Table of Contents
| Introduction | |
| 1 | Economic Evolution: Learning from Life Cycles | 3 |
| 2 | General Evolution: Learning from Nature | 27 |
| 3 | Biology and the World of Molecules | 44 |
| 4 | Information and the World of Bits | 69 |
| 5 | Adaptative Management | 97 |
| 6 | Seed, Select, and Amplify at Capital One | 131 |
| 7 | Breeding Early and Often at the U.S. Marine Corps | 151 |
| 8 | Creating the Capacity to Respond at BP | 162 |
| 9 | Born Adaptive at Maxygen | 181 |
| 10 | Becoming an Adaptive Enterprise | 199 |
| 11 | The Adjacent Possible | 229 |
| Notes | 247 |
| Acknowledgments | 257 |
| Glossary | 259 |
| Index | 265 |
Read an Excerpt
1
Economic Evolution: Learning from Life Cycles
Imagine that it's 1971 in Palo Alto, California.
You've wandered into a building at the Stanford Industrial Park, a nondescript place with cinder-block walls and rented furniture. The 3180 Porter Drive site is as plain and drab as the surface of the moon, and the guys working here seem to be living in their own private universe, speaking their own unique language. Someone's nattering on about the new "Intel 4004." Apparently, this new gadget he's talking about is called a "microprocessor." Someone else seems to think it's really great that this thing contains 2,250 transistors. They're both worked up over the fact that this "microprocessor" has an entire "CPU" on a single "chip."
As an average person living in 1971, you have no idea what they're talking about. In this ugly building on Porter Drive, also known as the Xerox Palo Alto Research Center (PARC), the computer wonks are also talking about "operating systems" and "laser printing" and "icons." Soon they will be going on about the "mouse," "point and click," and the "graphical user interface"; eventually, "bandwidth" and "network protocols."
In the early seventies, these terms were arcane jargon, but the words and the concepts they represent are as familiar to us now as "assembly line" and "mass production" were then. That's because the computer scientists at places like Xerox PARC and Bell Labs were, in fact, inventing the future--which is now our present--building the new economic engine that would overtake theindustrial economy of the preceding 150 years. "Computer speak" is the lingua franca of the world we inhabit at the beginning of the twenty-first century.
Today, in commercial laboratories with names like Maxygen, Diversa, and Nanosys, it's happening again. A new generation of scientists is inventing the next new world with its own novel nomenclature. Their terms of art, phrases such as "combinatorial chemistry," "gene shuffling," "high-throughput screening," and "MEMS" sound just as arcane to the average person now as computer terminology did in 1971.
But pay attention. In the same way that researchers at PARC and Fairchild Semiconductor and Bell Labs created technology that established a new economy based on information, scientists in labs today are inventing a future based on molecular technologies. These include not just biotechnology but nanotechnology and materials science as well. Cargill Dow Polymers is growing polymers for plastics in corn plants. PPG Industries is making nano-scale coatings that enable windows to wash themselves in the rain. Bio-Rad Laboratories is attaching naked strands of DNA to gold nanospheres and injecting them into people with a nano-BB gun.
A new "molecular economy" is on its way, while the information economy hasn't completely matured. As the information economy comes of age, a surprising thing is happening: Information systems are starting to take their cues from biological ones. Information is converging with biology, and business is following suit.
At John Deere, for example, the art of breeding--as in thoroughbreds and show dogs--has been used to evolve a schedule for a highly complex factory that makes seed planters. Using a computer, the metal-benders create a few random schedules that express the sequence of planters to be built in a digital code made of zeros and ones. That code is a set of instructions, just as DNA carries a set of instructions, its "genetic code." Deere engineers evaluate each schedule with a simulator, which is like letting the horses grow up, and then racing them--in silico. The winning sequences are then mixed, put out to stud in an approach that is essentially sex for software. Through this approach, which uses a "genetic algorithm," parts of the best schedules are recombined to create a new generation, just as horse genes are recombined, albeit through a somewhat messier process. Forty thousand new schedules run simulated races every night, and the winner is the schedule that runs tomorrow's real-life production derby on the John Deere factory floor.
Genetic algorithms are already in widespread use, improving jet-engine designs, credit-scoring forms, and stock-trading rules. The bigger story than sex for software is the abstract principle that biological behavior--in this case sex--can be written into digital code, then applied to the most intractable business problems. Stay with us, and you'll see that this translation of a biological function into a computer process is only one of many ways in which the concepts of evolution apply to business, in this case through precisely measurable operations improvements.
In 1984, a multidisciplinary group formed the Santa Fe Institute and began a research program based on a really big idea: that biology is not the only system that evolves, and that the concepts of evolution help to explain the process of change in any connected system, be it an ecology or an economy. Since then SFI has extended its work to other social systems--a business, a tribe, a crowd, a stock market, or a political party. Their work (and similar work at the University of Michigan, IBM, and many other places) has created some early tools, and a point of view that lets us see the economy as an ecology, and an organization as an organism, at the level of rigor needed to do empirical science. That's the level at which you begin to use evolutionary concepts to schedule factories.
These techniques, in time, may become as pervasive as the computerized spreadsheet is today. In fact, you can get started on your PC right now with a Microsoft Excel add-on called Evolver.
The theory of evolution through selection goes back to Charles Darwin in 1859, though the practice goes back to hunters and gatherers and their dogs. In Darwin's time, scientists recognized that biological systems evolve without any conception of the future, and yet the systems' future paths are significantly affected by their past. The new wrinkle for business today is the computing power that enables us to cast forward to test different evolutionary paths. At a startup called Icosystem, for example, former Santa Fe Institute research fellow Eric Bonabeau uses genetic algorithms to breed strategies for Internet service providers (ISP), then simulates an industry of competing strategies to observe the evolutionary adaptation of each "species" of ISP.
As these tools continue to develop, they will start to provide insight into the business problems currently reserved for senior strategists. A generation ago, the spreadsheet "deskilled" financial analysis, meaning that the most junior assistant in your company, equipped with the right software on a PC, could organize and manipulate data and enter the province of a once-highly specialized profession. In the years to come, new tools relying on the power of evolution can similarly "deskill" a wide range of activities, including strategy and planning. Today, the focus is on operations; the frontier, as at Icosystem, is strategy. Tomorrow, the boundary will move to organization, and managers will be able to test evolved organization designs and compensation systems to optimize the cultures that emerge.
Why is this worth reading about now? Well, consider what it would have meant in terms of your business, your career choices, and your investments, if you could have anticipated the impact of computers and information technology. Putting you well ahead of the curve in understanding the molecular economy is one of the ambitions of this book.
An even more urgent reason to pay attention to these converging economic life cycles is that the new technologies they are spawning hold the answer to the toughest problem business faces today: the inability of most companies to adapt to changes in the economic environment as fast as those changes occur. As we'll discuss shortly, it doesn't just seem that the world is changing faster and that volatility is greater than it used to be. Both are measurably and demonstrably true, and both emerge from our increasingly connected economy. Our institutions, businesses included, have been built for stability, not for change. As connectivity proceeds, business leaders face an imperative to create organizations that can adapt continually and rapidly, to keep pace with shifts in their markets, technologies, and society itself.
It is the world of biology that holds the key to meeting that adaptive imperative. Adaptation, the process by which organisms respond to volatility in their environments, has been going on for the past four billion years. As businesses today are struggling with volatility, they can look to nature's example for lessons on adaptation. And, as the Deere example makes clear, we're not talking only about language and metaphor, but about technical solutions and management approaches as well.
In this book, we'll see the lesson of PARC once again: By paying attention to what's going on in the labs of the next economy, we can find the management solutions we need to thrive in this one.
Economic Life Cycles
The future is already here--it's just unevenly distributed.
--William Gibson
The economy of the future derives from the science of today.
It happened this way in the information economy, and in the industrial economy before that, and it will happen again in the molecular economy. A new economic life cycle begins as science learns something new about the way the world works. Next, technology shows us how to turn new science into new productive capabilities. As a life cycle reaches maturity, every business employs the new technology to improve its performance. Ultimately, as an economy ages and the once-new technology becomes a commodity, we encode the deeper lessons from science and technology and apply them to the way work gets done and the way society is organized (see Figure 1-1).
We can use this simple four-quarter model to parse the Industrial Revolution:
Q1: Gestation. The Industrial Revolution began with scientific breakthroughs such as Maxwell's equations, which describe electricity, and Boyle's law, which taught us about heat and pressure, the beginning of thermodynamics.
Q2: Growth. These scientific advances translated directly into the technologies that powered the industrial economy: electrical networks, steel mills, and oil companies. These technologies were the basis of enormous fortunes--the equivalent of the high-tech industry in the subsequent life cycle, the information economy.
Q3: Maturity. Entrepreneurs of the industrial economy then organized around these new capabilities, recombining them with new management concepts such as the assembly line and interchangeable parts. Expanding by way of the growth-phase industries such as railroad, petroleum, electricity, and telephone, they went on to build national, then global, firms such as General Electric, General Motors, and General Foods.
Q4: Decline. These enterprises required a new form of organization--a steel mill, employed thousands, while its predecessor, a blacksmith shop, employed only a handful--and billions in capital. New ways of organizing work arose, leading to the now-familiar functional and divisional structures first observed in the railroads and at DuPont and General Motors. Even though these companies continued to thrive for a time, the period of exuberant growth ended, and industries consolidated into markets served by two or three companies, profitable not because of their growth or innovation but through their oligopoly power and their organization skills.
The pattern is straightforward: The economy transforms science into useful technology; business determines how to use the technology and then optimizes the resulting tasks organizationally. Society's trend-setters, decision-makers, and managers--sometimes unconsciously, sometimes deliberately--incorporate the concepts that bubble up and migrate out from these more fundamental, technical disciplines. Exposure to new technological capabilities alters the way we think. In the process, society, language, and politics change, too. In the industrial economy, this meant the shift of rural populations to cities, the shift of economic power to corporations, which led to the labor movement and antitrust laws, and so on. The incoming mind-set not only expands what is possible--it redefines our views of what will be possible next.
The Maturing Information Economy
In the mid-twentieth century, a new economic life cycle started. The information theory of Claude Shannon and the silicon semiconductor developed by William Shockley (both scientists from Bell Labs) gave rise to a new set of possibilities. Technology built on Shannon's and Shockley's insights enabled the manipulation of large quantities of data at high speed, building an infrastructure for providing cheap computer hardware, then software, then communications networks, and currently, an explosion of wireless devices. Today, we're in the middle of the third quarter, the growth phase, when every kind of business incorporates the new technologies to improve their value, cost, and quality performance (e.g., through mass customization, online order confirmation, and mobile connectivity), and to launch entirely new businesses based on real-time information like Yahoo!, OnStar, and Travelocity.
The information economy is just now beginning to glimpse its organizational phase, which will come into focus much more in the decade ahead. We see it as practices like Internet-based virtual teams, telecommuting, and networked organizations start to take hold. Self-organized entities like Linux have challenged the institutional framework, but now it's consumers as well as workers who are organizing. Napster has been quashed, at least for now, by the existing power structure. But, then again, unions were at first suppressed, too. Nonetheless, Linux has been embraced. Freeware like Shockwave Player, "open source" software, and ad campaigns developed by customers are all growing. Companies have begun seeing themselves as part of economic networks rather than freestanding entities. And the World Wide Web, one of the key infrastructure technologies of this economy, isn't provided by a corporation at all.
This is the beginning of a new organizational model built around a key technology from the growth phase: networks. The resultant social changes include the blending of work and the rest of life, the growing labor force working outside of a traditional full-time employment arrangement, the shift of economic power toward individuals, and the global economy that brings us summer vegetables year-round--and makes around-the-world terrorism possible.
Remember though, that all these aspects of today's economy were set in motion by the inventiveness of places like PARC and Bell Labs decades ago. Information technology has created cheap, intelligent, connected software agents. It has pushed miniaturization beyond the limits of ordinary comprehension. It has enabled autonomy in products and processes. It has given rise to artificial intelligence, offering a new wave of economic opportunity. And there have been other, unexpected consequences. Just as the industrial economy led to environmental degradation, the hyper-connected information economy has led to new assaults on privacy. What has been less well observed is that connectivity has created the marked increase in economic volatility that will define the key challenges of the next decade.
From the Hardcover edition.
Copyright© 2003 by Christopher Meyer and Stan Davis
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