Geoffrey West - The Universal Laws of Growth, Innovation, and Sustainability | Salesforce

Ladies and gentlemen, please welcome Professor Santa Fe Institute, Jeffrey West. Hi. Good afternoon, everybody. It's a pleasure to be here. I'm delighted to be invited back to talk at this sort of humongous, extraordinary, somewhat overwhelming event. I don't think they had the title of the talk that I gave them, but this is the. I have two other titles. This is one of them which I hope has come up, is the yes, There we Are. That's sort of a title to be provocative. Could you give me the previous slide, please? Yeah. So it's sort of encompassed by this. And what I'm going to talk about is an extraordinary broad range of. Of topics that are to do with the future of the planet. But they also involve not just your future, but the future of the cities you live in and the companies you work in. And I want to talk a little bit about it. And I'm first going to show you this picture, which is my image of what the night lights taken by a NASA satellite would have been in the year I was born, which is 1940. And to remind you what has happened in the planet on just my little lifetime, that it's gone from that to this in just whatever it is. What am I? 77 years? Nearly 78 years, which is extraordinary. And of course, all of that, all those lights, this glowing earth, of course, is a manifestation of the extraordinary urbanization that has taken place on the planet coming from this continuous exponential growth in population. And indeed, the point to recognize is that all of us live in this faster than exponentially expanding socioeconomic universe. And I put some numbers up there. In the United States, we've gone from a few percent to almost over 80% in terms of urbanization in just 200 years. The planet has crossed the halfway mark just a few years ago and is heading towards that 70 to 80% level in the next 50 to 75 years. And just to give you a sense of what that means, if you just average that into mid century, that is equivalent to urbanizing at the rate of about one and a half million people every week, which means that every couple of months we're adding a Los Angeles, the equivalent to a Los Angeles on this planet, or every four to five days, a city, the equivalent to San Francisco, or every month a country the size of Denmark. That's what's happening as we speak. And the stress on energy, resources, the environment, and maybe most importantly, the social fabric of are extraordinary. And I think it's fair to say that we effectively continue as if nothing is happening. And it's all going to work out fine. And I want to draw attention to that from a scientific viewpoint because I've spent most of my career doing things like quarks and gluons and string theory, and I want to take that kind of thinking to these questions. So that's the paradigm we live in. And it evolved, of course, from the industrial revolution, the discovery of entrepreneurship and capitalism and fossil fuels, which has led to this extraordinary quality and standard of living that we are privileged to participate in. But the future of the planet, as we move forward is clearly completely intertwined with what happens in our cities. So I want to spend a few minutes just talking briefly about cities and then I'm going to switch to, to talking about life biology and then I'm going to switch back to cities and talk then about companies. So that's why I'm going to hope to try to do that in the next 25 minutes or so. So we're all familiar with cities. They come in various shapes and forms. And we all know why we're attracted to cities. There's a greater sense of material well being, there's more culture, there's more access to goods, greater opportunities, there's better education, more fancy restaurants, more buzz, more sexy with it people. And this city of course, being one of the great examples of it. But of course none of this can happen. None of it, including every thought that takes place within a city, unless it's supplied by energy. Energy underlies everything. And all of this has to be supplied by energy. And the most fundamental law of physics, the most fundamental law that overrides everything else, is the second law of thermodynamics, which says if you spend energy to organize stuff, to make it into cities or companies or organisms or cells or iPhones or buildings, whatever you do, inevitably and inextricably, you create disorder somewhere else. There are always what you could think of as unintended consequences. And that's called entropy. And we have that phenomenon all around us. Things like this and this and this. You're all familiar with these. All of these are, so to speak, unintended consequences falling from the second law of thermodynamics, of trying to organize life and do the kinds of things that we do. And this is the question whether this is what San Francisco will look like in 50 years or London in 50 years. There are already places on the planet that look like this. So let me talk a little bit about cities. When we think of a city, you usually think of it in its physicality. You think of how beautiful San Francisco is physically, you think of the boulevards of Paris, the skyscrapers of New York and, and so on and so forth. But that's sort of the uninteresting part of the city really, because the most interesting part of the city are the people that live in it. Because the whole point of a city is simply to facilitate interaction, to do things like this, to bring people together, to create new ideas, to innovate, create wealth and so on. That's what cities are for. It is maybe the most extraordinary, fantastic machine that human beings have invented and it has been unbelievably successful. And this is what a city is. It's bringing people together in an environment that has been there for over 2,000 years. That stage, those people have come and gone, they've sat around bullshitting the way everybody, whatever 150,000 people supposed to be here, you're all going to sit around bullshitting and talking and meeting. And most of those ideas are pointless and useless to mostly everybody else. But you know what's extraordinary is that every once in a while with that phenomenon comes quantum mechanics, the theory of relativity, a Google, a Microsoft, a Salesforce, a Ford Motor company comes out of that phenomenon of providing the environment for interaction, which is the machinery of what a city is. So it's this or this. Here's a picture of New York from 120 years ago where you see very explicitly the very thing that is going on around us now. And of course again, those buildings are still there, those people have gone. But the what is in spirit going on there is what New York is trying to encourage. So cities are the integration and intersection between, on the one hand, physicality, energy, resources, the metabolism of a city, so to speak, its infrastructure, its physicality and information exchange between the people that live in it. It's kind of genomics leading to kinds of what I said a moment ago, innovation and wealth creation. So given this role of cities and given the role that it plays, it is crucial to ask the question, in terms of the long term future of the planet, whether there can be a serious science of cities, meaning quantitative, predictive, mathematizable, computable and so on. That's a huge challenge. And indeed the way we've dealt with cities and companies for that matter. And much of the social sciences is like all our problems, we put boxes around them and we stove pipe them. And each one has its own specialties. And most of the people in one box don't talk to the other. And within those boxes, this is just an arbitrary way of division that I made. But, but within those boxes, you can put boxes around each one of those things inside it, and inside that there's more boxes. And that's how you work. That's the way the system has worked. And that is a recipe, typically for those of us that study complex systems, a recipe for creating even more unintended consequences and even more problems. Because each one of these is what is called a complex adaptive system. The externalities are continually changing. These are complex adaptive systems. Each one of these is, and the whole thing together is a complex adaptive system of complex adaptive systems. A daunting question to ask whether you can make a science out of that. But if you don't, you end up with this phenomenon which you're very familiar with that, this. And then of course you have this is the solution. And this is typical of the way we deal things. This is obviously simplistic example. So I'm going to come back to all this in a moment, but first I want to change gears and talk about biology. And the reason is the following. A, because in all of those books that you read about how to run your company and how cities work and how to make money and leadership, very often biological metaphors are used. The ecology of the marketplace, the DNA of a company, the metabolism of city. And the question is, are those just metaphorical bullshit or is there some serious substance to it? So I want to try to examine that. And the point is that if you try to make a science of cities, it's not going to be like Newton's laws, it's going to be what we call coarse grained. So here are coarse grained questions from biology to take it out of the context of things you're more familiar with. Why is it you stop growing? Why is it that everybody in this room will with certainty be dead in 100 years or less? Will, where does that hundred years come from? Why isn't it that some of you aren't going to live for 1000 years? Or why isn't it that all human beings don't die like mice after two to three years? Or why is it that you had to sleep eight hours last night and not get away with just three or four, like an elephant? Or you needed maybe 15, which you used to have to sleep 15 hours a night. You remember once upon a time when you were baby, now you only sleep eight. Why? Why is that? These are the kinds of questions and we need to be able to answer those and these two more related to what I'm going to come back to why is it that not only we die, but all companies eventually disappear? By the time you are my age, the company that you are now working for will almost certainly in most cases be forgotten. And I will come back to that. And why is it that that despite all that, life gets faster and faster? So what's happening here? And what's happening here? That's some of the questions I'll come to. So here's us. We're one of these. We are a mammal. We range in size from over eight orders of magnitude, 100 million, from the shrew to the blue whale. And each one of us looks different as a species. We have different evolutionary histories. Each organ, each cell type, each genome has its own unique history. Evolutionary history we've got here by natural selection. And there's this image of sort of some arbitrary and capricious chaotic process that's led to this. And in that image, of course, if you asked about the various characteristics of these, and you plotted them versus their size, and given their historical contingency, you would expect the points on the graph to be all over the map, so to speak. Whereas in actual fact, underneath that extraordinary complexity lies an extraordinary simplicity. And this is rather remarkable. And what's plotted here is the most fundamental quantity of life, and indeed, I would even argue the most fundamental quantity of the company you work with, and that is its metabolic rate. How much energy, in general does it need to live to stay alive? How much food do you need each day to stay alive? And it's plotted versus size. And it's plotted logarithmically, meaning going up by factors of 10. And what you see is some incredible systematic regularity that's already rather surprising. Even more surprising is if you forget about all that. You forget about natural selection and all the rest. You just say, look, if I double the size of an organism, I double the number of cells. Therefore, I should double the amount of energy needed to start to stay alive. No, that's not true. The slope of this line is very close to this curious number of 3/4, which means that every time I double, instead of needing twice as much energy, I only need 75% as much. Roughly. There is a systematic economy of scale the bigger you are. So your cells work harder than your horses, but. But they work less hard than your dogs. So that's sort of amazing. And where does that come from? But what is even more amazing, this is true across all animals, from cells, all the way through any taxonomic group to ecosystems. I don't have time to show you the data, but it's also true for almost any physiological quantity you can look at or a life history event. And here's some I want to show you. That's what I've said here. Here's something mundane. Heart rate, the slope of this is 1/4, minus 1/4 because it's getting its decreases. That's the minus sign. Means here's your brain, your white to gray matter in the brain. The slope of that is very close to five quarters. Here's there's much more variance. This is genomes. This has got a slope of plus one quarter. And you can see a pattern emerging. Not only is there simplicity emerging, but the number one quarter is emerging. So much. So, and this is a side comment, that's just a mathematical way of saying that, top line, that lifespan scales in that way with a slope of 1/4. I just showed you that heart rates decrease with one quarter. If you multiply them together, the increase of 1 is canceled by the decrease of the other. So heart rate times lifespan is the same for all mammals. Whether you're a tiny shrew or sits on the palm of my hand or a blue whale that is bigger than this building. But what is that number? That's the number of heartbeats in a lifetime. So the number of heartbeats in a lifetime is the same for all mammals. And there's data showing it, and this is typical of this, that everything follows these approximate scaling laws and everything sort of fits together in some beautiful sustainable way. No doubt one of the reasons why it's been around for 2 to 3 billion years. So that's what I said. And everything around you in terms of the biosphere is constrained by the number four. That's the data. So where in the hell does that come from? Well, it turns out that comes from something that's universal about life, that it is supported like your city and company are by networks. And it is the mathematics and physics of those networks, which I don't have time obviously to show you, that explains the origin of these scaling laws and is a complete theory that one can derive and calculate all these various things and understand where these laws come from and predict many other things. So that's what I've said here. It's these networks at all scales, they're completely different scales, but the same generic properties of them. And as I say, you just have to take my word for that. And from that, not only calculate those scaling laws I showed you, but there's probably I could show you 75 others like it and tell you many other things and explain why you sleep eight hours and why you're going to die soon, meaning soon. Less than 100 years comes out of this. So. But one of the things that I'm going to focus on, because everybody at this Dreamforce is no doubt obsessed by growth, I want to talk a little bit about growth in terms of organisms. So you're all familiar with that. You've all done it. And you know how you did it. You eat, you metabolize, you send metabolic energy through the networks. The networks feed cells. They maintain the cells that are there, repair the damage and make new ones. So that's sort of the generic form of a growth equation. There it is again. That's in English. But that arrow going down is sort of the network distributing it. And you can put that into mathematics using the theory that I told you. And you have to take my word exists for doing it. And from that you can solve the equations and determine the growth curve for animals. And here's one. This happens to be a very good one. This is for rats, us as rats. This is weight versus age. That solid line is the theory. And those data points are measurements of lifespan. But the important point I want to make beyond the fact that the theory predicts this, three things I want to emphasize. One is it explains why you grow quickly and importantly why you stop growing. Because that presumably has. Maybe it has bothered you. Used to bother me. Why? Why is it I go on eating and I don't keep growing ontogenetically, that is, I don't grow bigger. You stop at some stage. And that is related to that so called sublinear scaling. The fact that the exponent of metabolic rate, that slope of the curve is 3/4, which is less than 1, reflecting an economy of scale. Economy of scale leads to bounded growth. And furthermore, the parameters that describe this are the same parameters that describe turns out any organism, because they're things like the average mass of a cell, energy needed to create a cell, and so on. And so these data, a bit more fluctuations in them, but just for four arbitrary animals. But here's taking just again a subset of animals. The theory tells you how to the right lens to look at growth is through rescaled quantities, which is done here. Anyone interested? I can talk to them afterwards. Rescale quantities. That shows you that looked in the right way, everything grows at the same way. And in fact you could do it so that everything dies in the same time, but a rescaled time. So everything kind of fits together and this is it. And the theory predicts the form of that curve. Okay, so there's a summary of what I just said. We have these extraordinary scaling laws in a system that is probably the most complex in the universe. It expresses economy of scale. The bigger you are, the less energy required per capita per cell, the pace of life. I didn't emphasize this. The bigger you are, the slower you are. Hearts beat, slower you live, longer time gets stretched out. Following these 1/4 power scaling laws. Growth is sigmoidal, meaning it stops. It's some stage reaching a stable point and you die. And all of that can be explained with this theory. So I want to take this paradigm over to cities and companies. So the first thing is, do they scale? So what I just told you was despite the fact that the whale is in the ocean, the giraffe has a long neck, the elephant a trunk, we walk on two legs and a mouse scurries around at sort of the 80, 90% level, we are actually rescaled versions of each other. We just scaled. If you just do the right scaling, we are the same things basically. And there's this kind of universality to them. And the question is, is that true of cities? Is San Francisco just a scaled down Los Angeles, which is a scaled down New York, and all of them are scaled up? Santa Fe's where I live. That's the question. Well, you can only get that by looking at data. First though is to remind you that of course, cities are network systems. They have similar kinds of networks that you do inside you, transport systems. And they're supplied by energy, supplied by transport systems. But I told you earlier, this is sort of the mundane part of a city, the really exciting part of a city. And the only point of cities is this is to encourage this and facilitate social interaction. This is a social network. And the important point I want to emphasize about that is that is often not emphasized is that the social networks that are important for understanding social organization are modularity. That is the fact that we have things like families and groups and departments and so on, and they have a systematic behavior and something that sounds sort of obvious. Everybody has to be standing on a two dimensional plane. You have to be somewhere. It's not, I can't go back with this. It's not. We're in cyberspace. Doesn't matter who you're, what instrument you're using. You have to be somewhere. You have to be on the bus, you have to be in the bathroom, in your kitchen, you have to be here somewhere. You have to be. And you have to move. You have to be able to go from your house to the job and to school and so on. And that's invoked in this cartoon. So we are, as I said earlier, the intersection of all these networks. And the question is, do we scale? And the answer is yes, as cities. This is something totally mundane. It's called petrol gas stations. I did it with European colleagues versus population size plotted in the same way. This is just four European countries. And you can see they all scale quite well. And they are like biology. That dotted line is linear, they are less than linear, they are sublinear, which means there's an economy of scale. The bigger you are, the less gas stations you need per capita. And they all scale, roughly speaking, in the same way. The slope of those lines is about 0.85. So the saving, instead of being 25% with each doubling, is 15%. But what is truly amazing is that this is the same for urban systems around the globe. I just have four European countries. It's true of the United States, it's true of China, Japan, all Latin American countries, et cetera, et cetera. They all express the same thing. Well, it's sort of cute that gas stations would do that. But what is amazing is all infrastructure does this. Everywhere across the world behaves in the same way. So it expresses a kind of universality, the way biology did, with a number of 0.15 instead of a number 0.25. But this is the uninteresting part. All the infrastructure, it's not so interesting. The really interesting thing is you and me interacting and talking. And that is represented by socioeconomic quantities. Like on the left, wages, things that didn't exist before we started talking to one another, formed communities and then emerged into cities and so forth. So this is wages on the left. On the right are people like you, so called super creative people, sexy professional people doing professional kinds of jobs. And you can see there's much more scatter than we saw in biology. But the important thing is the slope of these. It's denoted by beta up. There is a slope bigger than one. This is bigger than one, meaning instead of the bigger you are, the less per capita, which completely dominates biology. The bigger you are, the more per capita, the bigger you are, the higher the wages per capita. The more sexy people there are per capita, the more ideas per capita. Using patents as a proxy. I didn't write the slope on that, but it's similar. The more crime per capita, the more police taxes, the more fancy restaurants per capita, and so on and so forth. I could show you again, 30 or 40 of such things from different places around the world. And I'm just put on a panel of six of very different metrics. Could be anything from AIDS cases to wages, patents, I said GDP and so on. And you can see by eye they all have the same slope. So you have this extraordinary increasing returns to scale, that is the super linear behavior all governed instead of by 0.25, by 0.15, the bigger you are. So here it is in English. The good, the bad and the ugly come together. On average, if you double the size of a city, you systematically increase income, wealth, patents, colleges, all these wonderful things, but also all the shitty things, disease, crime and so forth. And you all do that by 15% anywhere in the world. So Argentina, Chile, Portugal, United States, wherever, China, Japan, wherever we can get data. And at the same time you save about 15% on all infrastructure. So from this viewpoint, cities are good. The bigger the city, the better, because people get more out of it, more interaction. And people are very good at suppressing the bad and the ugly. And collectively we say 15%. No wonder urbanization has been proceeding at a super exponential rate. So the question now begs itself, as it did in biology, where in the hell did this come from? It wasn't as if in 1780 there was a congress, international congress, and all the countries got together and said, the industrial revolution is coming and we can do all these wonderful things and urbanization is going to dominate the planet. We, we better have a bunch of rules and laws as to how to build our urban systems. No, it happened like biology, it happened organically. And the question is, what was it organically that has been going on that has made these scaling laws evolve? Despite all of the urban planning and the politics and the developers and all the rest doing all these things, nevertheless everything seems to end up following these laws. Why is that? Well, I'm sure you've already figured it out. It's because the most fundamental aspect of the city, as I said, is social interactions and social networks and social networks and social interaction is built into our DNA and is universal. Roughly speaking, we're pretty much the same human beings across the globe, certainly across the country, certainly across modern countries. So it's the universe, it's the mathematics and physics of these networks. And the idea is that what's really happening is that what the super linear behavior is. This more per capita is coming because I talk to you, you talk to her, she talks to him, he talks to me, and we sit like we were doing in that plaza there, and these ideas get generated and there's this positive feedback. So it comes out of the inherent positive feedback mechanisms in social interaction and social networks. And that gives rise to superlinear scaling more per capita, but it also gives rise to the pace of life speeding up in terms of the mathematical theory. So here it is, we had biology slowing down. Cities speed up, and this is just a whimsical example of it. On the left I showed you heart rates decreasing with size. On the right is some very ancient data. There's quite a bit of fluctuations in it of walking speed in cities. And you can see that there's this systematic increase. And it actually agrees surprisingly well with the theory. So I'm going to move on taking this and I'm going to do what I did before. And you can do lots of things with this. But I want to talk about growth. So it's quite similar in spirit as in biology, except it's much more complicated now because we have to take into account, so to speak, social metabolic rate. The social metabolic, not just the energetic metabolic rate. And that's all that's in those parentheses there. But the same idea persists, namely, what happens. Some of it goes to maintenance. Namely you maintain the roads and the buildings, but you maintain all the people. You have the doctors and hospitals. That's the first term. The second term is growth. You grow new stuff, you expand the city, you grow salesforce, grows a big building. But it also, you grow new people, obviously, and you bring in new people. So that's the generic form. And you can put that again into mathematics and use the theoretical structure and in biology, that's the thing that gave rise to the sublinear scaling and the stability that follows from it. The fact that you stop growing here. What happens is, and it's very, look on the left, that if you look at socioeconomic quantities and you calculate them, they have this property, which is what we see. They have super exponential growth, which is wonderful because then the theory is quite consistent. It gives rise to super linear scaling and. And that superlinear scaling gives rise to this extraordinary super exponential growth. That's great, except it has built into it a kind of fatal flaw. Not a fatal flaw in the mathematics, but a fatal consequence. And that's given by that dotted line at the point called tc. And that represents something in mathematics that's called a finite time singularity. And a finite time singularity means only that you can see there in some finite time the quantity that we're looking at, whether it's the number of AIDS cases, the gdp, whatever all those number of patents produced is going to go infinite, which is obviously loony. That can't be. And the theory tells you what happens as you approach that. The system, now you move to the right. The system stagnates and collapses. So that's not good. And so this, this dynamic that has led to these wonderful things has built into it, this collapse. But how? So how do you avoid that? And how have we avoided it? And we know how we've avoided it because we assume when we draw that and we solve the equations that we're in the same, if you like, innovative paradigm. We discovered bronze, we discovered coal, we invented computers, we discovered it. Something that, that is a major paradigm shift that determines the conditions under which things are growing. And so it tells you what to do. It says if you continued in the same paradigm, you would collapse, which I showed you on the last slide. So somewhere before that singularity, you better make a major invention. So you do it. Whoops. Oh, don't. Ah, very good. And you do it. And of course that's great. So you avoid it. So you sort of reset the clock by making a major innovation. But of course you would hit another singularity, that's bad. So you have to do it again. So there's this kind of theorem that if you demand open ended growth, as is in our economic paradigm that we live in and has been so successful, the then, and you're not surprised by this, you need to have successive cycles of major innovation. You have to have several continuous cycles of reinventing yourself and resetting the clock. Great. But this is a quantitative theory. We can calculate this. And one of the things I just told you, as you go along any one of those curves, life gets faster, the pace of life increases, so things are getting faster and faster, but more importantly, the time between innovations has to get shorter and shorter. So that's what I've written up there. So something that might have taken 100 years, 1000 years ago, now only takes 20 years and soon it will only take 15 and then it will have to take 10 and so on. So if you continue with this, you just take this argument to its logical conclusion, we will have to have the equivalent to an IT revolution every two years, then every year, then every six months, which is obviously also loony. So this system is not sustainable. And all we are doing by making major innovations from this viewpoint is postponing. So I want to show you some data. This is not mine. I have no idea who this guy is. I shamelessly stole it from the web. I'M still trying to find out who he is, but I use it because it looks like what I just showed you. And those numbers there are the time taken to reach 10 million customers for a bunch of what he considered innovations. And the reason I'm showing it is, first of all, it looks like what I showed, but also those numbers fit exactly the prediction of the theory. And this I borrowed from Ray Kurzweil, who is a whole different way of thinking about singularities, which I completely disagree with. But that's another story. And what's plotted here is, is on a logarithmic scale, the sequence of some innovations. And on the Y axis, on the vertical axis is how long it took to develop whatever it is that's listed there. And on the horizontal axis, how long ago it happened. And it turns out you can calculate this. And that straight line is a prediction from this theory. So I'm going to finish up very quickly. I've gone over my time. I'm going to finish up very quickly by saying a few words about companies. I'm going to miss this out about diversity. I won't explain that. I'm sorry. I will just simply tell you something that's extraordinary. You can get a theory of this, that the GDP of a city increases exponentially with diversity, meaning every time you add a new category of business, you increase exponentially the GDP. It's about 0.5. But that moves me into companies, because that's what diversity of a city in many ways should be thought about is the diversity of kinds of businesses. And here is data of U.S. companies, U.S. publicly traded companies, of assets and income plotted versus the number of employees, using that as a proxy for their size. And what you see is, and this is bin data I don't have. Since I'm running late, I won't explain other. But these are data from the US companies. Here's the complete set of data we have every single US publicly traded company since 1950 in this set. And those points are sort of the averages in the bins. But the important point is that the slopes of this are sublinear, meaning that they express in the language I used before economy of scale, just as it is in biology. And before I talk about what the consequences of that are. This is data. It's not a very good graph of. The red is what I just showed you for companies in the US the blue is Chinese companies. So that's from the Chinese stock market showing that they behave in the same way in the short 15 years where data has been taken there's 30,000 US companies on that since 1960, publicly traded. So from what I just said, what does that say? If it's sublinear, it's like biology. So instead of having open ended growth hockey stick, you inevitably have sigmoidal growth. It means that one of the conclusions is all companies, if they survive, stop growing and then they die. So that's what you would say from this. So here's the data. This is all companies since 1950. And you can see all the ones you're associated with are to the right there. And they're all zooming and all the old farts at the top are sort of going flat. And this has been adjusted for inflation and here it's adjusted for the gdp which is the right way of doing it. And everything's flat, so everything stops growing. And that red line is just one example. That's Boeing Aircraft Co. Which has stayed constant since 1950. And so this, in fact we've analyzed this, this follows the theory extremely well, all this rubbish that you see up here. And just to give one the best example, I'm sorry, this is really, I'm cheating a little bit. I mean this is correct data. But I took the best example. This is Walmart because it was so beautiful. So here's Walmart looking like a hockey stick until 1990. Here it is with recent data and that red line is a prediction from the theory. Can you believe it? If I were an investor, maybe I could have made money knowing what I knowing this. So the last thing is dying. So this is taking all the companies, all the data we can get from U.S. publicly traded companies. And what is on the left is the survivability curve. So the probability of survival plotted versus the age of the company. And on the right is their mortality. Of course they are complements of each other and the different colors represent different. This one represents different sizes of company. But we've done deconstruction into every possible sector you can imagine. But the point to emphasize here is that they all end up having a probability of one look at the top right of dying. Whether they disappear by bankruptcy or liquidation or are acquired or merged. So a large part of that, by the way, one of the big questions, why is that different from cities? Just a quick comment. That's because cities are multidimensional, open ended, highly diverse and companies are low dimensionality. In fact, they start out high dimensional, they get more and more unidimensional and they get less and less diverse and they cannot accommodate to the changing environment. And have become dominated by bureaucracies and administration when they get big. That's in sort of one simplistic sentence. So I'm going to finish off with just a couple of things and a couple of quotes. First, your metabolic rate. Sitting here, you're using about 100 watts. Staying alive, a light bulb. You are unbelievably efficient. Here's that. Second number is how much energy you're using in your social life in order to have a car, to have an iPhone, to have a nice home, refrigerators. If you add up all that energy, that's about 11,000 watts, you're 100 times bigger than you should have been from when you evolved. That's that graph I showed you where we sit. You see? Look where we sit. About 100 watts. If you look at that graph, men and women there. And what has happened since we formed cities, companies, and especially since the Industrial revolution and we've created all this marvelous stuff, is we've gone way up there to the top, totally out of whack with everything else. And you can ask how big we are. We're the equivalent. Every person in this room is as if he or she were 30,000 kilogram gorilla. You're about to 60, 70% of a blue whale, which is bigger than this building. And there are seven and a half billion people on the planet and each one wants to be like this. And there are 3 billion coming on in the next 50 years or so, or you're equivalent to a dozen elephants. So I'm going to finish up with three quotes. One is that you're very familiar with by the great economist Joseph Schumpeter. Creative destruction is a central fact of capitalism. That's really, in a certain sense, what I was talking about. But I'm also talking about sarquote that most people are not familiar with from him, which I like very, very much. And that is that all successful people are standing on ground that is crumbling beneath their feet. And I think that's roughly speaking, true. And I got two other quotes. One is by the great John von Neumann, the great mathematical physicist. For those of you that work in the IT industry, none of you would be here with a job and Marc Benioff would not be a billionaire had it not been for John von Neumann. The ever accelerating progress of technology gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs as we know them could not continue. So that's sort of what's being implied by what I was saying. There's big question marks. And finally, I want to do some shameless, absolutely shameless self promotion. Does this work? No, it doesn't. This is self promotion. Look at that. Look what Mark said. So if you want to read now, can you get it? Ah, there we are. There's the book. Read the book is my point. There you go. There it is. And good, we're done. Thank you. Sorry. There it is. Thank you.