B (3:36)

That gives you pause about AI, of course.

A (3:39)

Well, I'll tell you why it's not wrong, which is that a lot of what this show is about is reaching beyond the boundaries of who would traditionally talk about work and bringing in a perspective that we've never had before. We're reaching pretty far today, and I'm very excited to do it. So your work and your book Scale, you have applied approaches from physics to biology and in particular to complex systems, and found patterns that recur across incredibly diverse systems, and they affect us as humans in hundreds of ways. Eventually, by the end of today's discussion, we'll get to how they might affect us at work. But let's start with from why you expanded in the first place from physics, and not just physics, but cosmology to biology. What caused that to happen?

B (4:37)

It's, of course, a long story, and I'll try to keep it brief and just tell you the salient points. It was a combination of a couple of things. One was, in fact, maybe the major one, you may recall, and your listeners may recall, that in the 90s, there was this proposal to build a huge particle accelerator called the Superconducting Super Collider in Texas. And it got funded, and then it was going to cost at that time, 10, $12 billion. And it was going to be this humongous thing, much bigger than the one that we sometimes hear about from Geneva, CERN in Geneva, the Large Hadron Collider, and so on. But during the Clinton administration, there was sort of one of those periods of anti science and in particular anti physics, which we're going through again now, of course, in spades, much more generally. But then it was much more focused, and it ended up with the defunding of the Superconducting Super Collider after, I think about $3 billion had already been spent. It was one of those great absurdities. But part of the anti science thing, and in particular anti physics thing, was that we heard those of us involved in it time and time again, this statement that physics was the science of the 19th and 20th century and biology is going to be the science of the 21st century. And the corollary often left unsaid, but sometimes was said, there's sort of no need to do any more basic physics. We know all we need to know. So let's get on with the science of the 21st century biology. So my reaction, which was probably not atypical of some of my colleagues was that, yes, I mean, in some ways it's pretty obvious that the 21st century will be a century of biology, and indeed it is in many ways. I mean, many other things, although ironically, it's been totally overshadowed now by AI and so forth. But my reaction was, yes, maybe that's the case, that biology will be the science, the dominant science of the 21st century, but biology won't be a real science unless it starts to somehow integrate at least some of the culture and technical ways of thinking that have been so successful in making physics the prominent and dominant science of indeed the 19th and 20th century, and indeed has led, one could argue, to the extraordinary technological society we have. So I said that without knowing anything about biology, it was totally arrogant, ignorant, but it was this sort of gut defensive reaction. By the way, just a tangential point. Stephen Hawking was asked this question at the turn of the millennium and he responded to that question, no, the dominant science of the 21st century will be complexity, which of course I agree with.

A (7:54)

Yes. And so much of biology falls into that category. And it's a broader category than biology.

B (8:01)

Exactly. So it was sort of broadening in. And that I think I certainly strongly agree with. But I was sort of hanging around twiddling my thumbs after this demise of the ssc. And one day, having shut my mouth off about this too many times, I thought instead of just shooting my mouth off, maybe I should actually think about it. Maybe instead of just saying biology won't be a real science until it encompasses, maybe I should try to do some biology and actually illustrate that. So that came together with a personal phenomenon, and that is that I come from a family where males do not live very long. My father died when he was 61, his father died when he was 57. All my uncles died in the either 50s, 60s, one reached 70. But on the grand scale of things these days, those were considered somewhat premature. So the point of that is that I was in my 50s at that time of the SSC demise, roughly early 50s. And I thought, well, with luck I have 10 more years. And I thought, what is that? First of all, it sort of brought to mind, what is death? And why do I concern that I should only live 60 years? And why is it that no one I know actually is going to live more than 100 years anyway? Where does that come from? So that's a biological question. I put those two things together and thought, maybe I should think about that and see if I can figure it Out. And that led me, unbeknownst at the time, to a complete change in career, unconsciously, I would say.

A (9:48)

And how are you received by biologists? And I bring this up because we have a family friend, somebody you might have even crossed paths with, which is Eshel Ben Jacob, different branch of biophysicist. And my father was a geneticist and Eschel was a good friend of ours. But, you know, my father would always say, sort of say off to the side, yeah, but that's not biology, which I understand, which is that if you're looking at the emergent properties of something, that's a lower level thing, a lot of times you can't find knowledge in the lower level thing that describes the emergent properties. So I sort of understand it. But I also know that there's a polite. You don't really understand our field.

B (10:33)

Right. And that's natural. Of course, someone coming from the outside saying that they can understand something that people haven't understood for a long time is extreme arrogance, I would say, or could be interpreted as such. So in answer to your question, of course, it's a huge question and is ongoing, actually, even though I'd been doing biology now since really, I suppose, the mid-90s, so 25, 30 years. So what was fortunate for me was that I was serendipitously brought together with a very distinguished biologist, an ecologist man named Jim Brown, who actually, right after we met, became president of the American Ecological Society, I guess it's called the major professional society of ecologists. He was an established man. So let me go back one second as to why this connection happened. So when I was thinking about aging, I realized that, you know, if you want to understand how something ages and then dies, whatever it is, you need to understand first of all what's keeping it alive, what is sustaining it, and then try to figure out what the hell is going wrong that it can't sustain indefinitely. So of course, what sustains us is called metabolism. We eat and use that energy to keep ourselves going. So I had to learn about metabolism a little bit. And then I learned, which I sort of vaguely knew that. And here was the real crux of the matter, that metabolic rate, which is the rate at which you eat, which you intake food. There were this scaling law that had been known since the 1930s called Kleiber's Law, which was that if you plotted metabolic rate of animals versus their size as denoted by their weight, if you plotted on a logarithmic scale going up by factors of 10, they all lined up on a straight line, the simplest possible thing you could imagine. And when I saw that, I thought, that's absolutely amazing because here's probably the most complicated process in the universe, and yet it's manifesting something that's in some ways the simplest possible mathematical form you could imagine. Straight line, albeit on a logarithmic plot. And I thought, that's fascinating. But then I learned very quickly that in fact, it wasn't just metabolic rate, but almost anything that you could measure. Physiology or in life history, about organisms and across the biosphere, roughly speaking, had this same property that you plotted logarithmically. They had these very simple behavior. And I thought, my God, first of all, I thought, obviously there's some deep explanation someone has got. And I discovered very quickly that all this stuff was known phenomenologically, that is, in terms of the data, but there was no explanation. And I thought that was kind of remarkable. Because if you believe, as we presumably all do in evolution by natural selection, then every aspect of an organism, every subcomponent of it, but every characteristic, everything you can measure about it, is historically dependent. Everything depends upon its evolutionary history. Therefore, since everything is historically contingent and relies on all kinds of accidents that have happened, all kinds of different environments that it's evolved in and so on. If you plotted anything, you would see these points sort of scattered all over the graph, of course, reflecting that evolutionary history. Quite the contrary. You see everything lining up beautifully. And so I thought, obviously, this is extraordinary. There's some extraordinary constraint here. And by the way, one of those characteristics was longevity. It has a lot of variance in it. Nothing lines up. Obviously, when I say everything lines up on a straight line, everything deviates a little bit from it. But it's kind of extraordinary how well that agrees with a straight line better.

A (14:44)

Than most of my data ever comes out. I mean, yeah, there's a little bit of jitter in there, but some of that's probably data collection. I mean, it's very linear.

B (14:52)

Absolutely. So that's what set me off on thinking that there's something here. And also I discovered, by the way, the other thing I learned as I started getting into this and that gave me encouragement, was not just that, but I discovered that longevity on aging and mortality was sort of backwaters of biology and even biomedicine. Obviously, there were people working it, people had been working on it, but it was a very tiny, specialized niche that wasn't central. And in fact, you can see that if you pick up one of these big fat books on biology, if you look in the index for mortality or aging, it isn't there. And that blew my mind, frankly. I thought, here's what I consider the second most important event in an organism's life. The first being its birth isn't even mentioned. That sort of encouraged me because I thought, that's great. If no one's worked on this or very few people are working on it, it hasn't been solved. Here's all this data. Fantastic. So I concocted this idea, this very simple idea that what's keeping you alive is metabolism. But the way that metabolism works, the energy has to be distributed throughout the system through networks. And somehow it's the dissipation of energy in those networks and the wear and tear that's gradually wearing you out, even though you do repair. And so that was sort of the simplistic idea. And that's what I worked on, and I claimed to have solved the problem.

A (16:31)

And where you arrived, and this was in partnership with the ecologist you mentioned?

B (16:36)

Well, it wasn't a first. For the first year, I worked it on my own. And then I was brought together with this man, Jim Brown, his then student man named Brian Enquist, who's now himself a very established ecologist through the Santa Fe Institute, because Jim had become associated with it and had said he was very interested in these scaling laws and wanted to talk to a physicist that might know something about them. And it just so happened, not only did I know something about scaling in general, but I was actually working on trying to understand the scaling laws in biology. And at the time I met Jim, I had already developed the theory. And what was great about that collaboration was we came together and I could get all the biology straight, because I knew no biology, of course.

A (17:30)

And in the book, you arrive at an analysis of how the circulatory system branches as it moves throughout the body. And I thought it was a sudden transition in the book, which is to say, it seems like you would thrash around a while of all the possible things that could be driving scaling laws before you land on the circulatory system and how it functions. So was there a thrashing around, or was that something that you arrived at fairly quickly?

B (18:04)

No, I arrived at very quickly, actually, surprisingly. I mean, the thing that's amazing about these scaling laws is that it was true whether it was mammals, birds, fish, or plants. And I thought, that's pretty weird, you know, I mean, they're very different engineered designs. They're in very different environments. They function differently. So what in the Hell is common. What could be common among them that leads to similar scaling laws? And maybe because of my physics training, it was that it must be something to do with the distribution of energy and how energy is distributed and energy is distributed through these hierarchical networks, the most prominent being the circulatory system.

A (18:51)

So before we go on, I want to describe to listeners. I'll describe a few. You can describe a few of the more surprising scaling rules. One of them is that whether you're a shrew, which is the smallest mammal there is, or an elephant, maybe even a whale, the number of heartbeats in your lifetime is the same. And the heart of a very large animal beats very slowly. And the heart of a shoe is like 1500aminute or so. It's like, incredibly fast. And yet across all of those organisms, the number of heartbeats is the same, except humans. But to the extent that the heartbeat is a proxy for metabolism, it's saying you only get so much. Metabolizing, I guess, is the right word. So that's the one that blows my mind. Are there any particular ones that you might call out?

B (19:44)

Well, that one does, and that one is predicted by the theory. Everything you said is both predicted and explained by this theory. I mean, of course, it's huge. Lot of work has to go into. Has to be all the physics and all the mathematics has to be put together. But out of it comes these extraordinarily simple scaling laws, including this really remarkable one. The number of heartbeats is an approximately invariant shrew that lives one to two years to a whale, a blue whale that can live 125 years or so, that is predicted. And by the way, just again, a side comment, when I started thinking about this whole problem from the view of aging immortality, it wasn't just why do we age and why do we die? But I then took a physicist view. What in the hell sets the scale of 100 years for a human lifespan? Or put it slightly differently, one to two years for a shrew or two to three for a mouse. Where does that come from? Especially because we all basically have the same genes. Actually, we're all mammals, and we all, roughly speaking, have. So why is that anyway? And this explains it, this network dynamics explains it. By the way, the other invariant in terms of lifespan is the amount of energy that you use per gram over a lifetime. It's kind of amazing. To support the same gram of tissue in a shrew for its whole life is the same as that for the same gram of tissue for a whale. It's kind of amazing. So I know this is a weird thing to introduce to it, but it was very spiritual. That wonder of nature. The wonder of nature and that you could explain it was just a wonderful combination.

A (21:41)

One of the questions I kept having while I was reading the book, I want to foreshadow for everybody, just so you're not wondering where the hell we're going. Many of these scaling laws apply to cities and many of these scaling laws apply to companies and it affects the longevity of companies. We work in companies. That's where our work happens. That's where we're going. But before we go there, one of the questions I had was, was to what degree are the laws hard constraints that animals cannot deviate from versus optimizations, which is evolution could have picked a lot of different things. It picked the optimum thing because the optimum thing was one thing. They all picked the same optimum thing. And so the reason that that's going to matter in the long run is that if it's law as opposed to optimization, then it's much more inescapable. And so it may be different for different parts of the things that you measure. But what's the.

B (22:39)

This is a very important question. It's not a law in the sense of Newton's laws, for example. I mean, Newton's laws are, quote, immutable, you know, or quantum mechanics or fundamental laws of physics. It's a law in a statistical sense. I would say maybe that's a way of putting it, or coarse grained, meaning that so the theory, the theoretical structure is very idealized. So this is a result for an idealized organism having certain properties which we don't need to go into here. But one of those properties is related to what you just brought up is that, for example, we talked about the circulatory system. One of the things that goes into the derivation is the following. That of all the possible circulatory systems we as mammals have or could have in any mammal of any size throughout evolutionary history, anything that's called a mammal, the ones we could have, the ones that have evolved, are the ones that have been optimized in the sense that we have a circulatory system that minimizes the amount of energy our hearts have to do to pump blood through the circulatory system to supply oxygen and resources, energy to cells that sustain us. So it's a huge assumption. That's one of the starting points in the derivation. But the idea is that it is a proxy for what's called Darwinian fitness because the idea is that you want to minimize the amount of energy an organism has to do to stay alive to be sustained in order to maximize the amount of energy you want to put into sex and reproduction and the rearing of offspring. That's Darwinian fitness. So in my mind, it's a restatement of that into physical terms. So the scaling laws work at a level up to sort of 1890%. So one of the questions which the theory does not address, could, in principle, and we've thought about it, it's something that needs much more work, is the question you've asked. How much can you violate this law? How far can you deviate? Because as I said earlier, when you look at the data, there's this beautiful straight line predicted by the theory, and all the points lie very close to it, but everybody lies a little bit above or below it. And that above and below is very much the expression of the individuality of that particular species.

A (25:29)

Do you happen to know, if we look sort of lined up our heads and look down that line, what the sigma is of the Bell curve?

B (25:38)

Yes. One of the things you discover is it's not Gaussian. The distribution is not Gaussian. It's what is called Laplacian. It's exponential rather than E to the minus X instead of E to the minus X squared. Those familiar? Very roughly. So it's not random, it's something derived from the data. How far for a given size of organism. You can deviate, by the way, we've done that for companies, too.

A (26:08)

Okay. I think it's, by the way, just a little bit unfair to say it's only a law if it draws a straight line with no variation. Because to me, a law that sets boundaries that are highly enforced is a law. Just because it expresses itself as a range doesn't mean it's not a law.

B (26:28)

You could state it more rigorously if you wanted to have a more rigid view of what a law is, but you could state it that an organism satisfies this scaling law, but with this much variance. And you could write down the equation for that. You can write it explicitly. So that makes it a little more rigid, if you like. By the way, one of the things we didn't say in this is that for the metabolic rate in particular, the slope of that line is 3/4. Turns out, I mean, the theory predicts it's 3/4, and the data strongly confirms that. And what is remarkable is that the scaling laws for all these other physiological and life history events, life history characteristics like lifespan or how long you take to mature, but also mundane things like the length of the aorta, et cetera, et cetera. All of these, when you plot them, as I've said earlier, have these simple straight lines on log, log plot, and they all have slopes that are simple multiples of one quarter. And that's what the theory predicts, and the theory predicts where that one quarter comes from, et cetera.

A (27:41)

In fact, your law is called quarter power law. And so, I mean, it's one of the things that biology doesn't really do, I think, is that you've got one heart, you've got a trillion cells. That's a tough distribution problem. And it's an engineering problem. And that's not. I don't think, having grown up around biologists, that the way biologists think of that is how would I engineer that to be optimum? So I want to go on to cities, and then I want to talk about companies and I want to talk about cities first, because they're different. I mean, they have power laws, but they behave differently. How do cities, as they scale, change in terms of efficiency, in terms of innovation, things like that?

B (28:26)

So after this work was done, this initial part of this work, it was natural to think of other kinds of systems and cities, an obvious one, especially because in the past, people have sort of used metaphorically the idea of a city as an organism. That was very natural. And unlike the biology in biology, much of the data had already been collected during the 50s and 60s and codified and so forth turned out with cities. When you started looking at cities, almost very little work had been done on looking at how cities scale relative to each other, which is quite surprising, actually, given the huge amount of work that's been done in urban geography, urban economics, urban design, and so forth. So one of the first things we did was simply to try to gather data on various characteristics of cities. So everything from infrastructural characteristics, the ones that are sort of analogous to the things we've been talking about in biology, like the circulatory system, so roads, electrical lines, water lines, buildings and so on, all the sort of the physicality of the city, which is closer to sort of the biology, but then this other part of a city, which is actually the essential part of the city, which is its socioeconomic activity, meaning, for example, wages, average wages, a number of patents produced, amount of crime, amount of disease, et cetera, et cetera. Anything that is socioeconomic and has no simple analog in biology. So to summarize, what we learned was that the infrastructure, which was the first thing we looked at, like the length of all the roads or the length of all the electrical lines and so on, we discovered when you plotted that in a similar way, this logarithmic way versus the size of the city and that we used as population, we discovered similar scaling law. They get plotted logarithmically. Lovely straight line, greater variance, may I say, since we just talked about it. Nevertheless, they did. And what we also discovered something similar to biology, a kind of universality, that if you plotted how one of these urban metrics scaled in one urban system, the United States, and then looked at it in Argentina, it was pretty much the same. So to summarize all of that, we discovered that essentially all infrastructural characteristics of a city in a given urban system scale with size very similarly to the way they do in biology, not with a slope of 3/4 that was the difference, which was 0.75, but with a slope 0.85. And that seemed to be, in terms of the data we could lay our hands on, whether it was in North America, Central America, South America, Europe, Asia, China, Japan, et cetera. Everywhere we looked, this 0.85. So there was a very similar kind of structure being expressed.

A (31:42)

I want to draw a picture in everybody's head. What that means is that as animals grow, the line curves slowly more flat. But as cities grow and scale that the line curves up. And so what it means is that there are certain things that as cities get bigger, they do a lot more of than just linear.

B (32:08)

Yeah, so that's the socioeconomic part. Yes, that's the part to do with us in terms of interactions among ourselves, our social interactions.

A (32:21)

Something weird happens when you go to cities, which is that especially the socioeconomic part, there's physicality to the circulatory system that happens in three dimensional space. We're going to optimize for that the networks between people. And many of these things that scale super linearly in cities have to do with connections between people. And it's interesting because they're good things and bad things. More patents, more disease. Why, presumably more contact between people. And so that network, that's my problem. I don't know how to visualize it.

B (32:58)

There are two kinds of networks in a city. There's the physical ones that we've just mentioned, the roads and the power lines and so on. And they are the ones that are. And that gets to biology. Then we have what you just described in the socioeconomic ones. We have this phenomenon of the bigger you are, the more you get per capita. So you have more disease, more crime, the bad side, but you have the good side. More patents, higher wages, more activity in general, more fancy restaurants, more educational facilities. But the extraordinary thing is all of those, the good, bad and the ugly, all scale together in the same way. Why? Because what you just said, because they all derive from the interaction between human beings, the social networking between human beings producing these. The fundamental dynamic that leads to that is that when people come together, like we're doing now, but in a city, in a physical space, we interact with each other and we build on each other, we talk. And they may be trivial and useless ideas to almost everybody else, but we are continually building on ideas. And the city can be thought of as a machine for facilitating and encouraging that. And so the bigger you are as a city, the more people, the more interactions. Therefore more ideas, more innovation, a greater buzz, but also more crime and disease, all to the same degree.

A (34:39)

Animals die, cities don't seem to. Why?

B (34:43)

Well, it's all related to this because they're continually renewing themselves. Cities, there's this continuous buzz and innovation and it's not top down, it's not controlled by anybody. Of course you have to have mayor and administration. But cities, great cities, are extremely tolerant of all kinds of things. And in fact, great cities want to encourage new things to expand into new areas and so on. I mean, New York, the greatest city in the world, is sort of quintessential example of that. And that somehow is the dynamic that keeps the city alive, which is not happening in your body or mine. Those cells are not continually innovating to try to make new things. They're doing the same bloody thing for 75 to 100 years.

A (35:36)

And in fact, there's one of the graphs you show about cities where they might start to curve down, but then there's a new innovation. It reminds me of like swinging from branch to branch the way monkeys do, where you're like, oh, I'm going to fall. Nope, I've got another branch, I'm going to fall. And there's like this constant swinging from branch to branch that has to happen at an ever increasing pace.

B (35:58)

And that's the kicker. The price you pay for that sense of immortality or that parent's lack of mortality is that you have to innovate faster and faster. So one of the things that we didn't talk about in biology is the bigger you are, I think we intimated it. The slower things are you live longer, everything takes longer to do. You have sort of the image between the ponderous elephant and the scurrying mouse. But you live longer according to these scaling laws. Everything's the other way around in cities. The bigger you are, the faster everything is. Everything speeds up. Even the pace of walking systematically speeds up, the bigger the city. And one of the consequences of that is the pace of this renewal has to be faster and faster. The pace of innovation and of new things, this renewal, this rebirth that is continually happening. I mean, I hadn't been to New York for more than five years, maybe ten years, until recently. And just a few weeks ago I was in the city for the first time, just for a couple of days. And I was so taken. I live in a small town, Santa Fe, so I was so taken again, as I often am, by the extraordinary pulse of that city, the continuous activity, the fact that there are crazy people around and it's tolerated, it's almost encouraged in some weird way. And you know, that's extraordinary because that means that when you're there, you feel if you have an idea, you could do something, it's a psychological boost and you feel it when you're there, viscerally, I would say. And so that's what a great city does. But the pace of life is fast. I mean, walking down fifth Avenue two or three weeks ago on a Saturday, of course coming towards Christmas, you could feel it, you had to walk fast. And we can talk about what that leads to. That of itself is not sustainable.

A (38:10)

Hey everybody. Here are some upcoming events. On March 5, 2026 in Oakland, Robin Zander, the organizer of the Responsive Conference, is launching the first of a new series, the SNAFU Conference. It's about something that's important to all of us who are mission driven, which is how to sell yourself without selling out. Remember to use promo code elevenfold, that's eleven fold to get a significant discount for tickets to SNAFU. Also big event on March 20th, I'll be speaking at PX Live in London. Luca Mani and his remarkable community of PX leaders are getting together for a one day event. If you want to deliver an extraordinary people experience, this is the single best opportunity to meet kindred leaders. Thanks. And watch this space for announcements about my future speaking events. Yes, and so there's one thing I want to talk about before we talk about that. This is something that's bothered me about this from the very beginning that I have to get off my chest. And this is something long before this conversation I've been mulling over in my head. Animals are by default Dead cities are by default alive. You've said it different ways, but that's one of the ways I am worried that how I define something determines its durability. And so if I have an alarm clock and I hit it with a hammer, it stops being an alarm clock, it's dead. But if I have some stuff and I hit it with a hammer, it's still stuff. Now, if I had defined the clock as stuff and I hit it with a hammer, it would still be stuff. And so one of the things I worry about when we say that cities are hard to kill, hard to, by default alive, is maybe that's because we have a broader definition. It's more like stuff than it is like a living animal.

B (40:04)

Well, I would argue that, yes, I understand what you're saying, but you know, that alarm clock, if it's stuff and you bash it, it's still stuff. Indeed. But if it's an alarm clock, if you define it as an alarm clock, which has a function and what you might consider a higher function than stuff, it's no longer functional. So the point about a city continuing to live is that it's still functioning in terms of all the things that we think of, at least in a coarse grain. Where is defining what a city is? If you define the city just as its buildings, just as buildings, which you could, and many, by the way, that's one of the problems with urban geography. Sometimes that's a normal image of a city. It's just the building. Well, if you'd find it that way, of course, it's just stuff. Actually, really the only way it becomes a city is when you realize those buildings are there because of people. I mean, it's people that are the city. And then it has a function that those buildings are there to function for the people, to bring them together, to provide sustenance, to provide protection and so forth.

A (41:19)

This actually resolves it for me. You've resolved it for me, which is the people I know and love. I care whether they're alive or not, and I care whether I'm alive or not. And although if I die, I may still be stuff, something important that I cared about is not there anymore. And I agree. If you took London and you took away all the cities, but people were still doing the stuff they do, you'd have a living thing. Yes, if you kept all the cities but the people weren't there, it wouldn't be alive. And so that's the living part. I swear, I can't tell you how much time I've Spent on that question.

B (41:59)

Yeah, no, it's a very interesting question because I thought about that a lot when I started working on cities, because I had this standard image when I give talks and I introduce the city work, I show pictures of cities because that's what people think of as cities. You think of the skyscrapers of New York or whatever. You look at the Eiffel Tower, Paris and the Arc de Trio. But actually that's not Paris. I mean, that's the uninteresting part of Paris, actually. Because the interesting part is those are for the people. They represent something, they manifest something of the collective, of the people.

A (42:35)

Yeah.

B (42:36)

That's what gives it life.

A (42:38)

Christopher Alexander, architect, pointed out that what's important about architecture is the events that keep on happening there that are catalyzed by.

B (42:48)

Exactly. So they didn't catalyze by the infrastructure and by what architects do.

A (42:53)

Yes. So now we get to the crux of the conversation. Are companies by default alive or are they by default dead?

B (43:03)

That's a tough question. I would consider them alive. There's a piece of them that's biological, just like cities. There's a piece that's biological and there's a piece that's inanimate, and there's a piece that you could call economic, which is somehow even separate from that.

A (43:20)

I'm going to say it a different way. Are they mortal is really what I'm asking. Which is, cities seem to be, as far by the scale that we live by, seem to be pretty immortal. But how about companies?

B (43:33)

No, not at all. Cities. In fact, we did some analysis, just some data analysis, and we were quite shocked at first that just looking at the mortality of companies and discovered that the half life, that is, namely, that's the way we happen to do it, that is, if you took a cohort of companies at some particular time, within 10 years, half of them would be dead. And these companies, by the way, were already on the stock exchange. I mean, they were already mature companies. Your expected lifespan for a company is not much more on the average than about 10 years, even a successful one.

A (44:16)

So we're basically saying Chihuahuas. The half life of a Chihuahua is probably about 10 years.

B (44:25)

Yeah, it's probably true. There are, of course, and you can immediately think of huge outliers to that, clearly. But the interesting thing, I mean, I tell people, and I'm not the first to say this, go back to the Fortune 500 or whatever it is of even 50 years ago, and you'll be surprised how few of those companies still exist. It's quite surprising actually, is the mortality.

A (44:54)

Of companies associated with size, with scale?

B (44:59)

No, it turns out your chances of dying, what's considered the probability of dying is roughly independent of your size, which is interesting of itself.

A (45:08)

That's incredible. That means that Google's likelihood or Apple's likelihood of dying is about the same as the barber.

B (45:16)

Yeah, no, it's sort of weird actually. And in fact, first of all, we have to be careful here. You have to define birth and death. We defined it because that's the way it is defined in much of the literature. Most of the literature is that birth is when you post on the stock exchange, some stock exchange, and death is when you stop reporting sales and you're no longer there.

A (45:42)

So first of all, my barber's not in the data, so that's one thing.

B (45:45)

So it gets very complicated. And by the way, the data we examined was for publicly traded companies. So we do have. And we're just getting into it for non publicly traded companies. It's very hard to get a lot of this data. And that inhibited us for a long time. A lot of this has been difficult because the data that we have, if you trace it back, all comes from tax returns ultimately. And of course that's fraught with difficulties because you've had all kinds of accountancy practices and malpractices, no doubt, that have gone into it, and all kinds of tax avoidances, not necessarily illegal. So it becomes a little bit complicated. I have to say. It's not clean, but the data is unequivocal about of the order of a decade or so is the expected lifespan of a company that's already gone through gestation period. So getting a theoretical understanding of that is much harder for companies. Just go back to biology. One of the important things was to understand what is the internal mechanism that is going on inside your body that's keeping you alive. We know a huge amount about that cities. We also know a great deal actually. And it's in the public domain now. You come to companies and you know very little because it's all proprietary. You can't learn about what are the networks inside a company, who's really talking to whom, how does all that work? What is the social network, blah, blah, blah. And the best you can get is something that's not useful really. And that is the organization chart. That's about.

A (47:30)

Well, right. Which is like looking at the cities. Honestly. It's a stable structure that has nothing to do with flow and metabolism.

B (47:38)

No. And that's the whole point.

A (47:40)

And so here's what I'm going to say. It was true for elephants, it was true for cities in a different way. It was true for trees, it was true for lizards. We looked every place. We don't have a lot of data on companies, but they are complex systems. The lines were probably, if we had the data, there's a reasonable chance that we would find the same thing.

B (48:03)

Yes.

A (48:04)

And when we do have a little bit of data, we see some stuff.

B (48:08)

So by the way, what that shows is that companies are not like cities, even though they're social organizations. As we said, we've emphasized cities have this sort of apparent open ended growth which we could interpret as being approximately immortal for the time being. But companies don't. And part of it is to do with the fact that they can't reinvent themselves.

A (48:35)

Yes. And one of the things I considered when I was going through my stuff arguments with myself is that mosquitoes are mortal and are by default dead. But swarms of mosquitoes, pretty stable. Now cities may be swarms of companies.

B (48:52)

So that's a very interesting way of thinking about it. And I've thought about this quite a bit, that the companies come and go, so to speak. You could consider them on the grand scale as sort of the lifeblood of what's keeping a city going in terms of its economic activity. And they're all in the process of dying, actually. But of course they're getting rejuvenated. New companies are coming in, as are people, by the way. People are dying all the time and they're being born. And the city as an entity is kind of this metastable thing that sort of is the umbrella that encompasses all that activity that's going on underneath.

A (49:33)

So now I'm going to get to what it's like to work inside a company. And I have two topics here. The first thing is, you're in a company, you're trying to keep it alive. That's what your work is. It kind of wants to die.

B (49:49)

Right.

A (49:52)

You're trying to keep this thing alive, but it takes work, it takes constant work to keep it alive. You're in a larger context which is generating innovation at a faster and faster pace. For you to keep the company alive, you have to express as much variety in your company as the variety that's coming from outside. That's just hard.

B (50:16)

Yep, absolutely.

A (50:18)

And the reason I bring this up is I think it's an important thing to know that if you're in a company and it's hard, it's not you, it's physics. Because it's like a pace layer shearing, right? That the pace layer of the city around you or the human thing around you is constantly accelerating. And your company is not like that.

B (50:41)

Yes, that's one way of putting it. And definitely that's one of the reasons why companies eventually die, of course. And the other part of it, and the way it sort of gets expressed typically, is even if a company is successful, my vision, if you like, my speculation maybe is the way that comes from this work. And then of course, Talking to people, CEOs of various companies, some small, some of the biggest corporations in the country, is that there's this kind of funny life history of a company that's reflective of what you just said. And that is that at the beginning, of course, the image of all these great ideas of what you're going to do and great products you're going to have, and you're sort of in the back garage putting it all together, and it's all dominated by getting the product, getting the new idea. You have a spectrum of things and you put it out there. You don't care about the bureaucracy very much. That's secondary. We take care of that and so forth. And of course the market is going to determine what products are successful. And so already you have a problem. At the beginning, the things that you thought may be the sexiest things turn out to be not so successful. And the thing that you thought might have been a little mundane actually is the thing that's keeping the company going. I mean, that's not necessarily so, but you have this problem of this mismatch between your fantasy and of course, the reality of the market. But anyway, the company grows, it's successful. And of course then you have become much more serious because you have to make sure you're paying the taxes, you're getting floors cleaned. And so you have to have, inevitably, a bureaucracy is crucial, an administration to run the place and so forth. And more and more, you've started out with maybe a broad dimensionality in terms of product space, and gradually the market limits it inevitably to a few things that then define the company. So you had this double phenomenon taking place. You're having to build up a big bureaucracy to run it because it's getting bigger and bigger, more and more employees, more and more sales and so on. And at the same time, your product space is getting narrower and narrower as it gets more and more successful. And those two things start to collide because it shuts out innovation and ideas that are the very essence of what's Kept the city renewed. It's much, much harder to do that in a company. And the culture inevitably develops where that gets suppressed.

A (53:23)

I can't remember who said it. I think it might have been Peter Drucker who said, the greatest enemy of your future success is your current successful product.

B (53:31)

That's a very succinct, punchy way of saying it. Yeah, very hard to get away from that. And that coupled with this inevitable bureaucracy in administration, which you can't get around. I mean, you have to do it.

A (53:45)

Which built up to scale your successful product.

B (53:49)

That's right. Many companies turn out to be mostly bureaucracy, mostly administration, let me put it that way. The other thing that happens, of course, in the biggest companies is that if they have a research and development division or something, they want to call that when times get tough, which they inevitably will at some stage because of externalities, that's the easiest thing to fire, easiest thing to get rid of because you have this idea, look, get rid of that and then when things get good, we'll re establish it. That is, to me.

A (54:23)

Well, it's interesting because I've lived through the life cycle of growth companies as they become value companies. And it's the experience, you can feel it when you're living inside. This is probably my greatest interest in systems and complexity is what's the experience of an actor inside a complex system? And so people at work are that thing, are one of those actors. And I want to describe a network. And one of the principles of the work that we're doing that identifies work as a product that people at work get value out of is that work is distributed inside a company through a network. Here's the way it works. A customer comes along with a need. The need is perceived and it's received by the company. It's turned into a possible solution. And then making that solution a reality is broken up into pieces and distributed first to departments, then to teams than to individual contributors who then in turn respond to that need with help. I'm going to call it help is what they do. And so they contribute the help that gets slowly added back up to a solution that goes to the need. Well, the reason that this is not like a circulatory system is that at each branching, something different goes down each branch. It's not just red blood cells and red blood cells. It's no, you're going to be responsible for this subsystem or you're responsible for this. And yet very much in the same way that you can see a biological organism as A smoldering fire of adp. You can see companies the same way, which is that there's a hunger on the work side for good quality work. And so how the allocation works is really important. It is that description. Does it sound like anything else to you?

B (56:29)

No, it certainly isn't true in biology, what you said. You're exactly right, of course. Well, maybe I don't know enough, but it could be that one thing that possibly is analogous to it might be the neural system. Because even though it's electrical signals, it's all electrical signals, of course, so it's all electricity. I mean, fundamental level. On the other hand, it has different qualities somehow, otherwise we couldn't think these different thoughts. So somehow the branching separates out. So I'm saying different thoughts, different reactions, different emotions or whatever, somehow get processed by some sorting mechanism of the character of those signals that are going from neuron to neuron through axons, and then.

A (57:18)

They come back and get reassembled and.

B (57:20)

They get reassembled in some way into thoughts.

A (57:22)

I have an idea, you know, okay.

B (57:25)

So there might be some analog. It's information. So it's the quality of information, even though they're electrical signals. It's the quality of information somehow that is analogous to what you're talking about. And it could be even, you know, when in a city. I was thinking, as you were talking, we've done some work on the diversity of cities, because you're talking about. Also you could think of what your description as a description of diversity of task or diversity of whatever. So that happens, of course, in a city in terms of the great diversity of jobs and businesses, certainly businesses that constitute a city. We talked about companies, but it's not as if all companies, they are hardly all the same. In fact, quite the contrary. The diversity of a city increases systematically with its size. And so in order for all these multiple tasks that constitute what a city is can take place. So there are sort of loose analogs to what you're talking about, which happen in a company in terms of. Because after all, within companies we have, as you say, I mean, you described it as going through a network, I would say of different tasks.

A (58:44)

Yes, it's a network. Well, a network. It's branching. I mean, we even draw it. It's a work breakdown structure sometimes, which is. It's got branches and the branches are qualitatively different and then they're handed to different people who have different appetites. And this is very related to scale, which is, if I'm a farmer and I'm self provisioning. I'm not anywhere near a city. I do everything. I do absolutely everything. As my business grows, we start to divide up the work and that dividing of the work gets finer and finer.

B (59:22)

Yes, absolutely.

A (59:23)

As you get toward larger and larger companies.

B (59:26)

By the way, that happens in cities. That's why I was thinking in cities I've not done much on jobs, but we have on businesses, which is sort of a slight agglomeration of that. But going back to New York, I remember years ago being struck by what I was walking through Greenwich Village and there was a store there which may or may not still exist, which was a store that sold chess pieces. Talk about fine grained chess pieces. Wow. And then I found another store that was selling antique fireplaces. Now the point is that you cannot sustain a store like that probably in any other city, you certainly couldn't. In Santa Fe you couldn't have store. I mean it couldn't be sustained. Or even antique fireplaces, you probably couldn't. In Cleveland you have to have a certain size. And that is a complimentary way of saying what you said. The bigger you are, the more fine grained it is so that something like chess pieces becomes something of itself.

A (60:28)

It was one of my biggest discoveries working for large companies. When I first got to these companies, I thought that I had to pass as a corporate person and I thought that uniformity was important. But what I realized is that companies want specialization. Specialization's another way of saying be weird. And so once I said, wow, I get to be as weird as I want. That's called specialization. And found a beautiful, beautiful work as a result of that. But in a big enough company, you definitely have the chess piece makers.

B (61:00)

Yes, exactly.

A (61:02)

But there's probably a limit to the utility of that. Nobody's ever going to make just the queen of the chess. Right.

B (61:12)

So you. Yes, there definitely is a limit, for sure.

A (61:16)

But I'll tell you what your work has led me to believe, and this is something that I hadn't thought about for a long time, is that that network I was just talking about probably has a physics and it can probably be modeled and you can probably learn a lot about its variables by doing that.

B (61:36)

Yes. I mean, some of that recent work, I haven't thought about it quite in these terms, but it was in fact, in trying to understand the growth of diversity, which I realize is related to what you're talking about is first of all, what is just the data about diversity as a function of size for cities and companies. And then can we model can we understand how that diversity grows? And one of the standard models goes back to biology and ecology and that's called this. I don't know how familiar you are with it. Something that technically is called the Yule Simon process. Another way of saying it is the rich get richer. Meaning that if you have, let's take a company and you have a department that's doing some task with doing something, it grows according to the size it already is. If you're bigger, you could somehow have the power and the financing to add more of your own kind with a certain probability. And that could be either good or bad. But there's a lot of evidence that that's what happens. And that's why it's called the rich get richer. Those that have it create more of themselves.

A (62:53)

There's a related thing. I didn't do this analysis, but a friend of mine did. He wanted to know what predicted the growth of a department next year.

B (63:03)

Oh yes, that's a similar question. Yes.

A (63:05)

And the workforce plan said what that growth should be. But what predicted that growth was how much they grew last year.

B (63:13)

That's right, exactly. So that's the same idea. That's the same idea.

A (63:16)

It's a similar idea. It's certainly a similar idea which is that there's something besides the plan.

B (63:21)

That's right.

A (63:22)

That is predicting growth.

B (63:24)

Yes.

A (63:25)

We ask the question, what job do you hire your job to do for you? And so the reason we ask that question is if work's a product that you're buying, what do you buy it for? And we don't have to say job, we can say work. But what job do you hire your work to do for you?

B (63:42)

Me?

A (63:43)

Yes.

B (63:44)

Goodness me, I never thought about it in those terms. So it's interesting to turn it around like that. It is very interesting because if I think about it in those terms, I definitely hire it to provide me with interdisciplinarity and to provide me with like minds, I guess because of that, coworkers that are like that and ones that I can feed off of and bounce ideas off of. So that's what I hire it for. And that's why I go in, by the way, I'm at home today with this and I can do most of my work, I mean principle. I'm a theoretical physicist. Basically I'm going to do theory. I can be anywhere in a sense, but I go in on a regular basis precisely to get there. If I think of it more self centeredly, which is what this is asking me to do from My viewpoint, I'm hiring it, so to speak, to provide me with all that other stuff as well as, by the way, with an environment, with a blackboard and with young people around and so on. I mean a whole bunch of just a culture. I hire it for a certain culture.

A (65:01)

It's very interesting because my father was a butterfly collector, among other things, and we used to go to the tops of mountains to collect butterflies. And the reason is that butterflies have evolved to fly uphill. Why you want to meet other butterflies and if they all agree to fly uphill, they're going to find each other. But what they're looking for is same. But there's a way in which what you're looking for on your hilltop is difference.

B (65:24)

Well, it's both, actually. It's both. I look for, I wouldn't say same, but similarity on the one hand, but very importantly, I'm looking for something quite different, something that's going to shake me up possibly that's going to make me rethink, think something different and so forth. That's very important. But it's a sort of strange combination of those two things.

A (65:52)

It's open minded, smart people. That's the sameness.

B (65:55)

I hire it to provide me with.

A (65:57)

Smart people to interact with and a whiteboard to share. What does your work cost you?

B (66:02)

Well, it cost me time and energy. You do pay a price.

A (66:07)

And if you say time, what would you be spending the time on if it wasn't that?

B (66:11)

That's why I hesitated. That's why I hesitated. Yes, interesting question. Very hard for me to answer that one.

A (66:19)

It's often true for people who have had a lot of control over crafting their work.

B (66:25)

Yes. So that's the point. I mean, I'm enormously privileged by the fact that I'm in fact, for most of my career I've been allowed to do what I wanted to do. Quote now that's a glib way. Glib way of saying, argues that self.

A (66:41)

Assembly of organizations is a powerful idea.

B (66:46)

It is.

A (66:47)

You have a very open ability to sort of self assemble with others.

B (66:52)

You know, I'm just thinking through price. I don't have to pay now I'm old and I'm near the end of my career, et cetera, et cetera. But in general, of course, one of the prices I pay is I have to convince people to support me, which means I've got to write proposals and grant things and do various things, jump through various hoops, I suppose occasionally have to prostitute myself in some way. I don't think but there are certain things you have to do. There's an obligation. You have to be on committees. You have to participate in things that, given your druthers, you certainly wouldn't.

A (67:33)

You were the director of.

B (67:36)

I was. It's called the president.

A (67:38)

The president of the Santa Fe Institute. That had to have some cost.

B (67:42)

Yes. Oh, that had huge costs. It's amazing. I didn't think of that when you asked me the question because I hadn't been president for a number of years. But yes, being president is a huge cost because there's huge psychological costs. There's huge costs, really, to your time then in terms of your. It's 24. 7. There's huge costs. I mean, the biggest cost for me was 2008, 9, when everything collapsed. And Sanofi Institute relies less so now, but relied very heavily on. On immediate donations from private organizations and private donors and so on, who had a kind of cavalier attitude. You don't need a stash of money for the rainy day. So it was very cavalier about that, which I disagreed with. But nevertheless, that meant when 2008, 09 came, we were. Keeping the place alive became something that I had to do. And I consider that one of my great achievements in life was that the center of his soup survived through the market crash. So that's a huge price I paid.

A (68:54)

That's exactly what we were talking about, by the way, is keeping an organization alive in an environment that's expressing a lot of variation.

B (69:03)

Yes. And that's what it was.

A (69:04)

That's exactly what it was. Where can people learn more about you?

B (69:08)

Well, they could, of course, read my book. There's hints there about me, but certainly about my work that represents. Certainly my recent work, I should say. But you can read on the Santa Fe Institute website and I've got lots of podcasts, but lots of talks that I've given, from TED talks to things.

A (69:27)

Like that, which are much more visual than this. And so for anybody who wants to see the graphs, go to a TED Talk. Well, thank you. I really appreciate it. Thanks for taking the time to talk to me with my really weird request, which was an AI told me to talk to you and. And you said, let's try it.

B (69:48)

No, I couldn't resist. How could you resist that? It was slightly bizarre, as you said, but it was also very flattering. It was extremely flattering.

A (69:56)

Well, thank you. Thanks for joining me for another episode of Work for Humans. If you enjoyed this episode, please, please give us a five star rating. Wherever you listen to podcasts and share the show, with one person you think would get value from it. Believe it or not, this really helps us grow the show and reach more people who want to build the kind of work that people really want. As always, thank you to my producer Jason Ames at ninthpath Audio for his insights into content and his high standard for quality. Final note, the opinions shared here are my own and not the views of Google or Cisco Systems. Thanks again for listening. See you next time.