions of space that it has to fill. And that's the three dimensions of space we live in. so to speak, up, down sideways, and that, and the plus one comes from this fractality fractals in Tao objects geometrically, in terms of the way they scale with something that can be interpreted as an extra dimension. And so very roughly speaking, but four is actually the dimension of space refill plus one, which means that if we lived in, I don't know, 11 dimensions, it's my string theory friends, maybe we do, instead of be everything being dominated by one quarter, they'd be dominated by 112 112 11 plus one,

Nick Jikomes 48:38

the way that I've been listening, I'll go ahead.

Geoffrey West 48:41

No, I would just add one thing, it may have occurred already to listeners look, you know, if we lived in, in two dimensions, if you lived in sort of space, everything we do is dominated by 1/3 instead of one quarter. So we've actually tried to do some little experiments on our looking at plants that grow, you know, like IV, ah, actually. And we, we had a student do this some years ago. And indeed, what we learned was that the data on the stuff that you measure was consistent with the 1/3. But unfortunately, you couldn't grow IV big enough to have big enough changes scale, to really test it rigorously. So it's, but it was consistent with 1/3. It was just so it was interesting. You know, we've often thought about looking at flat fish and things like that. But again, there isn't the data help there. There wasn't enough sort of range of sizes to really test it.

Nick Jikomes 49:52

So we're three dimensional creatures. spatially speaking, everyone understands that intuitively. You can go forward, back, up, down, left, right. And then when you start talking about the fractal geometry stuff, everything gets a plus one. So we're in some sense,

Geoffrey West 50:06

really fractals, what's called completely fractal, then it adds one. And the way it's Microsoft, the partial,

Nick Jikomes 50:14

I see, the way that I tried to wrap my head around this, when I first read it was, I imagined, like a string of yarn. And we could just call that one dimensional. And then maybe, maybe you just bend the string in an S shape on itself, so that it's rectangular, you could imagine taking a string of yarn, bending it so that it's a flat rectangle. And now it's a one dimensional string, but it's behaving as if it's a two dimensional sheet. That's that the intuition?

Geoffrey West 50:43

Yes, it's sort of like that. And I know in the book, I tried to give the example of bedsheets which are two dimensional, or you know, sheet is two dimensional. But you know, when you scrunch it up, when you put it inside the washing machine, it becomes three dimensional, it's a, it's like a ball, you can scrunch it all up into a ball. And it acts three dimensionally. And so and in fact, by the way, if you look at all the creases, all those multiple creases, some of which may be, you know, quite large, maybe an inch, or a couple of inches, some a tiny, you know, a 10th of an inch, and so forth. But people have actually looked at that. And if you look at the distribution of all those creases, they follow a fractal law. So it is a fractal actually a scrunched up sheet, if you look at all the multiple creases, and and it is acting as if it's a three dimensional object, even though it's a sheet, which is a two dimensional object.

Nick Jikomes 51:48

Interesting. So at the end of the day, all of this, all of these scaling laws are about network architecture. And they dictate why we're mortal and finite, why we grow, the way we grow, and so forth. But organisms are quite different from cities in some ways. And so I thought we could start talking about cities now. And in the book, at least, at one point, you distinguish between two aspects of cities, one being their physical infrastructure, the roads and the power grids and those things. And the second being the socio economic factors that are enabled by this infrastructure. And so can you talk about each of those and how they're similar or different to what we've been talking about in organisms?

Geoffrey West 52:29

Sure, yes. So yes, so just to elaborate on what you just how you introduced it, you know, when you when you say the word city, somewhat, of course, they immediately think of the city in terms of its physical infrastructure that is, you know, the buildings and roads, and maybe the telephone wires and all the rest of it. But it's very much the image of the city, the skyscrapers of New York, or the boulevards of Paris, very much the image of a city. And, of course, that's what that is a city. But, you know, it's in a certain sense, it's the less interesting part of the city, because it's actually just the the stage or the backdrop for facilitating social interaction. That's why we developed it, that's why we evolved it. And, and I would even argue that it is the most marvelous, wonderful machine we've ever invented. Because it is the facilitator of social interactions. It's a place I mean, as successful city is, in terms of its infrastructure, is a place that encourages in social interaction. It has lecture halls and stadiums, and universities and schools and places to bring peeps of people together, businesses can be thought of that way, places to bring people together, to create ideas to innovate, to create wealth, and so forth. And, and a great city not only has that, but it has informal places, lots of squares, and coffee places and parks, again, to bring people together to create wealth, ideas, increase standards, the quality of life, and that's what it's done. It's been unbelievably successful doing it. And so. So it's useful in that context, to think of the city as these two components. Its physicality, its infrastructure, which you could think of as associated with metabolism in terms of biology, because, you know, the way I was talking about metabolism in biology was very much dominated by the transport of metabolic energy through networks. And that's what a city does, man. It's the road Of course, and the transport system in general, and the electrical lines and the water lines, and so on. And so in that sense, if we just focus on that, for the moment, the city is quite biological, and it's quite analogous to an organism. And so if you also think that as cities have evolved, and they haven't been volved, for very long, after all, they've only been around at most, a few 1000 years, and the vast majority for a few 100 years. But nevertheless, they have evolved by some process akin to, you know, natural selection evolution, there has been some version of that taking place. And in that sense, there's also been a process towards some kind of optimization that's going on. That is the the various structures and the various transport systems and so forth, have evolved was that not so if you take that, and you go back to the ideas that we were talking about, in terms of organisms and networks and metabolism, we would have a similar process here, that the the kind of the metabolism of a city in terms of its physicality, in terms of the physical aspect of it would be like an organism, it would have an economy of scale. That is, if you looked at various quantities, they would scale and I didn't use this phrase, sub linearly, meaning going back to the organisms, that three quarters, that 75% is less than one, and we call that sub linear. And cities would also be sublinear. That is, if you've doubled the size of a city, instead of needing twice as many roads and twice as many gas stations, and twice as much length of electrical lines, you don't really need some percentage of those, and indeed, wonderfully, if you look at the data across the globe, urban systems across the globe, that's what happens, there is this extraordinary systematic economy of scale, that if you double the size of a city, you don't need twice as many roads and twice as many gas stations, and twice as

length of electrolytes, you only need not 75%, as in biology, but 85%. So the only difference is that the amount that you save as you get bigger, is a little bit less than it is for organisms. And by the way, we don't really understand why that is. So you know why it is it is somehow related to the two dimensionality of a city rather than three dimensionality. But we don't fully understand this is that that part is still a work in progress. But we looked at data across the globe, for all the physical infrastructure, and amazingly the same, what was fantastic, it had the same kind of universality. Just as this and I didn't really emphasize this, that that scaling and metabolic rate and all the other physiological quantities, I talk mostly about mammals, but it's true of all taxonomic groups, birds, fish, insects, crustacea. And so it is, for cities, that the scaling that we see, for example, in the United States is the same as, as we see in Argentina, as we see in Brazil, as we see China, as we've seen Japan, as we see in Spain. So there's this kind of universality, that is at work, which is sort of extraordinary, because, you know, when you think of a city, especially somehow, you think of all the politics and the urban planning, and all the development that goes on, and the highly individuality, individual nature of a city, and so forth. And yet, when all is said and done in this very coarse grained way, everybody sort of lined up. According to the these, this very simple law, these are very simple laws. And so justice it is that and I didn't put it this way. But in, in biology, even though the world is in the ocean, and the elephant has a trunk and the giraffe has a long neck and work on two feet, and the mouse scurries around and we all live in different environments. We're actually at the kind of at 90% level scaled versions of one another following these nonlinear scaling laws. So it is in say in the United States, that despite the fact that New York, Los Angeles, Chicago and Santa Fe, where I live, all have different histories of geographies, even different cultures. Nevertheless, they are scaled versions one another, at least because we've talked so far about the infrastructure. Amazingly, Santa Fe is actually a scaled down version of New York, although it looks completely different. Yeah. And so so that's amazing.

Nick Jikomes 1:00:27

So another way of saying that, that's kind of interesting is, so what you've just told us, essentially, is that if I go and count all of the gas stations in Santa Fe, and I get a number that tells me the per capita concentration of gas stations in Santa Fe, I can predict with high precision the per capita density of gas stations in New York City,

Geoffrey West 1:00:47

right? Yes, but the idea 90%? No, no, by the way, I should add that we chose gas stations. But you know, in the last 1020 years, things have changed about gas stations, and sort of his caveats to this, of course, because there are social changes and innovations that come in, and we can talk about that, and maybe a little bit, but so there's the infrastructure. But you know, as I said a little while ago, in a certain sense, that's the uninteresting part. And it's, it's it's sort of it is biological, but it's not the part that is the essential feature of a city, which is socio economic activity, meaning something to do with the way people interact with each other, and the things that are outside of biology. And so we looked at socio economic metrics, like wages, like crime, my, the number of AIDS cases, number of flu cases, number of patents produced, that is the kind of innovation of a city and so we looked at those as a function of city size. And here we sat found something different, we did find scaling, we found very good evidence of scaling that it's quite regular, and systematic, with systematic but instead of an economy of scale, meaning the bigger you are, the less per capita biology, the less Purcell now we found, the bigger you are, the more per capita, the higher the wages per capita, the more aids cases per capita, the more fancy restaurants per capita, the more patents per capita. But what was fascinating was all of those to the same degree. Not only all of those to the same degree, but it was the same across the globe, roughly speaking, that is, and what the law was, was that if you double the size of a city, you don't get just twice as many patents produced, but you get twice as many plus 15%. Or you get, if you ask a violent crime, you get twice as much violent crime with double the size of a city. No, you get twice as many plus 15%. So this was true of all socio economic activity, whether it was good wages and patents and education, or whether it's bad and ugly, like disease, or crime. And, and indeed, like the pandemic, we looked at, of course, data in the pandemic, it's quite similar. So it's quite fascinating. So there was this extraordinary regularity. And not just that cities scale within an urban system so that Los Angeles is in fact, a scaled down New York, I mean, not the sort of 80 85% level. But and, but it's true across the globe, it's it's not that you have to be a little bit careful here. It's within urban systems that is failing through the scaling across China, or Japan, is the same as it is across the United States. But it doesn't say New York is a scaled down version of Beijing. That is it is if you renormalize. So New York, is a scaled up San Francisco in the same way that Tokyo would be a scaled up Osaka, within Japan, for example. Nevertheless, if you so they scale in the same way together, and what distinguishes Tokyo from New York is basically the overall scale of things which has to do of course, with the difference between Japanese culture And United States culture so. So violent crime or murders, say murders was scaled the same way between Tokyo and Osaka as it does between, say, New York and San Francisco. But the overall scale will be different. Because Japan is much less violent society, the United States, and that, of course, is outside of this framework.

Nick Jikomes 1:05:30

There is, I mean, this brings me to an interesting area, which is the nature of social networks. There's a great quote in the book where you say, cities are collective phenomena, whose origins emerge from the underlying dynamics and organization of how people interact with one another in social networks. And so I wanted to ask you about the evolving nature of social interaction, and in particular, social media. So social media is drastically changing the way that social networks actually look and how we're able to communicate with people within our own cities and and elsewhere around the globe. So how do you see social media changing the growth dynamics that we that we talk about for cities?

Geoffrey West 1:06:14

Sure, let me answer. Let me first back off a little bit. And first, go to the first part of what you said. And that is because it's, the first part is really answering. Where the hell do these scaling laws come from? In cities? You know, I mean, we it's, it's, it's pretty clear that as far as the infrastructure of cities, I already said, it is to do with the network, you know, the infrastructure networks, they're much like the biological networks, our circulatory system, respiratory system, and so forth. But what about these socio economic ones? Well, they are, they're derived from social networks, the thing and, and the reason that cities are sort of scaled versions, one another socio economically, and that it's the same across the globe, is that social networks are pretty much the same, not just across the United States, because we're all American, so to speak, but across the globe, because we're all human beings, and we all have pretty much the same kind of social DNA, modulated Of course, by our culture, local culture, and geography, and history, and so on. But the dominant thing, of course, is already sort of encoded, so to speak, in our genes and our your networks. So and that's why the scaling of the number of patents produced as a function of city size is related to the number of AIDS cases, because the in the end, they all have their origin in the interaction between people. Okay, in much the same way, by the way, to bring it to something more current, that the reason we have pandemics is we interact, and we exchange, unfortunately, viruses between each other. But you know, and that's what cities do best. That's why it's much, much worse in cities, cities, that's what cities are, therefore, is for you to transfer, quote, viruses, but of course, they're supposed to be good viruses, like ideas, and so on. So it's quite similar, the dynamic is actually quite similar. And so in combating a disease like a pandemic, especially, you have to decrease social interactions, which is completely against the whole functionality of city. And of course, means that you suffer socio economically for other things. But it's also why and it's sort of obvious why, you know, flus and colds went way down this year, because we're all physically separating. Okay, so that is the underlying mathematical theory for how all this works. But that brings up exactly the question which you've asked, which is, okay, with the coming of the internet and coming invitee, in general, we've expanded, so to speak the reach at least, and the immediacy of social networks. And how does that change things? And I must say, when I first started thinking about this, I thought, well, that's going to have a very profound effect. And I may well, it may well have a profound effect on the scaling on these on the things. Of course, it has a profound effect in terms of we just saw it the last four years, and in terms of the election and so forth, as an example, but I meant I mean, and what effect does it have on these very general goals? And kind of universalities? Does it change them? Does it change the 15%, and so on and so forth, and all the various implications of that 15%, which we haven't yet touched on. And I first thought, yes, it's gonna have, you know, it's, it's gonna have a huge effect. But the more I thought about it, and the more I read about previous major innovations, the less I was convinced, because I began to realize that, you know, a much bigger impact on on human socio economic activity occurred much earlier occurred, of course, with the first of all the invention of the steam engine, and the coming of trains, and locomotives, because what that did was change people's spatial reach.

I mean, until the coming of the train, and even actually, until relatively recently, the vast majority of people on the planet didn't move more than about 20 miles, 1020 miles in their entire lifetime, they stayed highly localized. Because they were limited by walking. And if they were fortunate, being able to use a horse, and most people were limited by walking. And so they were highly limited in their spatial extent, and then comes along the locomotive, which opens it up. And of course, we wouldn't have America, if it wasn't for the locomotive, I mean, that that's what allowed people, even people that are very modest means to move very large distances, compared to what they could before. So that is an extraordinary change and extraordinary discontinuity in, in human history. But you know, not so long after the coming of the locomotive within, you know, 50 years or so, of its big impact, we had another equally and maybe even more, so change was the invention of the telephone. I mean, up to the telephone, messages and communication between people unless you were in their proximity, would take at least days, sometimes, sometimes weeks, and occasionally months now to go overseas, and so on. And, you know, suddenly, you can have instant communication. So we've had instant communication for well over 100 years for the telephone. And, and, in fact, in some ways, more intimate, instant communication that we've had now, because we actually talk to one another. Now, of course, we don't even talk to one another. We Well, we do that. With the before, before the most recent innovations of zoom, and so on. It was basically, you know, messages, I mean, text and emails and so on, which have a certain personal quality to them, whereas the telephone release was still personal, and still involved, much more, much closer interaction. So, you know, nothing. But so what did that do? Well, as far as we can tell, it didn't change the scaling laws. By the way, let me just make a tangential comment here. Because that's an interesting question. It's been very hard to test them historically. Because we don't have data. But some of my colleagues did a wonderful test. They had data, archaeological data, which is a bit you know, I don't know. I mean, archaeological data is comes from things like pot shards and measurements, of course, it's very coarse, of course, nevertheless, you know, there is a science of it. And they took this data of pre Columbian urban system in Mexico, and which contains about 50 communities turns out, and they looked at all these these these various psychological metrics, and what did they find that socio economic thing scaled with this 50% completion, which was very nice, actually, it's controversial, obviously, because of the course, the very cost nature of this data, but it was satisfying that at least it was consistent with that. But in general, it's been hard to test. But the tests that we've done the data that has been done, shows that you know, it hasn't changed. So what so what did it do? It sped up life and this is Something we did not talk about in biology. And I'd like to spend a minute or two, just saying words in biology, it was implied in what I said that those network effects and those scaling laws have also implied in them, as a consequence is that the bigger you all the slower everything is, in a systematic way. And we touched on it, because your opening question about how long drugs get this, how long it takes to dissipate drugs in your body, it happens very quickly in mice, and very slowly in elephants say on for now it's relatively speaking. And that's because things slow down in a systematic, predictable way.

And that's associated with that economy of scale driven by the the networks. Now move to cities, where we have the opposite of economy of scale, increasing returns to scale, the super linear scaling, as we call it, and I didn't say this, but concomitantly with that we have, instead of the slowing of the pace of life, the increasing pace of life. And this is crucial that that network phenomenon increases the pace of life. And it's easy to understand, understand, because where does it come from? It comes from, when we gather together, what cities do bringing people together, a talks to B, B talks to C, C talks to D, and we build up on each other, we build up, we build up, conversations, ideas develop, most of which are useless and irrelevant to most other people. They don't, they don't go very far. But they are building on ideas. And what is remarkable about cities. And the whole phenomenon of this positive feedback in social networks, is every once in a while it produces the theory of relativity, or quantum mechanics, or an Amazon or a Google. That's what it that's what it's done. And so that positive feedback is the origin of the superlinear. The bigger you are, the more you have per capita, that's why big cities get more because they increase social interaction. And at the same time, instead of slowing the pace of life, they increase the pace of life in a systematic, predictable way. So diseases spread faster, ideas spread faster. And so.

Nick Jikomes 1:17:34

So I want to start to talk about companies, because you brought up a few that I specifically want to ask about. So so far, a very high level summary for me is animal animals follow these sub linear scaling laws, which makes us finite and makes us mortal, we grow, we stop, grow, and grow. And then we die. Cities exhibit some of these super linear scaling laws. And this gives them the potential for unbounded growth and effectively immortality.

Geoffrey West 1:18:01

Exactly. So that's the idea. We didn't we didn't say I didn't go through that. But that's exactly what the implications of this are that that sub linear scaling economies of scale, lead to bounded growth, which is what we have been explains, in fact, without going into any of the details, why it is that you grow quickly, and then you stop, and you spend most of your life, you know, roughly stable configuration. And of course, and you immediately realize that that's very bad in terms of our socio economic paradigm. I mean, since the discovery of fossil fuels, the exploitation of fossil fuels, and the Industrial Revolution, and the discovery of capitalism, entrepreneurship, and so on, the paradigm is one of open ended growth. And what is very nice about this theory is that the positive feedback in social networks induces the superlinear behavior, the more you the bigger you are, the more you have per capita. And if you feed that into what that says about growth, it says instead of bounded grow, you have open ended growth, which is what we have. So it's very self consistent, and it's very predictive, it does have built into it a fatal consequences a potentially fatal consequence. And that is that you cannot sustain that open ended growth indefinitely. You unless this is what the theory says, unless you have unless you innovate, unless you sort of so to speak, start the clock over again by reinventing yourself. Because otherwise, the theory tells you, you would collapse in some finite time. And it goes by the name technically of a finite time singularity. In the mathematics, there's something called a finite time singularity, which says that the system World collapse in some type finite time, unless you sort of speak, reset the clock, make a paradigm shift you, you discover oil, you discover coal, you invent computers, you deceive, invent the IIT, all these sort of our major paradigm shifts, that sort of sort of speed resets the clock, and allows you to continue with open ended growth. And the the price you pay for that, going back to what how you started this conversation, the price you pay for that is to try to do everything faster and faster. That is where the rub is, is that can you In fact, continue that increasing acceleration of the pace of life?

Nick Jikomes 1:20:49

Yeah, I think that's that's an interesting question. We could probably spend an entire podcast,

Geoffrey West 1:20:56

but that's sort of, you know, leads into all kinds of speculative phenomena. beyond what we've discussed, all I've said up to now pretty much is, you know, has a very sound scientific basis and is confirmed by data, and so on, and so forth. Once we get into this area of what do we do about open ended growth and collapse? It becomes much more speculative. related to the company's stuff.

Nick Jikomes 1:21:26

Yes. So now let's, let's shift the companies. So in some sense, you could say that companies are more like organisms in cities, and that they are mortal. They are born, they grow, they stop growing, and then they die. And so why is that? And what is perhaps the analog of metabolism in a company?

Geoffrey West 1:21:45

That's, so these are tough questions, actually, is a mean, to tell you the truth. I mean, and part of the reason that tough, is unlike cities and organisms, we can't get data. I mean, we can't get some beta, we're getting data. In fact, the data that we have is, is based on basically our tax returns. And that's potentially public, although we have to pay a minor fortune to get hold of that data, by the way. But we have that data and that data, just so that everything I'm going to talk about is based on that data set. And that data covers covers all US publicly traded company, since about 1950. So, so everything I say is based on that we don't have private companies, and we don't have data before. So that's it. But But what we also need so, you know, one of the things that is very important for understanding organisms, in particular, but also cities, is we, we know an enormous amount about our physiology, we know what, you know, we know what we look like on our insides, both macroscopically in terms of the, you know, all these networks and our organs, and so on, but also, within ourselves, we know in great detail, what goes on in ourselves, you know about our genes and so on. What do we know about companies, despite all of the business schools, case studies, and so on? Not a lot, actually, in terms of the kinds of things we need to know, what are the networks inside companies? Who's talking to who How does it really work? Who is that? You know?

It's proprietary, right? And it's very hard to get, as I say, there are case studies. But there isn't the kind of systematic study of the number of companies, by the way in that data set is about 30,000. What you'd like to have is all the details of what goes on inside those 30,000 companies, many of which no longer exists. That's what you'd like to have. And of course, we don't. So there's big caveats. And that's what makes answering your questions a little bit more difficult. Nevertheless, we can ask the question, the first question that starts off all of these studies, and that is, do companies scale? You know, is is Walmart, or Google a scaled up version of some small company that is in your town? And roughly speaking, the answer is yes. You know, if you look at the various metrics that we have, what do we have? We have sales, we have income, we have expenses, we have number of employees, we have the assets. So we have all these various things that you can imagine, need to be reported to the IRS. And we get sifted through data sets that we buy from Dun and Bradstreet for anyone interested. It's called copy stat. And we use that data set by To examine this, we do have, by the way, I should mention, we have a Chinese collaborator in Beijing, who had access to similar data for the Beijing and Shanghai Stock markets. And one of the remarkable things is that that data, even though it was only, you know, the stock markets only be going 10 or 15 years, mimics the US stock market in terms of the scaling results as just a side comment. So that was that was kind of nice. But so the data does show good evidence of scaling, but there's much more variants, there's much more spread and noise in the data, as you might expect, because many of these companies were only 10 years old. And and part of the conceptual framework behind scaling is that it often represents, as I mentioned, talking about biology, something that's being optimized in the system. And you know, life has been around a very long time. And so, you know, all these feedback mechanisms in natural selection have led to very good scaling. And if you look at those graphs, they line up beautifully. If you look at cities, well, they've only been around hundreds of years, some a few 1000. Maybe. So indeed, there's more variants, but still very good. You look at companies 10s of years, well, lots of fluctuate, because they have an optimized system doesn't optimize very well. But nevertheless, it does show good evidence. But the thing that comes out of it that is so interesting, is that that the scaling is much more like organisms, that you you've already mentioned this, that it is like cities, namely that it's sublinear, rather than superlinear. And when it was sublinear, as an organism's, what did that tell us? That said that? as they grow, they stop growing, they stabilize. It's called sigmoidal. growth, technically, because it looks like a Greek sigma. They stopped growing, and then they die. I mean, that's the kind of life history of an organism roughly. And that's what the theory explains. Whereas cities, you have this superlinear behavior that leads to open ended growth. And it's not clear if it's about mortality. I mean, cities don't die, roughly speaking, I know, people will yell and scream, of course, you know, cities die. Well, yes, they are ancient cities and their ghost towns sprinkled around. But the vast majority of cities that have ever existed, you know that serious cities still exist, you could drop atom bombs on city

25 years later, they're fine. You have a small fluctuation of the externalities in the stock market, and you lose TWA, you lose Lehman Brothers, you're gonna lose with with effectively losing Sears. And so it doesn't take much cities are fragile cities, I'm sorry, our robust companies are fragile. And, and so the growth curve of a company mimics much more the growth curve that you and I had grow quickly, and then you stabilize, where cities keep growing.

Nick Jikomes 1:28:42

And speaking of speaking of the fragility of companies, one of the astounding facts in this section of the book for me was that apparently, the risk of a company dying does not depend at all on its age or its size.

Geoffrey West 1:28:57

Yes, that is what's called the mortality, which is the relative rate of death doesn't change with the size. It's, it's, it's the same what is remarkable what we discovered, which was surprised that actually, was that if you looked at mortality of companies, it looks like, you know, in other words, if you take a cohort of companies at some particular time, and you ask, how many of these are still around 10 years, 2030 years later? It obeys the same law as taking a bunch of radioactive atoms, and asking how many of those are left to decay randomly, you know, and it just is an exponential decay. And that's what companies do, which is remarkable. And we don't fully understand it. When what's going on here. It's like, it's again, one of these things That, you know, when you think of companies, they seem so individual and so dependent upon a particular niche, and particular, you know, who, and they make a big deal about, you know, the CEO being so brilliant and so forth, right. And yet, somehow, when you average over all of them, it's just like a bunch of atoms decay, you know, it's not. Now, of course, like everything else, and I haven't emphasize this enough, throughout this, there are always outliers, and they're always variances. So just as I said, you know, when I said there be, you know, animals or cities obey those scaling laws, I tried to say, at the at 90% level, the individuality of the organism, the individuality of the city, in this case of the company, is the rest of it is that other 20% 10 20%, the individuality meaning the history, geography, culture, and so on. So it is with companies and companies have more of it much more variance, there's much more room for being individual, so to speak. But the work that will be done shows extraordinary regularities. Really, considering, you know, the system we're dealing with,

Nick Jikomes 1:31:21

one of the things I got to thinking about, as I was reflecting on your book, I considered the difference between organisms and cities, the differences that we discussed. And at first pass, you sort of say that, you know, companies are more like organisms, they're finite, they're mortal, and they die. And then I started to think about some modern companies, especially the bigger tech companies, the Amazons, the Googles, the apples. And, you know, I'm here sitting in Seattle, and Amazon essentially has taken over entire blocks of downtown Seattle, they have campuses where you work and you live effectively, get your laundry, and your haircut at work. And so are some of these companies becoming more city like in their organization? And could that potentially allow them to unlock the potential for open ended growth?

Geoffrey West 1:32:09

Well, this is a question that comes up a lot, actually, when I discuss I do talk to people in that world. And indeed, in the very world that you just talked about, maybe Amazon. And well, let's have a slightly awkward ground here. But the the, I'll say this, that there are some CEOs of companies and Bezos being one of them, that understands this, this phenomenon we've just been talking about, and the finiteness of competence, that companies are fragile, and have a finite lifetime. And I didn't say By the way, the data tells us that the sort of speed, the average lifespan of a company in the United States that's publicly traded, that means it's already gone through the gestation period of posting on the New York Stock Exchange, for example, is only 10 years. That's all you can expect of a company. Now, some companies may live 100 or 200 years, of course, there's great variance, and some may go bust after one year. But and so the question is, you know, one of the things I speculated about, and it is speculation, because we don't have the data on the internal mechanisms of companies. So it comes from both reading case studies and talking to major CEOs of major companies and others in their administration, is I've speculated on why this happens, why is the company has died because they, first of all, even though they may behave like organisms, in that sense, in the sense, we just described, that as they stopped growing and then die, the mechanism is different. It's not, you know, blood fly officer is wearing out the offer. And so although there might be some analog to that, it's much more, I believe, to do with the change that inevitably happens to a company as ages. And that is going from something but small, and can move fast, is dominated by ideas is dominated by, you know, ideas for its product, range and space, and is very innovative typically at the beginning. But then as it grows, inevitably, usually anyway, the product space narrows because it has to respond to the market so something's wrong thought we're very sexy and innovative, actually doesn't sell well, you can't go on producing it. Whereas things that you thought was somehow mundane, like Bezos and Amazon, something that was a side issue, you know, doing this stuff on the web. And so it turned out to be the thing that's made in essentially the richest man in the world, which is extraordinary. So you know, you have to respond to that. But, of course, what that does, in the vast majority of cases, it locks you in.

And so you get locked in, you know, a certain way of doing business, certain product space, so narrowness. And that gets coupled with the more boy in encroachment of bureaucracy and administration. But it's inevitable, because you're getting a bigger company, you have to have, you have all these, both external laws that you have to obey, you have to pay your taxes, you have all these HR laws, or the safety laws, then the company itself starts imposing its own laws, to make sure those laws are and be obeyed. And so now the whole thing builds up. So that we have this image of the company, that it's basically the bureaucracy of administration. And the innovative productive part becomes kind of secondary to that. I mean, that's sort of a cartoon version of what happens. And what that means is that it becomes somewhat ossified. And when the externalities change, the company cannot adjust. Or if the competition, something new happens, competition comes, comes along. You're, you're you can't move the battleship fast enough, and the company goes on. And that's the typical kind of life history of companies, successful companies that become very large and unknown, that are unable to adapt, simply unable to adapt fast enough. And so now, cities are quite different, even though they're also a socio economic organization, because they're really not top down. Of course, they have to every ministrations of bureaucracy is clearly something to manage much of the infrastructure, but cities are quite the opposite. They're open, they're open ended, they are great cities, allow new things all the times to big time to be developing. New York is maybe the greatest city in the world. Because when you go there, you feel you can accomplish anything. I mean, that's sort of the image, you know, to leave the farm and go to New York, because I can be free and explore, I can become a great dancer, I can invent this new thing, and so on.

Nick Jikomes 1:37:48

So anyone can move there,

Geoffrey West 1:37:49

anyone can move there. And that's Silicon Valley took over that within, you know, the button paradigm that we have. And do you know, I often say this, and it's nice prep, maybe I shouldn't. But, you know, when you go to New York, one of the things that you see in San Francisco is another innovative city, homeless people, Seattle, oh, my God, yeah, homeless people. And it's all fallen terrible. And crazy people walking on the streets, several people talking to themselves and so on. And the thing that's amazing about that, is that they provide a boundary for the rest of us, we are sort of free to move right up to that edge of being sort of crazy and different, and not fitting in quite, quite, quite in the right way, and so on. It's, it's it's symbolic and metaphorical. But you can't do that in a company. You don't have the analog to that in a company. Companies are extraordinarily intolerant, of having people that are different, that don't quite fit, that are bucking the system that aren't doing what they're told, and so on. And even Google, which presented itself as that image, you know, the, the, the MIT graduate living in the basement, who never washes and so on, you know, that was kind of the image they projected, and they could do anything regarding that. Right, that was never really true. might have been sort of true at the beginning with Google, actually. But now it's sort of like many other companies.

Nick Jikomes 1:39:27

Go ahead. The HR function of a company is very much designed. And it's one of its primary purposes, other than just keeping track of who's hired and how much they're being paid in those things, is really to create a culture a set of rules. everyone follows.

Geoffrey West 1:39:42

Absolutely. And, of course, silly subcultures, but it's so gray and so broad, relatively speaking, and so a great city, you know, people are attracted. Innovative cities are ones that have allowed innovative people to come and create And, you know, and I think that's, and it's very hard, I think that is the role of mayors and administrations ought to facilitate that in a way that is constructive because it can become destructive. But, and but many cities have been extraordinary in doing that. And companies have been terrible at doing that. And they want a mandate. They freak out, when things get rough, and they realize they haven't innovated, they haven't allowed things to develop, and so on. Now, one of the things that's developed in recent years, it was always there, but much more. So is that especially in Silicon Valley, IT companies is buying up companies, of course, to bring in that innovation. And I think that's only been marginally successful. I don't think that's been, it's not organic. It has worked. I mean, what Facebook bought Instagram, for example, didn't invent it. But, and that's presumably been successful. But many of these other little companies, they bought up, and sometimes they buy them up, because in a traditional way of suppressing new ideas, rather than innovating as they feel threatened. So it's a complicated process. But now I want to go to the very hub of your question, which is, the Amazons and the Googles, and so on, that provide something that cities are provided in the past and present to of course, they provide a certain infrastructure, I mean, Google was the first to do this, in providing, you know, childcare, and free meals and sort of you can always sleep there and live there, and they were having maybe some problems, even people sort of moving in, and so on. And Amazon has now done it, as you say, in Seattle, by building, you know, these housing for people and so on, which other companies have done, of course, now, I don't think that's gonna do it. I don't think that is, it may have some of the trappings of a city. But I don't think so I don't think it will have the essence of a city. And I say that, because in the 19th century, I mean, that's what companies did, you know, I mean, especially industrial companies, coal mining companies, after all, I mean, here, there are towns, of course, a company, I mean, that the set the company town, the company's store, so they were but you know, they didn't survive in the end. And I think that's going to be true of, of these IT companies. That is, they will not survive because of this, they will only survive, if they can somehow which is extraordinarily difficult. Encourage and engender the what a city can do, which is this much more open, transparent way of allowing sort of row people to move in. I mean, that's how Silicon Valley started outdraw. I mean, these were the people with that weren't going to go to IBM, or whatever, who had crazy, you know, what was, might have been considered crazy ideas at the time.

Nick Jikomes 1:43:35

But this does sound like you know, you I immediately think of Google's moonshot lab, they've essentially taken a pilot. Yeah, they essentially take a pile of money, say, You people go over there, don't break anything, but feel free to just tinker with whatever you think is interesting.

Geoffrey West 1:43:53

Yes. And I think that's been mixed. I mean, husband, I, there's no question that that's, that's stimulated by these, this kind of idea. That can you have, either, you know, a piece of you, that is unfettered or better still, would be to spawn a company that, in fact, is completely independent of you, somehow, which is, which then kind of means that if they're independent of you, so the question is, how much independence do they need in order to really be self, you know, self sustained in their innovative qualities without feeling beholden? Because there's a big psychological side to this, you know, as being part of and I know that from people at Google, I mean, that's highly non trivial. And so I don't know, I don't think there's a simple solution and it is If I had been thinking, by the way, that this was not possible, that is you can't actually have a company operating in the market in the free market system that we have that it operates like a city or even a piece of it, that it just is not going to work. I have backed off from that I that I may, I may be well be wrong on that. I don't know the jury's out. There aren't enough examples. And I'm not at all sure the company is. Even if they do things like the Google moonshot really able to let go. They're very much like parents, and children. And, you know, they always wanted them to be visiting normal the weekend coming for Sunday dinner, right, giving them presence. And so I think it's not so easy.

Nick Jikomes 1:46:03

Interesting. So one of the speculative. I mean, I don't I don't know, one of the last questions I want to ask has to do with the city's innovation and COVID. So there's been a lot of in the wake of the COVID pandemic, there's been a lot of immigration, emigration from some cities. I haven't seen that much data on this. But it seems to be happening at some reasonable level, people are moving out of certain cities that we traditionally think of as innovation hubs, and moving into new cities that we don't traditionally think of as innovation hubs. Do you think that that's happening very much? And what do you think the nature of, you know, how does a New York become a New York and attract the innovative people? Do you see anything interesting happening with respect to the COVID pandemic and its aftermath?

Geoffrey West 1:46:48

Well, of course, we're way too early, needless to say, to know what the long term effects of this are going to be? Certainly, the sparse data that I have seen, is actually not that impressive in terms of the hype that's been made out of it. Maybe yes, I mean, I'm in Santa Fe. And, you know, there's been a big influx of people from California, Texas, even, and so forth, New York. But what is the live flux mean? It's hundreds of people, actually, because we're a small town. And the number of people really leaving San Francisco in New York, is actually not that big, I think we have to wait to see this other big effect that we don't know, is, you know, going back to offices, and nine to five, or one of eight to five, or whatever, you know, we don't know quite yet. how, you know, well, the system, just slowly relaxed back to what it was effectively, with some, you know, bells and whistles to do with being on zoom occasionally, or will it in fact, become this kind of hybrid model of going into the office a few days a week, or not at all, in some instances, and doing it all electronically. I'm, you know, a lot of things work very well with zoom. I cos hate zoom, because I'm old. But I mean, two dimensional and so formalized and so structured, so now new, new applications will develop, you know, I mean, it will improve enormously, I'm sure. But in the kind of work that I do certain things work fine on zoo, you know, if you have collaboration going and something going you can keep, like keep it going and do good thing, but to do something truly creative and to start new things. It's very hard. And nothing can replace being in three dimensions in the same room, so to speak with a blackboard and bullshitting. nonlinearly you know, brainstorming and so on. And you need some of that. And companies. Yes, the 90 something percent of a company doesn't need that. But a few percent does. And that's how a company dies when that few percent goes to zero, and then it dies eventually, because not creating anything doesn't realize what's going on. So I think many companies will realize this, I think, and I think many people will feel more comfortable actually going back to work a traditional situation. But I my intuition is that there will be a change. And there will be these kind of hybrid things, but it may not have as big an effect as we feel at the moment. And and therefore, how much it will affect people leaving the city? And therefore, the evolution of the city? I'm not, I'm not sure I have my interest in is what affects city much in the long run. Because it's not cities are there for work? Yes. But Therefore, as I said, for interaction for bringing people together the buzz of New York is there, yes. Because what we said earlier, there's enormous opportunities, then the feeling that you can do all these various scenes, but it's also there for the sexy buzz of the city that, you know, all the interesting things that are going on, and it builds it all builds on each other. And, you know, it's, I think that's hard to replicate in many places. So I think there's a that people have a need for that, especially young people.

Nick Jikomes 1:51:05

Well, Jeffrey, I want to be respectful of your time. Thank you for joining me here. Are there any final thoughts that you want to leave people with in terms of your book? And is there any place that people can go to to follow your work?

Geoffrey West 1:51:19

Well, I do write things I write essays occasionally, I don't know, I bow. I struggle with writing, so I don't write much. But I would say, you know, one of the reasons I wrote that book for several reasons. One was, because I am became I was becoming increasingly concerned. And this is, by the way, prior to Mr. Trump, the lack of respect and understanding and the role of science in society, and how crucial that is, and that we are losing that input in a dramatic way. And this was before Trump and Trump just put a huge exclamation mark after that, because he introduced something that I thought could never happen. That was that, actually, you know, facts and science and truth, in a certain sense, don't matter, you can sort of bend them, when other way kind of suits you kind of that was the image anyway, that's been projected. And we've had some terrible results from it, like, you know, several 100,000 people dying unnecessarily. And so it has huge implications. So I wrote the book, because I wanted to write a book of science and about science and a way of thinking and this was important, a way of thinking that does that we don't integrate into society, it's not part of our political process. There. I don't know how many members of Congress there are. But there's, I think there's only one, maybe two, that have any inkling of what science is about. Yet, the whole of society runs because of science and technology, and all the things that are legislating on all the decisions they're making, actually, in many, many cases, have initiation in science and technology, and they don't understand it. And I find that distressing. And I find that a problem of some urgency because all kinds of bad decisions get made. And of course, part of that is drip was originally driven by the fact that people don't understand that. We're about the whole question of climate change, but much more importantly, the whole question of sustainability. And, fundamentally, I began to realize, as I wrote about this book, that most people even in Congress don't understand what an exponential is, even though they use that word, colloquially, all the time, that the extraordinary implications of exponential are just simply not appreciated. So I wrote it for that. And I wrote it also, because I wanted people to see something which I hope comes across a little bit in the book, the extraordinary interconnectedness of everything, and the extraordinary spiritual beauty that you can get from understanding and that's what I get from it is that I stand in awe of that in terms of the world around us. And that underneath the, you know, extraordinary diversity and messiness, the, you know, that is around us underlies an extraordinary regularity and simplicity and once You know, I want people to appreciate that, because it's beautiful. And it wants me to go on living. I'm 80 years old, but I want to live because I enjoy that.

Nick Jikomes 1:55:13

Well, Geoffrey, thank you for your time. I think that's the perfect place to end. And I look forward to talking to you again at some point.

Geoffrey West 1:55:19

Yes, Nick, thank you so much for having me and I enjoyed it can do Christians were terrific, by the way. No, they were really good, because you're obviously given a great thought, you know, whatever the bits and pieces I wrote. So thank you very much for joining me.


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