where good ideas come from 
by steven johnson 




Table of Contents
Title Page
Copyright Page
Dedication
Introduction
I. - THE ADJACENT POSSIBLE
II. - LIQUID NETWORKS
III. - THE SLOW HUNCH
IV. - SERENDIPITY
V. - ERROR
VI. - EXAPTATION
VII. - PLATFORMS
Conclusion
Acknowledgements
Notes and Further Reading
Bibliography
Index
ALSO BY STEVEN JOHNSON
Interface Culture:
How New Technology Transforms the Way We Create and Communicate
Emergence:
The Connected Lives of Ants, Brains, Cities, and Software
Mind Wide Open:
Your Brain and the Neuroscience of Everyday Life
Everything Bad Is Good for You:
How Today’s Popular Culture Is Actually Making Us Smarter
The Ghost Map:
The Story of London’s Most Terrifying Epidemic—and How
It Changed Science, Cities, and the Modern World
The Invention of Air:
A Story of Science, Faith, Revolution, and the Birth of America



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Where good ideas come from : the natural history of innovation / Steven Johnson.
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For Peter
Introduction
REEF, CITY, WEB
. . . as imagination bodies forth
The forms of things unknown, the poet’s pen
Turns them to shapes and gives to airy nothing
A local habitation and a name.
—SHAKESPEARE, A Midsummer Night’s Dream, V.i.14-17
Darwin’s Paradox
April 4, 1836. Over the eastern expanse of the Indian Ocean, the reliable
northeast winds of monsoon season have begun to give way to the serene days of
summer. On the Keeling Islands, two small atolls composed of twenty-seven
coral islands six hundred miles west of Sumatra, the emerald waters are
invitingly placid and warm, their hue enhanced by the brilliant white sand of
disintegrated coral. On one stretch of shore usually guarded by stronger surf, the
water is so calm that Charles Darwin wades out, under the vast blue sky of the
tropics, to the edge of the live coral reef that rings the island.
For hours he stands and paddles among the crowded pageantry of the reef.
Twenty-seven years old, seven thousand miles from London, Darwin is on the
precipice, standing on an underwater peak ascending over an unfathomable sea.
He is on the edge of an idea about the forces that built that peak, an idea that will
prove to be the first great scientific insight of his career. And he has just begun
exploring another hunch, still hazy and unformed, that will eventually lead to the
intellectual summit of the nineteenth century.
Around him, the crowds of the coral ecosystem dart and shimmer. The sheer
variety dazzles: butterflyfish, damselfish, parrotfish, Napoleon fish, angelfish;
golden anthias feeding on plankton above the cauliflower blooms of the coral;
the spikes and tentacles of sea urchins and anemones. The tableau delights
Darwin’s eye, but already his mind is reaching behind the surface display to a
more profound mystery. In his account of the Beagle’s voyage, published four
years later, Darwin would write: “It is excusable to grow enthusiastic over the
infinite numbers of organic beings with which the sea of the tropics, so prodigal
of life, teems; yet I must confess I think those naturalists who have described, in
well-known words, the submarine grottoes decked with a thousand beauties,
have indulged in rather exuberant language.”
What lingers in the back of Darwin’s mind, in the days and weeks to come, is
not the beauty of the submarine grotto but rather the “infinite numbers” of
organic beings. On land, the flora and fauna of the Keeling Islands are paltry at
best. Among the plants, there is little but “cocoa-nut” trees, lichen, and weeds.
“The list of land animals,” he writes, “is even poorer than that of the plants”: a
handful of lizards, almost no true land birds, and those recent immigrants from
European ships, rats. “The island has no domestic quadruped excepting the pig,”
Darwin notes with disdain.
Yet just a few feet away from this desolate habitat, in the coral reef waters, an
epic diversity, rivaled only by that of the rain forests, thrives. This is a true
mystery. Why should the waters at the edge of an atoll support so many different
livelihoods? Extract ten thousand cubic feet of water from just about anywhere
in the Indian Ocean and do a full inventory on the life you find there: the list
would be about as “poor” as Darwin’s account of the land animals of the
Keelings. You might find a dozen fish if you were lucky. On the reef, you would
be guaranteed a thousand. In Darwin’s own words, stumbling across the
ecosystem of a coral reef in the middle of an ocean was like encountering a
swarming oasis in the middle of a desert. We now call this phenomenon
Darwin’s Paradox: so many different life forms, occupying such a vast array of
ecological niches, inhabiting waters that are otherwise remarkably nutrient-poor.
Coral reefs make up about one-tenth of one percent of the earth’s surface, and
yet roughly a quarter of the known species of marine life make their homes
there. Darwin doesn’t have those statistics available to him, standing in the
lagoon in 1836, but he has seen enough of the world over the preceding four
years on the Beagle to know there is something peculiar in the crowded waters
of the reef.
The next day, Darwin ventures to the windward side of the atoll with the
Beagle’s captain, Vice Admiral James FitzRoy, and there they watch massive
waves crash against the coral’s white barrier. An ordinary European spectator,
accustomed to the calmer waters of the English Channel or the Mediterranean,
would be naturally drawn to the impressive crest of the surf. (The breakers,
Darwin observes, are almost “equal in force [to] those during a gale of wind in
the temperate regions, and never cease to rage.”) But Darwin has his eye on
something else—not the violent surge of water but the force that resists it: the
tiny organisms that have built the reef itself.
The ocean throwing its waters over the broad reef appears an invincible,
all-powerful enemy; yet we see it resisted, and even conquered, by means
which at first seem most weak and inefficient. It is not that the ocean
spares the rock of coral; the great fragments scattered over the reef, and
heaped on the beach, whence the tall cocoa-nut springs, plainly bespeak
the unrelenting power of the waves . . . Yet these low, insignificant coralislets stand and are victorious: for here another power, as an antagonist,
takes part in the contest. The organic forces separate the atoms of
carbonate of lime, one by one, from the foaming breakers, and unite them
into a symmetrical structure. Let the hurricane tear up its thousand huge
fragments; yet what will that tell against the accumulated labour of
myriads of architects at work night and day, month after month?
Darwin is drawn to those minuscule architects because he believes they are
the key to solving the mystery that has brought the Beagle to the Keeling
Islands. In the Admiralty’s memorandum authorizing the ship’s five-year
journey, one of the principal scientific directives is the investigation of atoll
formation. Darwin’s mentor, the brilliant geologist Charles Lyell, had recently
proposed that atolls are created by undersea volcanoes that have been driven
upward by powerful movements in the earth’s crust. In Lyell’s theory, the
distinctive circular shape of an atoll emerges as coral colonies construct reefs
along the circumference of the volcanic crater. Darwin’s mind had been
profoundly shaped by Lyell’s understanding of the deep time of geological
transformation, but standing on the beach, watching the breakers crash against
the coral, he knows that his mentor is wrong about the origin of the atolls. It is
not a story of simple geology, he realizes. It is a story about the innovative
persistence of life. And as he mulls the thought, there is a hint of something else
in his mind, a larger, more encompassing theory that might account for the vast
scope of life’s innovations. The forms of things unknown are turning, slowly,
into shapes.
Days later, back on the Beagle, Darwin pulls out his journal and reflects on
that mesmerizing clash between surf and coral. Presaging a line he would
publish thirty years later in the most famous passage from On the Origin of
Species, Darwin writes, “I can hardly explain the reason, but there is to my mind
much grandeur in the view of the outer shores of these lagoon-islands.” In time,
the reason would come to him.
The Superlinear City
From an early age, the Swiss scientist Max Kleiber had a knack for testing the
edges of convention. As an undergraduate in Zurich in the 1910s, he roamed the
streets dressed in sandals and an open collar, shocking attire for the day. During
his tenure in the Swiss army, he discovered that his superiors had been trading
information with the Germans, despite the official Swiss position of neutrality in
World War I. Appalled, he simply failed to appear at his next call-up, and was
ultimately jailed for several months. By the time he had settled on a career in
agricultural science, he had had enough of the restrictions of Zurich society. And
so Max Kleiber charted a path that would be followed by countless sandalwearing, nonconformist war protesters in the decades to come. He moved to
California.
Kleiber set up shop at the agricultural college run by the University of
California at Davis, in the heart of the fertile Central Valley. His research
initially focused on cattle, measuring the impact body size had on their
metabolic rates, the speed with which an organism burns through energy.
Estimating metabolic rates had great practical value for the cattle industry,
because it enabled farmers to predict with reasonable accuracy both how much
food their livestock would require, and how much meat they would ultimately
produce after slaughter. Shortly after his arrival at Davis, Kleiber stumbled
across a mysterious pattern in his research, a mathematical oddity that soon
brought a much more diverse array of creatures to be measured in his lab: rats,
ring doves, pigeons, dogs, even humans.
Scientists and animal lovers had long observed that as life gets bigger, it slows
down. Flies live for hours or days; elephants live for half-centuries. The hearts of
birds and small mammals pump blood much faster than those of giraffes and
blue whales. But the relationship between size and speed didn’t seem to be a
linear one. A horse might be five hundred times heavier than a rabbit, yet its
pulse certainly wasn’t five hundred times slower than the rabbit’s. After a
formidable series of measurements in his Davis lab, Kleiber discovered that this
scaling phenomenon stuck to an unvarying mathematical script called “negative
quarter-power scaling.” If you plotted mass versus metabolism on a logarithmic
grid, the result was a perfectly straight line that led from rats and pigeons all the
way up to bulls and hippopotami.
Physicists were used to discovering beautiful equations like this lurking in the
phenomena they studied, but mathematical elegance was a rarity in the
comparatively messy world of biology. But the more species Kleiber and his
peers analyzed, the clearer the equation became: metabolism scales to mass to
the negative quarter power. The math is simple enough: you take the square root
of 1,000, which is (approximately) 31, and then take the square root of 31, which
is (again, approximately) 5.5. This means that a cow, which is roughly a
thousand times heavier than a woodchuck, will, on average, live 5.5 times
longer, and have a heart rate that is 5.5 times slower than the woodchuck’s. As
the science writer George Johnson once observed, one lovely consequence of
Kleiber’s law is that the number of heartbeats per lifetime tends to be stable from
species to species. Bigger animals just take longer to use up their quota.
Over the ensuing decades, Kleiber’s law was extended down to the
microscopic scale of bacteria and cell metabolism; even plants were found to
obey negative quarter-power scaling in their patterns of growth. Wherever life
appeared, whenever an organism had to figure out a way to consume and
distribute energy through a body, negative quarter-power scaling governed the
patterns of its development.
Several years ago, the theoretical physicist Geoffrey West decided to
investigate whether Kleiber’s law applied to one of life’s largest creations: the
superorganisms of human-built cities. Did the “metabolism” of urban life slow
down as cities grew in size? Was there an underlying pattern to the growth and
pace of life of metropolitan systems? Working out of the legendary Santa Fe
Institute, where he served as president until 2009, West assembled an
international team of researchers and advisers to collect data on dozens of cities
around the world, measuring everything from crime to household electrical
consumption, from new patents to gasoline sales.
When they finally crunched the numbers, West and his team were delighted to
discover that Kleiber’s negative quarter-power scaling governed the energy and
transportation growth of city living. The number of gasoline stations, gasoline
sales, road surface area, the length of electrical cables: all these factors follow
the exact same power law that governs the speed with which energy is expended
in biological organisms. If an elephant was just a scaled-up mouse, then, from an
energy perspective, a city was just a scaled-up elephant.
But the most fascinating discovery in West’s research came from the data that
didn’t turn out to obey Kleiber’s law. West and his team discovered another
power law lurking in their immense database of urban statistics. Every datapoint
that involved creativity and innovation—patents, R&D budgets, “supercreative”
professions, inventors—also followed a quarter-power law, in a way that was
every bit as predictable as Kleiber’s law. But there was one fundamental
difference: the quarter-power law governing innovation was positive, not
negative. A city that was ten times larger than its neighbor wasn’t ten times more
innovative; it was seventeen times more innovative. A metropolis fifty times
bigger than a town was 130 times more innovative.
Kleiber’s law proved that as life gets bigger, it slows down. But West’s model
demonstrated one crucial way in which human-built cities broke from the
patterns of biological life: as cities get bigger, they generate ideas at a faster clip.
This is what we call “superlinear scaling”: if creativity scaled with size in a
straight, linear fashion, you would of course find more patents and inventions in
a larger city, but the number of patents and inventions per capita would be
stable. West’s power laws suggested something far more provocative: that
despite all the noise and crowding and distraction, the average resident of a
metropolis with a population of five million people was almost three times more
creative than the average resident of a town of a hundred thousand. “Great cities
are not like towns only larger,” Jane Jacobs wrote nearly fifty years ago. West’s
positive quarter-power law gave that insight a mathematical foundation.
Something about the environment of a big city was making its residents
significantly more innovative than residents of smaller towns. But what was it?
The 10/10 Rule
The first national broadcast of a color television program took place on January
1, 1954, when NBC aired an hour-long telecast of the Tournament of Roses
parade, and distributed it to twenty-two cities across the country. For those lucky
enough to see the program, the effect of a moving color image on a small screen
seems to have been mesmerizing. The New York Times, in typical language,
called it a “veritable bevy of hues and depth.” “To concentrate so much color
information within the frame of a small screen,” the Times wrote, “would be
difficult for even the most gifted artist doing a ‘still’ painting. To do it with
constantly moving pictures seemed pure wizardry.” Alas, the Rose Parade
“broadcast” turned out to be not all that broad, given that it was visible only on
prototype televisions in RCA showrooms. Color programming would not
become standard on prime-time shows until the late 1960s. After the advent of
color, the basic conventions that defined the television image would go
unchanged for decades. The delivery mechanisms began to diversify with the
introduction of VCRs and cable in the late 1970s. But the image remained the
same.
In the mid-1980s, a number of influential media and technology executives,
along with a few visionary politicians, had the eminently good idea that it was
time to upgrade the video quality of broadcast television. Speeches were
delivered, committees formed, experimental prototypes built, but it wasn’t until
July 23, 1996, that a Raleigh, North Carolina, CBS affiliate initiated the first
public transmission of an HDTV signal. Like the Tournament of Roses footage,
though, there were no ordinary consumers with sets capable of displaying its
“wizardry.”
1 A handful of broadcasters began transmitting HDTV signals in
1999, but HD television didn’t become a mainstream consumer phenomenon for
another five years. Even after the FCC mandated that all television stations cease
broadcasting the old analog standard on June 12, 2009, more than 10 percent of
U.S. households had televisions that went dark that day.
It is one of the great truisms of our time that we live in an age of technological
acceleration; the new paradigms keep rolling in, and the intervals between them
keep shortening. This acceleration reflects not only the flood of new products,
but also our growing willingness to embrace these strange new devices, and put
them to use. The waves roll in at ever-increasing frequencies, and more and
more of us are becoming trained surfers, paddling out to meet them the second
they start to crest. But the HDTV story suggests that this acceleration is hardly a
universal law. If you measure how quickly a new technology progresses from an
original idea to mass adoption, then it turns out that HDTV was traveling at the
exact same speed that color television had traveled four decades earlier. It took
ten years for color TV to go from the fringes to the mainstream; two generations
later, it took HDTV just as long to achieve mass success.
In fact, if you look at the entirety of the twentieth century, the most important
developments in mass, one-to-many communications clock in at the same social
innovation rate with an eerie regularity. Call it the 10/10 rule: a decade to build
the new platform, and a decade for it to find a mass audience. The technology
standard of amplitude-modulated radio—what we now call AM radio—evolved
in the first decade of the twentieth century. The first commercial AM station
began broadcasting in 1920, but it wasn’t until the late 1920s that radios became
a fixture in American households. Sony inaugurated research into the first
consumer videocassette recorder in 1969, but didn’t ship its first Betamax for
another seven years, and VCRs didn’t become a household necessity until the
mid-eighties. The DVD player didn’t statistically replace the VCR in American
households until 2006, nine years after the first players went on the market. Cell
phones, personal computers, GPS navigation devices—all took a similar time
frame to go from innovation to mass adoption.
Consider, as an alternate scenario, the story of Chad Hurley, Steve Chen, and
Jawed Karim, three former employees of the online payment site PayPal, who
decided in early 2005 that the Web was ripe for an upgrade in the way it handled
video and sound. Video, of course, was not native to the Web, which had begun
its life fifteen years before as a platform for academics to share hypertext
documents. But over the years, video clips had begun to trickle their way online,
thanks to new video standards that emerged, such as Quick-Time, Flash, or
Windows Media Player. But the mechanisms that allowed people to upload and
share their own videos were too challenging for most ordinary users. So Hurley,
Chen, and Karim cobbled together a rough beta for a service that would correct
these deficiencies, raised less than $10 million in venture capital, hired about
two dozen people, and launched YouTube, a website that utterly transformed the
way video information is shared online. Within sixteen months of the company’s
founding, the service was streaming more than 30 million videos a day. Within
two years, YouTube was one of the top-ten most visited sites on the Web. Before
Hurley, Chen, and Karim hit upon their idea for a start-up, video on the Web was
as common as subtitles on television. The Web was about doing things with text,
and uploading the occasional photo. YouTube brought Web video into the
mainstream.
Now compare the way these two ideas—HDTV and YouTube— changed the
basic rules of engagement for their respective platforms. Going from analog
television to HDTV is a change in degree, not in kind: there are more pixels; the
sound is more immersive; the colors are sharper. But consumers watch HDTV
the exact same way they watched old-fashioned analog TV. They choose a
channel, and sit back and watch. YouTube, on the other hand, radically altered
the basic rules of the medium. For starters, it made watching video on the Web a
mass phenomenon. But with YouTube you weren’t limited to sitting and
watching a show, television-style; you could also upload your own clips,
recommend or rate other clips, get into a conversation about them. With just a
few easy keystrokes, you could take a clip running on someone else’s site, and
drop a copy of it onto your own site. The technology allowed ordinary
enthusiasts to effectively program their own private television networks,
stitching together video clips from all across the planet.
Some will say that this is merely a matter of software, which is intrinsically
more adaptable than hardware like televisions or cellular phones. But before the
Web became mainstream in the mid-1990s, the pace of software innovation
followed the exact same 10/10 pattern of development that we saw in the spread
of other twentieth-century technologies. The graphical user interface, for
instance, dates back to a famous technology demo given by pioneering computer
scientist Doug Engelbart in 1968. During the 1970s, many of its core elements—
like the now ubiquitous desktop metaphor—were developed by researchers at
Xerox-PARC. But the first commercial product with a fully realized graphical
user interface didn’t ship until 1981, in the form of the Xerox Star workstation,
followed by the Macintosh in 1984, the first graphical user interface to reach a
mainstream, if niche, audience. But it wasn’t until the release of Windows 3.0 in
1990—almost exactly ten years after the Xerox Star hit the market—that
graphical user interfaces became the norm. The same pattern occurs in the
developmental history of other software genres, such as word processors,
spreadsheets, or e-mail clients. They were all built out of bits, not atoms, but
they took just as long to go from idea to mass success as HDTV did.
There are many ways to measure innovation, but perhaps the most elemental
yardstick, at least where technology is concerned, revolves around the job that
the technology in question lets you do. All other things being equal, a
breakthrough that lets you execute two jobs that were impossible before is twice
as innovative as a breakthrough that lets you do only one new thing. By that
measure, YouTube was significantly more innovative than HDTV, despite the
fact that HDTV was a more complicated technical problem. YouTube let you
publish, share, rate, discuss, and watch video more efficiently than ever before.
HDTV let you watch more pixels than ever before. But even with all those extra
layers of innovation, YouTube went from idea to mass adoption in less than two
years. Something about the Web environment had enabled Hurley, Chen, and
Karim to unleash a good idea on the world with astonishing speed. They took the
10/10 rule and made it 1/1.
This is a book about the space of innovation. Some environments squelch new
ideas; some environments seem to breed them effortlessly. The city and the Web
have been such engines of innovation because, for complicated historical
reasons, they are both environments that are powerfully suited for the creation,
diffusion, and adoption of good ideas. Neither environment is perfect, by any
means. (Think of crime rates in big cities, or the explosion of spam online.) But
both the city and the Web possess an undeniable track record at generating
innovation.
2
In the same way, the “myriad tiny architects” of Darwin’s coral reef
create an environment where biological innovation can flourish. If we want to
understand where good ideas come from, we have to put them in context.
Darwin’s world-changing idea unfolded inside his brain, but think of all the
environments and tools he needed to piece it together: a ship, an archipelago, a
notebook, a library, a coral reef. Our thought shapes the spaces we inhabit, and
our spaces return the favor. The argument of this book is that a series of shared
properties and patterns recur again and again in unusually fertile environments. I
have distilled them down into seven patterns, each one occupying a separate
chapter. The more we embrace these patterns—in our private work habits and
hobbies, in our office environments, in the design of new software tools—the
better we will be at tapping our extraordinary capacity for innovative thinking.
3
These patterns turn out to have a long history, much older than most of the
systems that we conventionally associate with innovation. This history is
particularly rich because it is not exclusively limited to human creations like the
Internet or the metropolis. The amplification and adoption of useful innovation
exist throughout natural history as well. Coral reefs are sometimes called the
“cities of the sea,” and part of the argument of this book is that we need to take
the metaphor seriously: the reef ecosystem is so innovative in its exploitation of
those nutrient-poor waters because it shares some defining characteristics with
actual cities. In the language of complexity theory, these patterns of innovation
and creativity are fractal: they reappear in recognizable form as you zoom in and
out, from molecule to neuron to pixel to sidewalk. Whether you’re looking at the
original innovations of carbon-based life, or the explosion of new software tools
on the Web, the same shapes keep turning up. When life gets creative, it has a
tendency to gravitate toward certain recurring patterns, whether those patterns
are emergent and self-organizing, or whether they are deliberately crafted by
human agents.
It may seem odd to talk about such different regions of experience as though
they were interchangeable. But in fact, we are constantly making equivalent
conceptual leaps from biology to culture without blinking. It is not a figure of
speech to say that the pattern of “competition”—a term often associated with
innovation—plays a critical role in the behavior of marketplaces, in the
interaction between a swarm of sperm cells and an egg, and in the ecosystemscale battle between organisms for finite energy sources. We are not using a
metaphor of economic competition to describe the struggles of those sperm cells:
the meaning of the word “competition” is wide (or perhaps deep) enough to
encompass sperm cells and corporations. The same principle applies to the seven
patterns I have assembled here.
Traveling across these different environments and scales is not merely
intellectual tourism. Science long ago realized that we can understand something
better by studying its behavior in different contexts. When we want to answer a
question like “Why has the Web been so innovative?” we naturally invoke
thoughts of its creators, and the workspaces, organizations, and information
networks they used in building it. But it turns out that we can answer the
question more comprehensively if we draw analogies to patterns of innovation
that we see in ecosystems like Darwin’s coral reef, or in the structure of the
human brain. We have no shortage of theories to instruct us how to make our
organizations more creative, or explain why tropical rain forests engineer so
much molecular diversity. What we lack is a unified theory that describes the
common attributes shared by all those innovation systems. Why is a coral reef
such an engine of biological innovation? Why do cities have such an extensive
history of idea creation? Why was Darwin able to hit upon a theory that so many
brilliant contemporaries of his missed? No doubt there are partial answers to
these questions that are unique to each situation, and each scale: the ecological
history of the reef; the sociology of urban life; the intellectual biography of a
scientist. But the argument of this book is that there are other, more interesting
answers that are applicable to all three situations, and that by approaching the
problem in this fractal, cross-disciplinary way, new insights become visible.
Watching the ideas spark on these different scales reveals patterns that singlescale observations easily miss or undervalue.
I call that vantage point the long zoom. It can be imagined as a kind of
hourglass:
As you descend toward the center of the glass, the biological scales contract:
from the global, deep time of evolution to the microscopic exchanges of neurons
or DNA. At the center of the glass, the perspective shifts from nature to culture,
and the scales widen: from individual thoughts and private workspaces to
immense cities and global information networks. When we look at the history of
innovation from the vantage point of the long zoom, what we find is that
unusually generative environments display similar patterns of creativity at
multiple scales simultaneously. You can’t explain the bio-diversity of the coral
reef by simply studying the genetics of the coral itself. The reef generates and
sustains so many different forms of life because of patterns that recur on the
scales of cells, organisms, and the wider ecosystem itself. The sources of
innovation in the city and the Web are equally fractal. In this sense, seeing the
problem of innovation from the long-zoom perspective does not just give us new
metaphors. It gives us new facts.
The pattern of “competition” is an excellent case in point. Every economics
textbook will tell you that competition between rival firms leads to innovation in
their products and services. But when you look at innovation from the longzoom perspective, competition turns out to be less central to the history of good
ideas than we generally think. Analyzing innovation on the scale of individuals
and organizations—as the standard textbooks do—distorts our view. It creates a
picture of innovation that overstates the role of proprietary research and
“survival of the fittest” competition. The long-zoom approach lets us see that
openness and connectivity may, in the end, be more valuable to innovation than
purely competitive mechanisms. Those patterns of innovation deserve
recognition—in part because it’s intrinsically important to understand why good
ideas emerge historically, and in part because by embracing these patterns we
can build environments that do a better job of nurturing good ideas, whether
those environments are schools, governments, software platforms, poetry
seminars, or social movements. We can think more creatively if we open our
minds to the many connected environments that make creativity possible.
The academic literature on innovation and creativity is rich with subtle
distinctions between innovations and inventions, between different modes of
creativity: artistic, scientific, technological. I have deliberately chosen the
broadest possible phrasing—good ideas—to suggest the cross-disciplinary
vantage point I am trying to occupy. The good ideas in this survey range from
software platforms to musical genres to scientific paradigms to new models for
government. My premise is that there is as much value to be found in seeking the
common properties across all these varied forms of innovation and creativity as
there is value to be found in documenting the differences between them. The
poet and the engineer (and the coral reef) may seem a million miles apart in their
particular forms of expertise, but when they bring good ideas into the world,
similar patterns of development and collaboration shape that process.
If there is a single maxim that runs through this book’s arguments, it is that we
are often better served by connecting ideas than we are by protecting them. Like
the free market itself, the case for restricting the flow of innovation has long
been buttressed by appeals to the “natural” order of things. But the truth is, when
one looks at innovation in nature and in culture, environments that build walls
around good ideas tend to be less innovative in the long run than more openended environments. Good ideas may not want to be free, but they do want to
connect, fuse, recombine. They want to reinvent themselves by crossing
conceptual borders. They want to complete each other as much as they want to
compete.
I.
THE ADJACENT POSSIBLE
Sometime in the late 1870s, a Parisian obstetrician named Stephane Tarnier took
a day off from his work at Maternité de Paris, the lying-in hospital for the city’s
poor women, and paid a visit to the nearby Paris Zoo. Wandering past the
elephants and reptiles and classical gardens of the zoo’s home inside the Jardin
des Plantes, Tarnier stumbled across an exhibit of chicken incubators. Seeing the
hatchlings totter about in the incubator’s warm enclosure triggered an
association in his head, and before long he had hired Odile Martin, the zoo’s
poultry raiser, to construct a device that would perform a similar function for
human newborns. By modern standards, infant mortality was staggeringly high
in the late nineteenth century, even in a city as sophisticated as Paris. One in five
babies died before learning to crawl, and the odds were far worse for premature
babies born with low birth weights. Tarnier knew that temperature regulation
was critical for keeping these infants alive, and he knew that the French medical
establishment had a deep-seated obsession with statistics. And so as soon as his
newborn incubator had been installed at Maternité, the fragile infants warmed by
hot water bottles below the wooden boxes, Tarnier embarked on a quick study of
five hundred babies. The results shocked the Parisian medical establishment:
while 66 percent of low-weight babies died within weeks of birth, only 38
percent died if they were housed in Tarnier’s incubating box. You could
effectively halve the mortality rate for premature babies simply by treating them
like hatchlings in a zoo.
Tarnier’s incubator was not the first device employed for warming newborns,
and the contraption he built with Martin would be improved upon significantly
in the subsequent decades. But Tarnier’s statistical analysis gave newborn
incubation the push that it needed: within a few years, the Paris municipal board
required that incubators be installed in all the city’s maternity hospitals. In 1896,
an enterprising physician named Alexandre Lion set up a display of incubators—
with live newborns—at the Berlin Exposition. Dubbed the Kinderbrutenstalt, or
“child hatchery,” Lion’s exhibit turned out to be the sleeper hit of the exposition,
and launched a bizarre tradition of incubator sideshows that persisted well into
the twentieth century. (Coney Island had a permanent baby incubator show until
the early 1940s.) Modern incubators, supplemented with high-oxygen therapy
and other advances, became standard equipment in all American hospitals after
the end of World War II, triggering a spectacular 75 percent decline in infant
mortality rates between 1950 and 1998. Because incubators focus exclusively on
the beginning of life, their benefit to public health—measured by the sheer
number of extra years they provide—rivals any medical advance of the twentieth
century. Radiation therapy or a double bypass might give you another decade or
two, but an incubator gives you an entire lifetime.
In the developing world, however, the infant mortality story remains bleak.
Whereas infant deaths are below ten per thousand births throughout Europe and
the United States, over a hundred infants die per thousand in countries like
Liberia and Ethiopia, many of them premature babies that would have survived
with access to incubators. But modern incubators are complex, expensive things.
A standard incubator in an American hospital might cost more than $40,000. But
the expense is arguably the smaller hurdle to overcome. Complex equipment
breaks, and when it breaks you need the technical expertise to fix it, and you
need replacement parts. In the year that followed the 2004 Indian Ocean
tsunami, the Indonesian city of Meulaboh received eight incubators from a range
of international relief organizations. By late 2008, when an MIT professor
named Timothy Prestero visited the hospital, all eight were out of order, the
victims of power surges and tropical humidity, along with the hospital staff’s
inability to read the English repair manual. The Meulaboh incubators were a
representative sample: some studies suggest that as much as 95 percent of
medical technology donated to developing countries breaks within the first five
years of use.
Prestero had a vested interest in those broken incubators, because the
organization he founded, Design that Matters, had been working for several
years on a new scheme for a more reliable, and less expensive, incubator, one
that recognized complex medical technology was likely to have a very different
tenure in a developing world context than it would in an American or European
hospital. Designing an incubator for a developing country wasn’t just a matter of
creating something that worked; it was also a matter of designing something that
would break in a non-catastrophic way. You couldn’t guarantee a steady supply
of spare parts, or trained repair technicians. So instead, Prestero and his team
decided to build an incubator out of parts that were already abundant in the
developing world. The idea had originated with a Boston doctor named Jonathan
Rosen, who had observed that even the smaller towns of the developing world
seemed to be able to keep automobiles in working order. The towns might have
lacked air conditioning and laptops and cable television, but they managed to
keep their Toyota 4Runners on the road. So Rosen approached Prestero with an
idea: What if you made an incubator out of automobile parts?
Three years after Rosen suggested the idea, the Design that Matters team
introduced a prototype device called the NeoNurture. From the outside, it looked
like a streamlined modern incubator, but its guts were automotive. Sealed-beam
headlights supplied the crucial warmth; dashboard fans provided filtered air
circulation; door chimes sounded alarms. You could power the device via an
adapted cigarette lighter, or a standard-issue motorcycle battery. Building the
NeoNurture out of car parts was doubly efficient, because it tapped both the
local supply of parts themselves and the local knowledge of automobile repair.
These were both abundant resources in the developing world context, as Rosen
liked to say. You didn’t have to be a trained medical technician to fix the
NeoNurture; you didn’t even have to read the manual. You just needed to know
how to replace a broken headlight.
Good ideas are like the NeoNurture device. They are, inevitably, constrained
by the parts and skills that surround them. We have a natural tendency to
romanticize breakthrough innovations, imagining momentous ideas transcending
their surroundings, a gifted mind somehow seeing over the detritus of old ideas
and ossified tradition. But ideas are works of bricolage; they’re built out of that
detritus. We take the ideas we’ve inherited or that we’ve stumbled across, and
we jigger them together into some new shape. We like to think of our ideas as
$40,000 incubators, shipped direct from the factory, but in reality they’ve been
cobbled together with spare parts that happened to be sitting in the garage.
Before his untimely death in 2002, the evolutionary biologist Stephen Jay Gould
maintained an odd collection of footware that he had purchased during his
travels through the developing world, in open-air markets in Quito, Nairobi, and
Delhi. They were sandals made from recycled automobile tires. As a fashion
statement, they may not have amounted to much, but Gould treasured his tire
sandals as a testimony to “human ingenuity.” But he also saw them as a
metaphor for the patterns of innovation in the biological world. Nature’s
innovations, too, rely on spare parts. Evolution advances by taking available
resources and cobbling them together to create new uses. The evolutionary
theorist François Jacob captured this in his concept of evolution as a “tinkerer,”
not an engineer; our bodies are also works of bricolage, old parts strung together
to form something radically new. “The tires-to-sandals principle works at all
scales and times,” Gould wrote, “permitting odd and unpredictable initiatives at
any moment—to make nature as inventive as the cleverest person who ever
pondered the potential of a junkyard in Nairobi.”
You can see this process at work in the primordial innovation of life itself. We
do not yet have scientific consensus on the specifics of life’s origins. Some
believe life originated in the boiling, metallic vents of undersea volcanoes;
others suspect the open oceans; others point to the tidal ponds where Darwin
believed life first took hold. Many respected scientists think that life may have
arrived from outer space, embedded in a meteor. But we have a much clearer
picture of the composition of earth’s atmosphere before life emerged, thanks to a
field known as prebiotic chemistry. The lifeless earth was dominated by a
handful of basic molecules: ammonia, methane, water, carbon dioxide, a
smattering of amino acids, and other simple organic compounds. Each of these
molecules was capable of a finite series of transformations and exchanges with
other molecules in the primordial soup: methane and oxygen recombining to
form formaldehyde and water, for instance.
Think of all those initial molecules, and then imagine all the potential new
combinations that they could form spontaneously, simply by colliding with each
other (or perhaps prodded along by the extra energy of a propitious lightning
strike). If you could play God and trigger all those combinations, you would end
up with most of the building blocks of life: the proteins that form the boundaries
of cells; sugar molecules crucial to the nucleic acids of our DNA. But you would
not be able to trigger chemical reactions that would build a mosquito, or a
sunflower, or a human brain. Formaldehyde is a first-order combination: you can
create it directly from the molecules in the primordial soup. The atomic elements
that make up a sunflower are the very same ones available on earth before the
emergence of life, but you can’t spontaneously create a sunflower in that
environment, because it relies on a whole series of subsequent innovations that
wouldn’t evolve on earth for billions of years: chloroplasts to capture the sun’s
energy, vascular tissues to circulate resources through the plant, DNA molecules
to pass on sunflower-building instructions to the next generation.
The scientist Stuart Kauffman has a suggestive name for the set of all those
first-order combinations: “the adjacent possible.” The phrase captures both the
limits and the creative potential of change and innovation. In the case of
prebiotic chemistry, the adjacent possible defines all those molecular reactions
that were directly achievable in the primordial soup. Sunflowers and mosquitoes
and brains exist outside that circle of possibility. The adjacent possible is a kind
of shadow future, hovering on the edges of the present state of things, a map of
all the ways in which the present can reinvent itself. Yet is it not an infinite
space, or a totally open playing field. The number of potential first-order
reactions is vast, but it is a finite number, and it excludes most of the forms that
now populate the biosphere. What the adjacent possible tells us is that at any
moment the world is capable of extraordinary change, but only certain changes
can happen.
The strange and beautiful truth about the adjacent possible is that its
boundaries grow as you explore those boundaries. Each new combination ushers
new combinations into the adjacent possible. Think of it as a house that
magically expands with each door you open. You begin in a room with four
doors, each leading to a new room that you haven’t visited yet. Those four rooms
are the adjacent possible. But once you open one of those doors and stroll into
that room, three new doors appear, each leading to a brand-new room that you
couldn’t have reached from your original starting point. Keep opening new doors
and eventually you’ll have built a palace.
Basic fatty acids will naturally self-organize into spheres lined with a dual
layer of molecules, very similar to the membranes that define the boundaries of
modern cells. Once the fatty acids combine to form those bounded spheres, a
new wing of the adjacent possible opens up, because those molecules implicitly
create a fundamental division between the inside and outside of the sphere. This
division is the very essence of a cell. Once you have an “inside,” you can put
things there: food, organelles, genetic code. Small molecules can pass through
the membrane and then combine with other molecules to form larger entities too
big to escape back through the boundaries of the proto-cell. When the first fatty
acids spontaneously formed those dual-layered membranes, they opened a door
into the adjacent possible that would ultimately lead to nucleotide-based genetic
code, and the power plants of the chloroplasts and mitochondria—the primary
“inhabitants” of all modern cells.
The same pattern appears again and again throughout the evolution of life.
Indeed, one way to think about the path of evolution is as a continual exploration
of the adjacent possible. When dinosaurs such as the velociraptor evolved a new
bone called the semilunate carpal (the name comes from its half-moon shape), it
enabled them to swivel their wrists with far more flexibility. In the short term,
this gave them more dexterity as predators, but it also opened a door in the
adjacent possible that would eventually lead, many millions of years later, to the
evolution of wings and flight. When our ancestors evolved opposable thumbs,
they opened up a whole new cultural branch of the adjacent possible: the
creation and use of finely crafted tools and weapons.
One of the things that I find so inspiring in Kauffman’s notion of the adjacent
possible is the continuum it suggests between natural and man-made systems. He
introduced the concept in part to illustrate a fascinating secular trend shared by
both natural and human history: this relentless pushing back against the
barricades of the adjacent possible. “Something has obviously happened in the
past 4.8 billion years,” he writes. “The biosphere has expanded, indeed, more or
less persistently exploded, into the ever-expanding adjacent possible. . . . It is
more than slightly interesting that this fact is clearly true, that it is rarely
remarked upon, and that we have no particular theory for this expansion.” Four
billion years ago, if you were a carbon atom, there were a few hundred
molecular configurations you could stumble into. Today that same carbon atom,
whose atomic properties haven’t changed one single nanogram, can help build a
sperm whale or a giant redwood or an H1N1 virus, along with a near-infinite list
of other carbon-based life forms that were not part of the adjacent possible of
prebiotic earth. Add to that an equally formidable list of human concoctions that
rely on carbon—every single object on the planet made of plastic, for instance—
and you can see how far the kingdom of the adjacent possible has expanded
since those fatty acids self-assembled into the first membrane.
The history of life and human culture, then, can be told as the story of a gradual
but relentless probing of the adjacent possible, each new innovation opening up
new paths to explore. But some systems are more adept than others at exploring
those possibility spaces. The mystery of Darwin’s paradox that we began with
ultimately revolves around the question of why a coral reef ecosystem should be
so adventurous in its exploration of the adjacent possible—so many different life
forms sharing such a small space—while the surrounding waters of the ocean
lack that same marvelous diversity. Similarly, the environments of big cities
allow far more commercial exploration of the adjacent possible than towns or
villages, allowing tradesmen and entrepreneurs to specialize in fields that would
be unsustainable in smaller population centers. The Web has explored the
adjacent possible of its medium far faster than any other communications
technology in history. In early 1994, the Web was a text-only medium, pages of
words connected by hyperlinks. But within a few years, the possibility space
began to expand. It became a medium that let you do financial transactions,
which turned it into a shopping mall and an auction house and a casino. Shortly
afterward, it became a true two-way medium where it was as easy to publish
your own writing as it was to read other people’s, which engendered forms that
the world had never seen before: user-authored encyclopedias, the blogosphere,
social network sites. YouTube made the Web one of the most influential video
delivery mechanisms on the planet. And now digital maps are unleashing their
own cartographic revolutions.
You can see the fingerprints of the adjacent possible in one of the most
remarkable patterns in all of intellectual history, what scholars now call “the
multiple”: A brilliant idea occurs to a scientist or inventor somewhere in the
world, and he goes public with his remarkable finding, only to discover that
three other minds had independently come up with the same idea in the past
year. Sunspots were simultaneously discovered in 1611 by four scientists living
in four different countries. The first electrical battery was invented separately by
Dean Von Kleist and Cuneus of Leyden in 1745 and 1746. Joseph Priestley and
Carl Wilhelm Scheele independently isolated oxygen between 1772 and 1774.
The law of the conservation of energy was formulated separately four times in
the late 1840s. The evolutionary importance of genetic mutation was proposed
by S. Korschinsky in 1899 and then by Hugo de Vries in 1901, while the impact
of X-rays on mutation rates was independently uncovered by two scholars in
1927. The telephone, telegraph, steam engine, photograph vacuum tube, radio—
just about every essential technological advance of modern life has a multiple
lurking somewhere in its origin story.
In the early 1920s, two Columbia University scholars named William Ogburn
and Dorothy Thomas decided to track down as many multiples as they could
find, eventually publishing their survey in an influential essay with the delightful
title “Are Inventions Inevitable?” Ogburn and Thomas found 148 instances of
independent innovation, most them occurring within the same decade. Reading
the list now, one is struck not just by the sheer number of cases, but how
indistinguishable the list is from an unfiltered history of big ideas. Multiples
have been invoked to support hazy theories about the “zeitgeist,” but they have a
much more grounded explanation. Good ideas are not conjured out of thin air;
they are built out of a collection of existing parts, the composition of which
expands (and, occasionally, contracts) over time. Some of those parts are
conceptual: ways of solving problems, or new definitions of what constitutes a
problem in the first place. Some of them are, literally, mechanical parts. To go
looking for oxygen, Priestley and Scheele needed the conceptual framework that
the air was itself something worth studying and that it was made up of distinct
gases; neither of these ideas became widely accepted until the second half of the
eighteenth century. But they also needed the advanced scales that enabled them
to measure the minuscule changes in weight triggered by oxidation, technology
that was itself only a few decades old in 1774. When those parts became
available, the discovery of oxygen entered the realm of the adjacent possible.
Isolating oxygen was, as the saying goes, “in the air,” but only because a specific
set of prior discoveries and inventions had made that experiment thinkable.
The adjacent possible is as much about limits as it is about openings. At every
moment in the timeline of an expanding biosphere, there are doors that cannot be
unlocked yet. In human culture, we like to think of breakthrough ideas as sudden
accelerations on the timeline, where a genius jumps ahead fifty years and invents
something that normal minds, trapped in the present moment, couldn’t possibly
have come up with. But the truth is that technological (and scientific) advances
rarely break out of the adjacent possible; the history of cultural progress is,
almost without exception, a story of one door leading to another door, exploring
the palace one room at a time. But of course, human minds are not bound by the
finite laws of molecule formation, and so every now and then an idea does occur
to someone that teleports us forward a few rooms, skipping some exploratory
steps in the adjacent possible. But those ideas almost always end up being shortterm failures, precisely because they have skipped ahead. We have a phrase for
those ideas: we call them “ahead of their time.”
Consider the legendary Analytical Engine designed by nineteenth-century
British inventor Charles Babbage, who is considered by most technology
historians to be the father of modern computing, though he should probably be
called the great-grandfather of modern computing, because it took several
generations for the world to catch up to his idea. Babbage is actually in the
pantheon for two inventions, neither of which he managed to build during his
lifetime. The first was his Difference Engine, a fantastically complex fifteen-ton
contraption, with over 25,000 mechanical parts, designed to calculate
polynomial functions that were essential to creating the trigonometric tables
crucial to navigation. Had Babbage actually completed his project, the
Difference Engine would have been the world’s most advanced mechanical
calculator. When the London Science Museum constructed one from Babbage’s
plans to commemorate the centennial of his death, the machine returned accurate
results to thirty-one places in a matter of seconds. Both the speed and precision
of the device would have exceeded anything else possible in Babbage’s time by
several orders of magnitude.
For all its complexity, however, the Difference Engine was well within the
adjacent possible of Victorian technology. The second half of the nineteenth
century saw a steady stream of improvements to mechanical calculation, many
of them building on Babbage’s architecture. The Swiss inventor Per Georg
Scheutz constructed a working Difference Engine that debuted at the Exposition
Universelle of 1855; within two decades the piano-sized Scheutz design had
been reduced to the size of a sewing machine. In 1884, an American inventor
named William S. Burroughs founded the American Arithmometer Company to
sell mass-produced calculators to businesses around the country. (The fortune
generated by those machines would help fund his namesake grandson’s writing
career, not to mention his drug habit, almost a century later.) Babbage’s design
for the Difference Engine was a work of genius, no doubt, but it did not
transcend the adjacent possible of its day.
The same cannot be said of Babbage’s other brilliant idea: the Analytical
Engine, the great unfulfilled project of Babbage’s career, which he toiled on for
the last thirty years of his life. The machine was so complicated that it never got
past the blueprint stage, save a small portion that Babbage built shortly before
his death in 1871. The Analytical Engine was—on paper, at least—the world’s
first programmable computer. Being programmable meant that the machine was
fundamentally open-ended; it wasn’t designed for a specific set of tasks, the way
the Difference Engine had been optimized for polynomial equations. The
Analytical Engine was, like all modern computers, a shape-shifter, capable of
reinventing itself based on the instructions conjured by its programmers. (The
brilliant mathematician Ada Lovelace, the only daughter of Lord Byron, wrote
several sets of instructions for Babbage’s still-vaporware Analytical Engine,
earning her the title of the world’s first programmer.) Babbage’s design for the
engine anticipated the basic structure of all contemporary computers:
“programs” were to be inputted via punch cards, which had been invented
decades before to control textile looms; instructions and data were captured in a
“store,” the equivalent of what we now call random access memory, or RAM;
and calculations were executed via a system that Babbage called “the mill,”
using industrial-era language to describe what we now call the central processing
unit, or CPU.
Babbage had most of this system sketched out by 1837, but the first true
computer to use this programmable architecture didn’t appear for more than a
hundred years. While the Difference Engine engendered an immediate series of
refinements and practical applications, the Analytical Engine effectively
disappeared from the map. Many of the pioneering insights that Babbage had hit
upon in the 1830s had to be independently rediscovered by the visionaries of
World War II-era computer science.
Why did the Analytical Engine prove to be such a short-term dead end, given
the brilliance of Babbage’s ideas? The fancy way to say it is that his ideas had
escaped the bounds of the adjacent possible. But it is perhaps better put in more
prosaic terms: Babbage simply didn’t have the right spare parts. Even if Babbage
had built a machine to his specs, it is unclear whether it would have worked,
because Babbage was effectively sketching out a machine for the electronic age
during the middle of the steam-powered mechanical revolution. Unlike all
modern computers, Babbage’s machine was to be composed entirely of
mechanical gears and switches, staggering in their number and in the intricacy of
their design. Information flowed through the system as a constant ballet of metal
objects shifting positions in carefully choreographed movements. It was a
maintenance nightmare, but more than that, it was bound to be hopelessly slow.
Babbage bragged to Ada Lovelace that he believed the machine would be able to
multiply two twenty-digit numbers in three minutes. Even if he was right—
Babbage wouldn’t have been the first tech entrepreneur to exaggerate his
product’s performance—that kind of processing time would have made
executing more complicated programs torturously slow. The first computers of
the digital age could perform the same calculation in a matter of seconds. An
iPhone completes millions of such calculations in the same amount of time.
Programmable computers needed vacuum tubes, or, even better, integrated
circuits, where information flows as tiny pulses of electrical activity, instead of
clanking, rusting, steam-powered metal gears.
You can see a comparable pattern—on a vastly accelerated timetable—in the
story of YouTube. Had Hurley, Chen, and Karim tried to execute the exact same
idea for YouTube ten years earlier, in 1995, it would have been a spectacular
flop, because a site for sharing video was not within the adjacent possible of the
early Web. For starters, the vast majority of Web users were on painfully slow
dial-up connections that could sometimes take minutes to download a small
image. (The average two-minute-long YouTube clip would have taken as much
as an hour to download on the then-standard 14.4 bps modems.) Another key to
YouTube’s early success is that its developers were able to base the video
serving on Adobe’s Flash platform, which meant that they could focus on the
ease of sharing and discussing clips, and not spend millions of dollars
developing a whole new video standard from scratch. But Flash itself wasn’t
released until late 1996, and didn’t even support video until 2002.
To use our microbiology analogy, having the idea for a Difference Engine in
the 1830s was like a bunch of fatty acids trying to form a cell membrane.
Babbage’s calculating machine was a leap forward, to be sure, but as advanced
as it was, the Difference Engine was still within the bounds of the adjacent
possible, which is precisely why so many practical iterations of Babbage’s
design emerged in the subsequent decades. But trying to create an Analytical
Engine in 1850—or YouTube in 1995—was the equivalent of those fatty acids
trying to self-organize into a sea urchin. The idea was right, but the environment
wasn’t ready for it yet.
All of us live inside our own private versions of the adjacent possible. In our
work lives, in our creative pursuits, in the organizations that employ us, in the
communities we inhabit—in all these different environments, we are surrounded
by potential new configurations, new ways of breaking out of our standard
routines. We are, each of us, surrounded by the conceptual equivalent of those
Toyota spare parts, all waiting to be recombined into something magical,
something new. It need not be the epic advances of biological diversity, or the
invention of programmable computing. Unlocking a new door can lead to a
world-changing scientific breakthrough, but it can also lead to a more effective
strategy for teaching second-graders, or a novel marketing idea for the vacuum
cleaner your company’s about to release. The trick is to figure out ways to
explore the edges of possibility that surround you. This can be as simple as
changing the physical environment you work in, or cultivating a specific kind of
social network, or maintaining certain habits in the way you seek out and store
information.
Recall the question we began with: What kind of environment creates good
ideas? The simplest way to answer it is this: innovative environments are better
at helping their inhabitants explore the adjacent possible, because they expose a
wide and diverse sample of spare parts—mechanical or conceptual—and they
encourage novel ways of recombining those parts. Environments that block or
limit those new combinations—by punishing experimentation, by obscuring
certain branches of possibility, by making the current state so satisfying that no
one bothers to explore the edges—will, on average, generate and circulate fewer
innovations than environments that encourage exploration. The infinite variety
of life that so impressed Darwin, standing in the calm waters of the Keeling
Islands, exists because the coral reef is supremely gifted at recycling and
reinventing the spare parts of its ecosystem.
There’s a famous moment in the story of the near-catastrophic Apollo 13
mission—wonderfully captured in the Ron Howard film—where the mission
control engineers realize they need to create an improvised carbon dioxide filter,
or the astronauts will poison the lunar module atmosphere with their own
exhalations before they return to Earth. The astronauts have plenty of carbon
“scrubbers” on board, but these filters were designed for the original, damaged
spacecraft, and don’t fit the air ventilation system of the lunar module they are
using as a lifeboat to return home. Mission Control quickly assembles what it
calls a “tiger team” of engineers to hack their way through the problem, and
creates a rapid-fire inventory of all the available equipment currently on the
lunar module. In the movie, Deke Slayton, head of Flight Crew Operations,
tosses a jumbled pile of gear on a conference table: suit hoses, canisters, stowage
bags, duct tape, and other assorted gadgets. He holds up the carbon scrubbers.
“We gotta find a way to make this fit into a hole for this,” he says, and then
points to the spare parts on the table, “using nothing but that.”
The space gear on the table defines the adjacent possible for the problem of
building a working carbon scrubber on a lunar module. The device they
eventually concoct, dubbed the “mailbox,” performs beautifully. The canisters
and nozzles are like the ammonia and methane molecules of the early earth, or
Babbage’s mechanical gears, or those Toyota parts heating an incubator: they are
the building blocks that create—and limit—the space of possibility for a specific
problem. In a way, the engineers at Mission Control had it easier than most.
Challenging problems don’t usually define their adjacent possible in such a
clear, tangible way. Part of coming up with a good idea is discovering what
those spare parts are, and ensuring that you’re not just recycling the same old
ingredients. This, then, is where the next six patterns of innovation will take us,
because they all involve, in one way or another, tactics for assembling a more
eclectic collection of building block ideas, spare parts that can be reassembled
into useful new configurations. The trick to having good ideas is not to sit
around in glorious isolation and try to think big thoughts. The trick is to get more
parts on the table.
II.
LIQUID NETWORKS
There are a dozen different metaphors we use colloquially to describe good
ideas: we call them sparks, flashes, lightbulb moments; we have brainstorms and
breakthroughs, eureka moments and epiphanies. Something about the concept
pushes our language into rhetorical overdrive, our verbiage straining to
reproduce the innovation it describes.
And yet, florid as they are, none of those metaphors captures what an idea
actually is, on the most elemental level.
A good idea is a network. A specific constellation of neurons—thousands of
them—fire in sync with each other for the first time in your brain, and an idea
pops into your consciousness. A new idea is a network of cells exploring the
adjacent possible of connections that they can make in your mind. This is true
whether the idea in question is a new way to solve a complex physics problem,
or a closing line for a novel, or a feature for a software application. If we’re
going to try to explain the mystery of where ideas come from, we’ll have to start
by shaking ourselves free of this common misconception: an idea is not a single
thing. It is more like a swarm.
When you think about ideas in their native state of neural networks, two key
preconditions become clear. First, the sheer size of the network: you can’t have
an epiphany with only three neurons firing. The network needs to be densely
populated. Your brain has roughly 100 billion neurons, an impressive enough
number, but all those neurons would be useless for creating ideas (as well as all
the other achievements of the human brain) if they weren’t capable of making
such elaborate connections with each other. The average neuron connects to a
thousand other neurons scattered across the brain, which means that the adult
human brain contains 100 trillion distinct neuronal connections, making it the
largest and most complex network on earth. (By comparison, there are
somewhere on the order of 40 billion pages on the Web. If you assume an
average of ten links per page, that means you and I are walking around with a
high-density network in our skulls that is orders of magnitude larger than the
entirety of the World Wide Web.)
The second precondition is that the network be plastic, capable of adopting
new configurations. A dense network incapable of forming new patterns is, by
definition, incapable of change, incapable of probing at the edges of the adjacent
possible. When a new idea pops into your head, the sense of novelty that makes
the experience so magical has a direct correlate in the cells of your brain: a
brand-new assemblage of neurons has come together to make the thought
possible. Those connections are built by our genes and by personal experience:
some connections help regulate our heartbeat and trigger reflex reactions; others
conjure up vivid sense memories of the cookies we ate as children; others help
us invent the concept of a programmable computer. The connections are the key
to wisdom, which is why the whole notion of losing neurons after we hit adulthood is a red herring. What matters in your mind is not just the number of
neurons, but the myriad connections that have formed between them.
Of course, everything that happens in your brain is, technically speaking, a
network. Remembering to cut your toenails involves a network of neurons firing
in some kind of orderly fashion. But that doesn’t make it an epiphany. It turns
out that good ideas have certain signature patterns in the networks that make
them. The creating brain behaves differently from the brain that is performing a
repetitive task. The neurons communicate in different ways. The networks take
on distinct shapes.
The question is how to push your brain toward those more creative networks.
The answer, as it happens, is delightfully fractal: to make your mind more
innovative, you have to place it inside environments that share that same
network signature: networks of ideas or people that mimic the neural networks
of a mind exploring the boundaries of the adjacent possible. Certain
environments enhance the brain’s natural capacity to make new links of
association. But these patterns of connection are much older than the human
brain, older than neurons even. They take us back, once again, to the origin of
life itself.
As far as we know, “carbon-based life” is a redundant expression: life would be
impossible without the carbon atom. Most astrobiologists—scientists who study
the possibility of life elsewhere in the universe—believe that if we are ever to
discover convincing evidence of extraterrestrial life, be it on Mars or in some
distant galaxy, it, too, will turn out to be carbon-based.
Why are we so confident about carbon’s essential role in creating living
things? The answer has to do with the core properties of the carbon atom itself.
Carbon has four valence electrons residing in the outermost shell of the atom,
which, for complicated reasons, makes it uniquely talented at forming
connections with other atoms, particularly with hydrogen, nitrogen, oxygen,
phosphorus, sulfur—and, crucially, with other carbon atoms. These six atoms
make up 99 percent of the dry weight of all living organisms on earth. Those
four valence bonds give carbon a strong propensity for forming elaborate chains
and rings of polymers: everything from the genetic information stored in nucleic
acids, to the building blocks of proteins, to the energy storage of carbohydrates
and fats. (Modern technology has exploited the generative potential of the
carbon atom via the artificial polymers we call plastics.) Carbon atoms measure
only 0.03 percent of the overall composition of the earth’s crust, and yet they
make up nearly 20 percent of our body mass. That abundance highlights the
unique property of the carbon atom: its combinatorial power. Carbon is a
connector.
Those connections are essential for the day-to-day functioning of life: chains
of nucleic acids instructing amino acids to assemble into long strings of protein,
powered by the stored energy of carbohydrates. But the connective properties of
carbon were essential to the original innovations of life itself. Without carbon’s
innate talent for forming new complex molecules with other atoms, it is hard to
imagine how the first organisms would have evolved. Those four valence
electrons allowed the prebiotic earth to explore its own adjacent possible, sifting
through the long list of potential molecular combinations until it hit upon a series
of stable chemical reactions that blossomed into the first organisms. Without the
generative links of carbon, the earth would have likely remained a lifeless soup
of elements, a planet of dead chemistry.
Carbon’s connective talents lie at the center of one of the most famous
scientific experiments of the twentieth century. In 1953, two University of
Chicago professors, Stanley L. Miller and Harold C. Urey, created a closed
system of glass tubes and flasks that simulated the early conditions of the
prebiotic earth. The main ingredients were methane (CH4
), ammonia (NH3
),
hydrogen (H2
), and water (H2O). Only the methane contained carbon atoms. One
flask connected to the chemical soup contained a pair of electrodes, which Miller
and Urey used to simulate lightning by triggering a series of quick sparks
between them. They ran the experiment for seven straight days, and by the time
they had completed the first cycle, they found that more than 10 percent of the
carbon had spontaneously recombined into many of the organic compounds
essential to life: sugars, lipids, nucleic acids. Miller claimed at the time that “just
turning on the spark in a basic pre-biotic experiment” produced half of the
twenty-two amino acids. Several years ago, a team reanalyzed the original flasks
from the Miller-Urey experiments, and found that in one version—which
simulated the environment around an undersea volcano—all twenty-two amino
acids had been created.
In the half-century that has passed since Miller and Urey triggered their
primordial spark, hundreds of rival theories have emerged to explain the early
stages of life, some emphasizing the initial development of self-replication, some
emphasizing the development of metabolism; some are predicated on the intense
heat of undersea vents, others on life-bearing comets colliding with the earth’s
surface. But all of these theories share a common motif: the combinatorial power
of the carbon atom. A few researchers and science-fiction authors have
speculated on an alternate scenario, where life emerges around the silicon atom.
Silicon sits directly below carbon on the periodic table, and shares its four
valence electrons. But silicon lacks carbon’s unique versatility, its ability to form
the double and triple bonds that create the long chains and rings of fatty acids
and sugars. Silicon also requires far more energy to form bonds than carbon
does. Tellingly, the earth contains over a hundred times as much silicon as it
does carbon, and yet Mother Nature decided to base life on the much rarer
element.
Silicon-based life may be impossible for one other reason: silicon bonds
readily dissolve in water. Most theories of life’s origin depend on H20 not
merely because hydrogen and oxygen are important elements in many organic
compounds, but also because the environment of liquid water facilitated the
early “chemistry experiments” that led to the emergence of life. The Miller-Urey
experiment was, in a way, an attempt to test more rigorously a hunch that
Charles Darwin had had a century before about the watery origins of life. In a
letter to the botanist Joseph Hooker, Darwin speculated that life had first
emerged in “some warm little pond, with all sorts of ammonia and phosphoric
salts, light, heat, electricity.” Most theories of life’s origins incorporate some
variation of the “primordial soup”: an environment where novel combinations
could occur thanks to the swirl and flow of liquid. Carbon may be a talented
connector, but without a medium that allows it to collide randomly with other
elements, those connective powers are likely to go to waste. All those
spectacular polymer chains would remain unrealized, hidden behind the locked
doors of the adjacent possible.
Like carbon, the H2O molecule possesses several exceptional properties that
make the medium of liquid water uniquely suited to sustain early life. The
hydrogen bonds that form between distinct water molecules are about ten times
stronger than equivalent bonds in “normal” liquids, which gives the medium
several crucial properties. For starters, the temperature range at which water
remains in liquid form is much larger than that of almost every other substance,
thanks in part to those hydrogen bonds, thus preventing the oceans from boiling
away during the early days of life on earth. Water is also a fiendishly talented
dissolver of things. (Even the famously inert gold is soluble in seawater if you
give it enough time.) The combination of water’s fluidity and solubility makes it
marvelously adept at creating new networks of elements, as they churn through
the ever-shifting medium, colliding with each other in unpredictable ways. At
the same time, the strength of the hydrogen bonds means that new combinations
with some stability to them—many of them anchored around carbon atoms—can
endure and seek out additional connections in the soup.
And so, when we look back to the original innovation engine on earth, we find
two essential properties. First, a capacity to make new connections with as many
other elements as possible. And, second, a “randomizing” environment that
encourages collisions between all the elements in the system. On earth, at least,
the story of life’s creativity begins with a liquid, high-density network:
connection-hungry carbon atoms colliding with other elements in the primordial
soup. The molecules they formed mark the point at which chemistry and physics
gave way to biology. When the first lipids self-assembled, they unlocked a door
that would ultimately lead to the cell membrane; when the first nucleotides
formed, a wing of the adjacent possible opened that eventually traced a path to
DNA. They were the first hints of life’s good idea.
The computer scientist Christopher Langton observed several decades ago that
innovative systems have a tendency to gravitate toward the “edge of chaos”: the
fertile zone between too much order and too much anarchy. (The notion is
central to Stuart Kauffman’s idea of the adjacent possible, as well.) Langton
sometimes uses the metaphor of different phases of matter—gas, liquid, solid—
to describe these network states. Think of the behavior of molecules in each of
these three conditions. In a gas, chaos rules; new configurations are possible, but
they are constantly being disrupted and torn apart by the volatile nature of the
environment. In a solid, the opposite happens: the patterns have stability, but
they are incapable of change. But a liquid network creates a more promising
environment for the system to explore the adjacent possible. New configurations
can emerge through random connections formed between molecules, but the
system isn’t so wildly unstable that it instantly destroys its new creations. Those
connective carbon atoms swirling in the primordial soup formed a high-density
liquid network. The 100 billion neurons in your brain form another kind of liquid
network: densely interconnected, constantly exploring new patterns, but also
capable of preserving useful structures for long periods of time.
There is a prediction (albeit retroactive) lurking in this idea of the liquid
network, as well as in the premise that innovative environments share signature
patterns at different scales. The prediction is that whenever human beings first
organized themselves into settlements that resembled liquid networks, a great
flowering of innovation would have immediately followed. For ages, early
humans lived in the cultural equivalent of gaseous networks: small packs of
hunter-gatherers bouncing around the landscape, with almost no contact between
groups. But the rise of agriculture changed all that. For the first time, humans
began forming groups that numbered in the thousands, or tens of thousands.
After millennia of living in an intimate cluster of extended family, they began
sharing a space crowded with strangers. With that increase in population came a
crucial increase in the number of possible connections that could be formed
within the group. Good ideas could more readily find their way into other brains
and take root there. New forms of collaboration became possible. Economists
have a telling phrase for the kind of sharing that happens in these densely
populated environments: “information spillover.” When you share a common
civic culture with thousands of other people, good ideas have a tendency to flow
from mind to mind, even when their creators try to keep them secret. “Spillover”
is the right word; it captures the essential liquidity of information in dense
settlements. As species go, Homo sapiens had been on a fairly good run in the
million years that led up to the birth of agriculture: its members had invented
spoken language, art, sophisticated tools for hunting, and cooking. But until they
settled in cities, they had not figured out a way to live inside a high-density
liquid network.
What happened when they did? To grasp the magnitude of the change, we
need to put it in some kind of perspective, by measuring the speed of innovation
before the first cities were settled. So let us condense seventy thousand years of
innovation into a single time line, ending circa 2000 B.C., a few millennia after
the first true cities formed.
Looking at the past from this perspective makes one thing clear: somewhere
within a thousand years of the first cities emerging, human beings invented a
whole new way of inventing. A strong correlation exists between those dense
settlements and the dramatic surge in the societal innovation rate. But is there a
causal relationship between the two? The chart alone cannot tell us, and we do
not know enough about the specific histories of these innovations to document
how essential the urban context was to their creation. But the circumstantial
evidence is strong.
No doubt some ingenious hunter-gatherer stumbled across the cleansing
properties of ashes mixed with animal fat, or dreamed of building aqueducts in
those long eons before the rise of cities, and we simply have no record of his
epiphany. But the lack of a record is exactly the point. In a low-density, chaotic
network, ideas come and go. In the dense networks of the first cities, good ideas
have a natural propensity to get into circulation. They spill over, and through
that spilling they are preserved for future generations. For reasons we will see,
high-density liquid networks make it easier for innovation to happen, but they
also serve the essential function of storing those innovations. Before writing,
before books, before Wikipedia, the liquid network of cities preserved the
accumulated wisdom of human culture.
The pattern was repeated in the explosion of commercial and artistic
innovation that emerged in the densely settled hill towns of Northern Italy, the
birthplace of the European Renaissance. Once again, the rise of urban networks
triggers a dramatic increase in the flow of good ideas. It is not a coincidence that
Northern Italy was the most urbanized region in all of Europe during the
fourteenth and fifteenth centuries. But, in a crucial sense, the pattern of
Renaissance innovation differs from that of the first cities: Michelangelo,
Brunelleschi, and da Vinci were emerging from a medieval culture that suffered
from too much order. If dispersed tribes of hunter-gatherers are the cultural
equivalent of a chaotic, gaseous state, a culture where the information is largely
passed down by monastic scribes stands at the opposite extreme. A cloister is a
solid. By breaking up those information bonds and allowing ideas to circulate
more freely through a wider, connected population, the great Italian innovators
brought new life to the European mind.
Historians have long noted the connection between the artistic and scientific
flowering of the Renaissance and the formation of early merchant capitalism in
the region, which of course involved its own set of innovations in banking,
accounting, and insurance. To be sure, capitalism accelerated the growth of the
Italian cities, and created surplus wealth that was then deployed to support artists
and architects like Michelangelo and Brunelleschi. But the connection between
capitalism and innovation is more subtle than we often make it out to be. Yes,
free markets introduce new forms of competition and capital accumulation that
can drive the creation and adoption of new ideas. But markets should not be
exclusively defined in terms of the profit motive. Consider the invention of one
of capitalism’s key conceptual tools: double-entry accounting, which Goethe
called one of the “finest inventions of the human mind.” Now the cornerstone of
all financial bookkeeping, double-entry’s innovation of recording every financial
event in two ledgers (one reflecting a debit, the other a credit) allowed merchants
to track the financial health of their businesses with unparalleled accuracy. First
codified by the Franciscan friar and mathematician Luca Pacioli in 1494, the
double-entry method had been used for at least two centuries by Italian bankers
and merchants. We do not know if the method originated in the mind of a single
visionary proto-accountant, or whether the idea emerged simultaneously in the
minds of multiple entrepreneurs, or whether it was passed on by Islamic
entrepreneurs who may have experimented with the technique centuries before.
Whatever its roots, the technique first became commonplace in the trade capitals
of Italy—Genoa, Venice, and Florence—as the merchants of the early
Renaissance shared tips among themselves on how best to manage their
finances. What makes the history of double-entry so fascinating is the simple
fact that no one seems to have claimed ownership of the technique, despite its
immense value to a capitalist enterprise. One of the essential instruments in the
creation of modern capitalism appears to have been developed collectively,
circulating through the liquid networks of Italy’s cities. Double-entry accounting
made it far easier to keep track of what you owned, but no one owned doubleentry accounting itself. The idea was too powerful not to spill over into other
nearby minds.
Double-entry accounting illustrates a key principle in the emergence of
markets: when economic systems shift from feudal structures to the nascent
forms of modern capitalism, they become less hierarchical and more networked.
A society organized around marketplaces, instead of castles or cloisters,
distributes decision-making authority across a much larger network of individual
minds. The innovation power of the marketplace derives, in part, from this most
elemental math: no matter how smart the “authorities” may be, if they are
outnumbered a thousand to one by the marketplace, there will be more good
ideas lurking in the market than in the feudal castle. Cities and markets recruit
more minds into the collective project of exploring the adjacent possible. As
long as there is spillover between those minds, useful innovations will be more
likely to appear and spread through the population at large.
In thinking about networked innovation this way, I am specifically not talking
about a “global brain,” or a “hive mind.” There are indeed some problems that
are wonderfully solved by collective thinking: the formation of neighborhoods in
cities, the variable signals of market pricing, the elaborate engineering feats of
the social insects. But as many critics have pointed out—most recently, the
computer scientist and musician Jaron Lanier—large collectives are rarely
capable of true creativity or innovation. (We have the term “herd mentality” for
a reason.) When the first market towns emerged in Italy, they didn’t magically
create some higher-level group consciousness. They simply widened the pool of
minds that could come up with and share good ideas. This is not the wisdom of
the crowd, but the wisdom of someone in the crowd. It’s not that the network
itself is smart; it’s that the individuals get smarter because they’re connected to
the network.
In 1964, Arthur Koestler published his epic account of innovation’s roots, The
Act of Creation. The book was an attempt to explain how breakthrough ideas in
science and art come about. (Koestler also had a long opening section on humor,
which he believed was closely related to the more erudite inspiration of the poets
and physicists.) Koestler’s survey extends from Archimedes to Einstein, from
Milton to Joyce, and his analysis is unfailingly interesting, often brilliant. Yet
across such a wide-ranging survey, one pattern recurs with a surprising
regularity. The act of creation, in Koestler’s account, is something that happens
exclusively in the mind. He spends almost no time discussing the many habitats
that sustain or encourage innovation. The book’s index, for instance, lacks a
single reference to that great engine of supercreativity, the city. Koestler was a
great believer in the creative power that emerges when different intellectual
disciplines collide. But he seems to have had little interest in the environments
that make those collisions possible: living environments, office environments,
media environments. On a basic level, it is true that ideas happen inside minds,
but those minds are invariably connected to external networks that shape the
flow of information and inspiration out of which great ideas are fashioned.
Koestler was hardly alone in his interest in the roots of scientific
breakthrough. Thomas Kuhn’s even more influential book The Structure of
Scientific Revolutions had been published two years before The Act of Creation.
Since those two books were published, countless dissertations and scholarly
essays have explored the psychology and sociology of scientific progress. Some
focused on biographical accounts of legendary scientists at work; others tested
theories through lab experiments that simulated the kind of cognitive work
involved in scientific discovery. Others conducted extensive interviews with
prominent researchers, asking them to recall the details of their eureka moments
and private paradigm shifts.
In the early 1990s, a psychologist at McGill University named Kevin Dunbar
decided to take another approach: instead of reading biographies or theorizing in
the lab or listening to scientists recount their greatest hits, he would actually
watch them as they worked. Dunbar’s research style was closer to the reality
show Big Brother than it was to traditional philosophy of science. He set up
cameras in four leading molecular biology laboratories and recorded as much of
the action as possible. He also conducted extensive interviews in which the
researchers described the latest developments in their experiments and their
shifting hypotheses, all in the present tense. The taping and in medias res
interviews allowed Dunbar to get around one of the major failings of traditional
studies that rely on retrospective interviews: people tend to condense the origin
stories of their best ideas into tidy narratives, forgetting the messy, convoluted
routes to inspiration that they actually followed. Dunbar called his approach in
vivo, as opposed to the more traditional in vitro approach to studying scientific
cognition. In other words, Dunbar wasn’t studying idea formation in an artificial
petri dish environment. He was studying it in the wild.
Dunbar and his team transcribed all the interactions and coded each exchange
using a classification scheme that allowed them to track patterns in the flow of
information through the lab. In group interactions, for instance, exchanges
between scientists could be formally coded as “clarification” or “agreement and
elaboration” or “questioning.” Most important, Dunbar tracked the conceptual
changes that occurred over the course of each project: a researcher baffled by
persistent problems in achieving a stable control result who suddenly realizes
that the control problem could be the basis for a whole new experiment; an
exchange between two scientists working on different projects who recognize a
surprising and important connection between their work.
The most striking discovery in Dunbar’s study turned out to be the physical
location where most of the important breakthroughs occurred. With a science
like molecular biology, we inevitably have an image in our heads of the scientist
alone in the lab, hunched over a microscope, and stumbling across a major new
finding. But Dunbar’s study showed that those isolated eureka moments were
rarities. Instead, most important ideas emerged during regular lab meetings,
where a dozen or so researchers would gather and informally present and discuss
their latest work. If you looked at the map of idea formation that Dunbar created,
the ground zero of innovation was not the microscope. It was the conference
table.
Dunbar uncovered a set of interactions that consistently led to important
breakthroughs during lab conversations. The group environment helped
recontextualize problems, as questions from colleagues forced researchers to
think about their experiments on a different scale or level. Group interactions
challenged researchers’ assumptions about their more surprising findings,
making them less likely to dismiss them as experimental error. In group
problem-solving sessions, Dunbar writes, “the results of one person’s reasoning
became the input to another person’s reasoning . . . resulting in significant
changes in all aspects of the way the research was conducted.” Productive
analogies between different specialized fields were more likely to emerge in the
conversational setting of the lab meeting.
Dunbar’s research suggests one vaguely reassuring thought: even with all the
advanced technology of a leading molecular biology lab, the most productive
tool for generating good ideas remains a circle of humans at a table, talking
shop. The lab meeting creates an environment where new combinations can
occur, where information can spill over from one project to another. When you
work alone in an office, peering into a microscope, your ideas can get trapped in
place, stuck in your own initial biases. The social flow of the group conversation
turns that private solid state into a liquid network.
Dunbar’s generative conference room meetings remind us that the physical
architecture of our work environments can have a transformative effect on the
quality of our ideas. The quickest way to freeze a liquid network is to stuff
people into private offices behind closed doors, which is one reason so many
Web-era companies have designed their work environments around common
spaces where casual mingling and interdepartmental chatter happens without any
formal planning. (In a New Yorker essay, Malcolm Gladwell wonderfully
described this trend as the West Village- ification of the corporate office.) The
idea, of course, is to strike the right balance between order and chaos. Inspired
by the early hype about telecommuting, the advertising agency
TBWA/Chiat/Day experimented with a “nonterritorial” office where desks and
cubicles were jettisoned, along with the private offices: employees had no fixed
location in the office and were encouraged to cluster in new, ad hoc
configurations with their colleagues depending on that day’s projects. By all
accounts, it was a colossal failure, precisely because it traded excessive order for
excessive chaos.
Slightly less ambitious open-office plans have grown increasingly
unfashionable in recent years, for one compelling reason: people don’t like to
work in them. To work in an open office is to work exclusively in public, which
turns out to have just as many drawbacks as working entirely in your private lab.
A better model might be MIT’s legendary Building 20, the temporary structure
built during World War II that somehow managed to last fifty-five years, in part
because it had an extraordinary track record for cultivating both breakthrough
ideas and organizations like Noam Chomsky’s linguistics department, Bose
Acoustics, and the Digital Equipment Corporation. As MIT wrote in a press
release commemorating the building’s remarkable history: “Not assigned to any
one school, department, or center, it seems to always have had space for the
beginning project, the graduate student’s experiment, the interdisciplinary
research center.”
The magic of Building 20, powerfully eulogized in Stewart Brand’s How
Buildings Learn, lay in the balance the environment struck between order and
chaos. There were walls and doors and offices, as in most academic buildings.
But the structure’s temporary origins—it was originally built with the
expectation that it would be torn down after five years—meant that those
structures could be reconfigured with little bureaucratic fuss, as new ideas
created new purposes for the space.
Because they are fixed physical structures, most offices have a natural
tendency to disrupt liquid networks of information. They themselves are, quite
literally, made out of solids, and they often map out the conceptual solid of a
formal org chart, with its neatly defined departments and hierarchies. Building
20 resisted those calcifying forces for a simple reason: it was built on the cheap,
which meant its residents had no qualms about tearing down a wall or punching
a hole in the ceiling to adapt the space to a new idea. But architects and interior
designers are learning how to build work environments that facilitate liquid
networks in more permanent structures.
In November of 2007, Microsoft opened the doors to the new Redmond,
Washington-based headquarters of its research division: Building 99. Created by
a Microsoft designer named Martha Clarkson after deep collaboration with the
tinkerers and multidisciplinarians of the research division, Building 99 was
created from the ground up to be reinvented by the unpredictable flow of
collaboration and inspiration. All the office spaces are modular, with walls that
can be easily reconfigured to match the needs of the employees. Larger
“situation rooms” house groups working on high-priority projects, with a mix of
private workstations, conference tables, and sofas. Most walls are write-on/wipeoff, so if inspiration hits on the way to the restroom, you can quickly sketch out
an idea for your colleagues to see. The traditional kitchenette with a coffeepot
and refrigerator is replaced by open “mixer stations” where employees gather to
share ideas or gossip. In a sense, Clarkson built the water-coolers first, and then
designed an office building around them.
Two decades ago, the psychologist Mihaly Csikszentmihalyi proposed the
concept of “flow” to describe the internal state of energized focus that
characterizes the mind at its most productive. It’s a lovely metaphor, precisely
because it suggests the essential fluidity that good ideas so often need. Flow is
not the singular intensity of focusing “like a laser,” as we often say. And it is not
the miraculous illumination of a sudden brainstorm. Rather, it is more the feeling
of drifting along a stream, being carried in a clear direction, but still tossed in
surprising ways by the eddies and whirls of moving water.
But standing in the atrium of Building 99, it’s impossible not to think that this
space was designed to conjure up a different kind of flow: the collective flow of
energized minds forming liquid networks in their mixing spaces and situation
rooms. Building 99—like Building 20 before it—is a space that sees information
spillover as a feature, not a flaw. It is designed to leak. In this, it shares some
core values with the liquid networks of dense cities. No, a closed office at one of
the world’s richest corporations will never have the open-ended collisions and
vitality that a city sidewalk has. But those are extreme points on a continuum.
What is important in a structure like Building 99 is what it has learned about
flow from those urban environments, and from temporary structures like
Building 20. A corporate office building will never re-create fourteenth-century
Genoa, or even twentieth-century Greenwich Village. But office design is
moving in that direction, away from the crystal palaces of Organization Man,
with their corner offices and anonymous cubicles. And with that increased
fluidity—all those new ideas jostling against each other, in rooms expanding and
contracting to meet their needs—it’s not hard to imagine the space generating a
reliable flow of innovation in the years to come. Exploring the adjacent possible
can be as simple as opening a door. But sometimes you need to move a wall.
III.
THE SLOW HUNCH
On July 10, 2001, an Arizona-based FBI field agent named Ken Williams filed
an “electronic communication” with his superiors in Washington and New York,
using the Bureau’s Automated Case Support system, the antiquated electronic
repository through which the Bureau shared information about ongoing
investigations. The six-page document began with this prophetic sentence: “The
purpose of this communication is to advise the Bureau and New York of the
possibility of a coordinated effort by USAMA BIN LADEN (UBL) to send
students to the United States to attend civil aviation universities and colleges.”
This was the now legendary “Phoenix memo,” a warning shot fired—and
largely ignored—during the lazy summer months leading up to 9/11. (Ironically,
the very day that Williams filed his memo, the New York Times ran an op-ed
titled “The Declining Terrorist Threat.”) Williams had been inspired to write the
memo by a pattern he had detected over the preceding year: an “inordinate
number” of people of “investigative interest” who had registered for various
flight schools and other civil aviation colleges in Arizona. Williams had
conducted interviews with several of these subjects, including one Zakaria
Mustapha Soubra, an aeronautical engineering student on an F-1 visa from the
UK. Soubra had pictures of bin Laden in his home and told Williams that he
believed the U.S. forces and embassies attacked in the Gulf and in Africa had
been “legitimate military targets of Islam.” Williams also suggested that nine
other students from Algeria, Kenya, India, Saudi Arabia, and other Middle
Eastern states had enrolled in flight schools and possessed extensive ties to
radical Islamic movements. Two of them apparently were acquaintances of Hani
Hanjour, who would be at the controls of American Airlines Flight 77 when it
crashed into the Pentagon on the morning of September 11.
Williams failed to anticipate the immediacy of the threat; the memo suggests a
long-term plan that would “establish a cadre of individuals who will one day be
working in the civil aviation community around the world. These individuals
will be in a position in the future to conduct terror activity against civil aviation
targets.” Williams thought that al Qaeda might be plotting a measured
infiltration of the airline industry; he failed to imagine the brute-force hijacking
that was to unfold just two months later. But his recommendations were right on
target. Williams argued that the FBI should assemble a comprehensive list of all
flight schools and other aviation institutions around the country, and flag anyone
attempting to obtain a visa to attend one of these schools.
Though it had been addressed to several high-level offices, including that of
David Frasca, the head of the Radical Fundamentalist Unit in D.C., Williams’s
memo quickly entered what investigators later dubbed a “black hole” at FBI
headquarters. For nearly three weeks, it remained in limbo, before it was finally
assigned to an analyst to review. The analyst labeled it “routine” instead of
“urgent.” Another agent in New York called it “speculative and not very
significant.” Though it was standard for analysts to then pass on reports of this
ilk to their superiors, the memo never reached RFU chief Frasca.
When word of the memo first leaked during 2002, intelligence and law
enforcement officials were quick to dismiss Williams’s warning, calling it
nothing more than a hunch. “He made a recommendation that we initiate a
program to look at flight schools that was received at Headquarters,” FBI chief
Robert Mueller testified. “It was not acted on by September 11. I should say in
passing that even if we had followed those suggestions at that time, it would not,
given what we know since September 11, have enabled us to prevent the attacks
of September 11.”
Both statements about the Phoenix memo are demonstrably true. Williams had
a hunch about terrorist groups and flight schools, and that hunch on its own
would not have been enough to prevent the attacks of September 11. But
dismissing it on those grounds fundamentally misses the point. Williams
stumbled across a provocative and surprising idea that was nonetheless
incomplete. But if that hunch had connected with another equally provocative
idea, one that emerged three weeks later and five hundred miles away, the
Phoenix memo might well have transformed the history of the early twenty-first
century.
You can learn a great deal about the history of innovation by examining great
ideas that changed the world. Indeed, most intellectual histories are structured in
exactly this fashion, a narrative of breakthroughs and insights and eureka
moments that had a transformative impact on human society. But because those
ideas were by definition successful ones, it’s tempting to attribute their success
to intrinsic causes: the sheer brilliance of the idea itself, or the sheer brilliance of
the mind that came up with it. But those intrinsic causes can easily overshadow
the environmental role in the creation and spread of those ideas. This is why it is
just as useful to look at the sparks that failed, the ideas that found their way to a
promising region of the adjacent possible but somehow collapsed there. The
Phoenix memo was precisely one of those failed sparks. It contained great
wisdom and foresight—in July of 2001, Ken Williams was probably closer to
the 9/11 plot than any human being on the planet, save the perpetrators
themselves—but that information proved to be ultimately useless. Why?
The simple answer is that no one implemented Williams’s recommendations,
in part because the memo itself had failed to persuade the mid-tier analysts of its
importance, and in part because a communications failure inside the FBI kept the
memo from reaching the top brass at Counterterrorism and the RFU. But even if
the memo had reached David Frasca in mid-July, and somehow persuaded him
that Ken Williams was on to something, it almost certainly would have failed to
stop the 9/11 plot, because it would have taken months to cross-reference all the
visa applications with the enrollment records for flight schools across the
country. Detecting such subtle patterns in real time was the unrealized goal of
the much-criticized Total Information Awareness project spearheaded by
Admiral John Poindexter in the years immediately after 9/11. But in 2001, FBI
agents could barely send e-mail to each other, much less cross-reference visa
applications with flight school attendance records. This is the technicality that
allowed Robert Mueller to testify that following the recommendations of the
Phoenix memo would have done nothing to stop the 9/11 attacks. Looking for
unusual visa applications in flight school attendees might well have led the
Bureau to the hijackers, but there was no information architecture in place that
could have successfully executed that kind of query in a matter of weeks. And
so, by that standard, Ken Williams’s hunch was not enough on its own to prevent
9/11.
But the Phoenix memo might well have been instrumental in stopping the
attacks had it followed a pattern that recurs throughout the history of worldchanging ideas. It was a hunch that needed to collide with another hunch.
Exactly one month after Ken Williams filed his memo, Zacarias Moussaoui
enrolled at Pan Am International Flight Academy on the outskirts of St. Paul,
Minnesota, where he began training to fly a Boeing 747-400 on a simulator.
Instructors and other employees at the flight school were immediately suspicious
about their new pupil, who paid his entire $8,300 fee in cash. Moussaoui
possessed an inordinate amount of interest in the operation of the cockpit doors
and flight tour communications, despite the fact that he claimed no interest in
ever flying a real plane. The Pan Am employees contacted the FBI, and after a
quick background check, Moussaoui was arrested on immigration violations at a
motel on August 16. An interrogation convinced the field agents, led by Harry
Samit and Greg Jones, that Moussaoui posed an active threat and might be part
of a wider conspiracy. The Minnesota office of the FBI then began a frantic, and
ultimately unsuccessful, attempt to obtain a search warrant to examine the files
on Moussaoui’s laptop. On August 21, the request to seek a search warrant was
formally denied on the grounds that the evidence for probable cause was
“shaky,” just another hunch from the hinterlands. For the next week, the
Minneapolis office implored headquarters to get access to Moussaoui’s laptop,
to no avail. Agent Jones at one point warned that Moussaoui might “try to fly
something into the World Trade Center.” The search warrant would not be
granted until the afternoon of September 11, after Jones’s vision turned out to be
all too prescient.
This is a story of two hunches: Ken Williams’s hunch that a plot involving
multiple radical Islamic fundamentalists could be intercepted by tracking visa
applications and flight school enrollment records; and the Minneapolis field
agents’ hunch that Moussaoui wanted to fly a plane into the World Trade Center.
(The latter began, of course, with another hunch: the Pan Am school instructors’
hunch that Zacarias Moussaoui was not being honest about his interest in using a
747 simulator.) On their own, they were indeed hunches; on their own, the
evidence for their validity was indeed shaky. But contemplating them together
amplified their persuasive power dramatically. Connecting the dots between
them would have certainly supplied enough probable cause to justify examining
the contents of Zacarias Moussaoui’s laptop. And had the agents examined his
belongings, they would have uncovered direct connections to eleven of the 9/11
hijackers, along with Western Union wire-transfer numbers tracking recent
payments from Ramzi bin al-Shibh, one of the central coordinators of the 9/11
attack. We cannot know for certain whether that information alone would have
led the authorities to Mohamed Atta in time, or whether a more aggressive
interrogation of Moussaoui himself might have elicited a confession that would
have unraveled the plot. Certainly it is within the realm of possibility. What is
undeniable is that in late August of 2001, the only real hope for stopping the
attacks lay in connecting these two hunches.
The failed spark of the Phoenix memo suggests an answer to the mystery of
superlinear scaling in cities and on the Web. A metropolis shares one key
characteristic with the Web: both environments are dense, liquid networks where
information easily flows along multiple unpredictable paths. Those
interconnections nurture great ideas, because most great ideas come into the
world half-baked, more hunch than revelation. Genuine insights are hard to
come by; it’s challenging to imagine a terrorist plot to fly passenger planes into
buildings, or to invent a programmable computer. And so, most great ideas first
take shape in a partial, incomplete form. They have the seeds of something
profound, but they lack a key element that can turn the hunch into something
truly powerful. And more often than not, that missing element is somewhere
else, living as another hunch in another person’s head. Liquid networks create an
environment where those partial ideas can connect; they provide a kind of dating
service for promising hunches. They make it easier to disseminate good ideas, of
course, but they also do something more sublime: they help complete ideas.
The real problem with Ken Williams’s hunch was not that it failed to envision
the exact details or the imminence of the 9/11 plot, or even that his
recommendations would have failed to prevent the plot had they been followed.
The problem with Ken Williams’s hunch was environmental: instead of
circulating through a dense network, the Phoenix memo was dropped into the
black hole of the Automated Case Support system. Instead of seeking out new
connections, the Phoenix memo was deposited in the equivalent of a locked file
cabinet. Hunches that don’t connect are doomed to stay hunches.
There is a fundamental difference between the Phoenix and Minnesota hunches,
though, and that difference is time. The flight instructors had a bad feeling about
Moussaoui in a matter of hours; something just seemed unsettling about his
manner and the questions he asked. Ken Williams, on the other hand, developed
his hunch about the flight school threat over years of investigation. The Phoenix
memo was not the result of a gut impression; it was an idea that slowly took
shape over time, a pattern detected after countless hours of observation and
inquiry.
The Minnesota hunch has become intellectually fashionable in recent years:
the gut instinct, the “emotional brain” flash assessment of a situation that defies
the slower calculations of logic—but which nonetheless turns out to be
uncannily accurate. The interest in this kind of hunch dates back to the 1980s
and António Damásio’s experiments with brain-damaged patients whose
inability to make intuitive snap judgments produced startlingly irrational
behavior. Malcolm Gladwell’s bestseller Blink focused almost exclusively on the
power (and the occasional danger) of the instant hunch: the art historian who
knows in a second that the ancient sculpture is a fraud; the NYPD officer who
makes a disastrous snap judgment that a suspect is reaching for a gun when he is
actually reaching for his wallet.
But the snap judgments of intuition—as powerful as they can be—are rarities
in the history of world-changing ideas. Most hunches that turn into important
innovations unfold over much longer time frames. They start with a vague, hardto-describe sense that there’s an interesting solution to a problem that hasn’t yet
been proposed, and they linger in the shadows of the mind, sometimes for
decades, assembling new connections and gaining strength. And then one day
they are transformed into something more substantial: sometimes jolted out by
some newly discovered trove of information, or by another hunch lingering in
another mind, or by an internal association that finally completes the thought.
Because these slow hunches need so much time to develop, they are fragile
creatures, easily lost to the more pressing needs of day-to-day issues. But that
long incubation period is also their strength, because true insights require you to
think something that no one has thought before in quite the same way. Flash
judgments are often just that—judgments. Is this guy trustworthy or not? Is the
sculpture a fake? A new idea is something larger than that: it’s a new perspective
on a problem, or a recognition of a new opportunity that has gone unexplored to
date. Those kinds of breakthroughs usually take time to develop. When the
eighteenth-century scientist Joseph Priestley decided to isolate a mint sprig in a
sealed glass in an ingenious experiment that ultimately proved that plants were
creating oxygen—one of the founding discoveries of modern ecosystem science
—he was building on a hunch that he’d been cultivating for twenty years, dating
back to his boyhood obsession with trapping spiders in glass jars. He’d had a
hunch that there was something interesting in the way that organisms perished
when you sealed them in closed vessels, something that pointed to a larger truth.
And he kept that hunch alive until he was ready to make sense of it. This was not
a matter of doggedly pursuing a single line of inquiry. During those twenty
years, Priestley dabbled in a dozen different fields, concocted hundreds of novel
experiments in his home lab, engaged in extensive conversations with the
leading intellectuals of the day. A minuscule percentage of that time was
devoted directly to the problem of plant respiration. He just kept it alive in the
back of his mind. Sustaining the slow hunch is less a matter of perspiration than
of cultivation. You give the hunch enough nourishment to keep it growing, and
plant it in fertile soil, where its roots can make new connections. And then you
give it time to bloom.
The Vaseline-daubed lens of hindsight tends to blur slow hunches into eureka
moments. Inventors, scientists, entrepreneurs, artists—they all like to tell the
stories of their great breakthroughs as epiphanies, in part because there is a kind
of narrative thrill that comes from that lightbulb moment of sudden clarity, and
in part because the leisurely background evolution of the slow hunch is much
harder to convey. But if one examines the intellectual fossil record closely, the
slow hunch is the rule, not the exception.
In a famous passage from his Autobiography, Darwin describes his great
moment of insight as a young man struggling to understand the evolution of life:
In October 1838, that is, fifteen months after I had begun my systematic
enquiry, I happened to read for amusement Malthus on Population, and
being well prepared to appreciate the struggle for existence which
everywhere goes on from long-continued observation of the habits of
animals and plants, it at once struck me that under these circumstances
favourable variations would tend to be preserved, and unfavourable ones
to be destroyed. The result of this would be the formation of new species.
Here, then, I had at last got a theory by which to work.
This is evolution’s version of Newton’s apple: Malthus falls out of a tree and
hits Darwin on the head, and voilà—natural selection is born. In part, the appeal
of this eureka story stems from the simple elegance of the theory itself. Unlike
more technically intricate scientific breakthroughs, it seems somehow
appropriate that the basic evolutionary algorithm should just pop into the mind in
a moment of recognition. (Darwin’s great supporter, T. H. Huxley, is said to
have exclaimed, on hearing the natural selection argument for the first time,
“How incredibly stupid not to think of that.”) Darwin’s account also possesses a
strangely poetic symmetry, because years later, when Alfred Russel Wallace
independently hit upon the theory of natural selection, he claimed his
breakthrough had been inspired by Malthus as well.
For almost a century, the Malthusian epiphany was the canonical story of
Darwinism’s roots. But in the early 1970s, a psychologist and intellectual
historian named Howard Gruber decided to revisit Darwin’s copious notebooks
from the period, reconstructing the elaborate dance of speculation, factmarshaling, and internal debate that led to Darwin’s breakthrough in the fall of
1838. What Gruber found in the notebooks was a story very different from the
account relayed in Darwin’s Autobiography. All the core elements of Darwin’s
theory are present in the notebooks well before the Malthusian epiphany, which
the notebooks explicitly date at September 28, 1838. Darwin understands the
importance of variation; the connection between natural and artificial selection;
the competition among different species for survival; the clear physiological
connections among species; the epic scale of evolutionary time. All these key
concepts are discussed at great length in the notebooks from 1837 on. It is not
merely that Darwin possesses the puzzle pieces but fails to put them together in
the right configuration. In a number of remarkable passages, written many
months before the Malthusian insight, he appears to be describing the theory of
natural selection in almost full dress. Exactly a year before his Malthus reading,
he asks, in shorthand English: “Whether every animal produces in course of ages
ten thousand varieties (influenced itself perhaps by circumstances) and those
alone preserved which are well adapted?” All it takes to cement a working
theory of natural selection is to modify the formula ever so slightly, and clarify
that the preservation of “well adapted” forms comes from their reproductive
success. And yet somehow Darwin fails to understand that he has the solution at
his fingertips, and continues his enquiry for another year before “getting a theory
by which to work.”
Even after the Malthusian insight, Darwin seems incapable of grasping the full
consequences of the theory he has established. The journal entries on September
28 are suitably excited, and do seem to grapple with the fundamental elements of
the theory:
Population is increase at geometrical ratio in FAR SHORTER time than
25 . . . The final cause of all this wedging, must be to sort out proper
structure, and adapt it to change.—to do that for form, which Malthus
shows is the final effect (by means however of volition) of this
populousness on the energy of man. One may say there is a force like a
hundred thousand wedges trying [to] force every kind of adapted
structure into the gaps in the economy of nature, or rather forming gaps
by thrusting out weaker ones.
But in the days and weeks that follow, Darwin’s notes do not suggest a mind
that has crossed an intellectual watershed. As Gruber notes, the very next day
Darwin writes a long entry on the sexual curiosity of primates that appears to
have nothing to do with his new discovery. More than a month passes before he
even attempts to write down the governing rules of natural selection.
All of which means we cannot say definitively that Darwin hit upon the idea
for his theory of natural selection on September 28, 1838. The best we can do is
say that he did not possess the idea when he embarked on his enquiry in the
summer of 1837, and that he had it in an enduring form by November of 1838.
This is not a matter of gaps in the historical record. It is simply hard to pinpoint
exactly when Darwin had the idea, because the idea didn’t arrive in a flash; it
drifted into his consciousness over time, in waves. In the months before the
Malthus reading, we could probably say that Darwin had the idea of natural
selection in his head, but at the same time was incapable of fully thinking it. This
is how slow hunches often mature: by stealth, in small steps. They fade into
view.
This pattern recurs with the other iconographic story of Darwin’s intellectual
journey: his formative months observing the strange diversity of the Galápagos
Islands during the voyage of the Beagle. To be sure, Darwin’s early exploration
of the principles of natural selection relied heavily on the striking deviations he
had seen between related species on the Galápagos archipelago. Darwin’s
finches are famous for a reason. But the notebooks written during the Galápagos
expedition in October of 1835 have almost no hint of the world-changing theory
that they will eventually inspire. In fact, the overwhelming majority of notes
from Darwin’s sojourn on the Galápagos are geological in nature, far more
concerned with Lyell’s uniformitarian theory than with the birds and reptiles of
the archipelago. (One inventory of Darwin’s notebooks found 1,383 pages of
geological notes, versus 368 pages on zoology.) He does take extensive notes in
his “naturalist” mode, but all the speculative energy of the Beagle journals is
generated by the geology. For Darwin the biologist, the Galápagos days were a
fact-finding mission, but Darwin the geologist was consciously processing and
interpreting the facts as he gathered them.
According to Darwin’s own account, he didn’t really lock on to the tantalizing
puzzle of the finches and their exotic neighbors until the next spring, just as the
Beagle was finding safe harbor in the Keeling Islands. His journal for 1837
includes the line: “In July opened first notebook on ‘Transmutation of
Species’—Had been greatly struck from about Month of previous March on
character of S. American fossils—& species on Galápagos archipelago. These
facts origin (especially latter) of all my views.” He had witnessed firsthand the
marvelous diversity of species on the Galápagos, and had documented it with a
precision that no human had ever attempted before. But it took him five months
to realize why it was important.
Keeping a slow hunch alive poses challenges on multiple scales. For starters,
you have to preserve the hunch in your own memory, in the dense network of
your neurons. Most slow hunches never last long enough to turn into something
useful, because they pass in and out of our memory too quickly, precisely
because they possess a certain murkiness. You get a feeling that there’s an
interesting avenue to explore, a problem that might someday lead you to a
solution, but then you get distracted by more pressing matters and the hunch
disappears. So part of the secret of hunch cultivation is simple: write everything
down.
We can track the evolution of Darwin’s ideas with such precision because he
adhered to a rigorous practice of maintaining notebooks where he quoted other
sources, improvised new ideas, interrogated and dismissed false leads, drew
diagrams, and generally let his mind roam on the page. We can see Darwin’s
ideas evolve because on some basic level the notebook platform creates a
cultivating space for his hunches; it is not that the notebook is a mere
transcription of the ideas, which are happening offstage somewhere in Darwin’s
mind. Darwin was constantly rereading his notes, discovering new implications.
His ideas emerge as a kind of duet between the present-tense thinking brain and
all those past observations recorded on paper. Somewhere in the middle of the
Indian Ocean, a train of association compels him to revisit his notes on the fauna
of the Galápagos archipelago from five months before. As he reads through his
observations, a new thought begins to take shape in his mind, which provokes a
whole new set of notes that will only make complete sense to Darwin two years
later, after the Malthus episode.
Darwin’s notebooks lie at the tail end of a long and fruitful tradition that
peaked in Enlightenment-era Europe, particularly in England: the practice of
maintaining a “commonplace” book. Scholars, amateur scientists, aspiring men
of letters—just about anyone with intellectual ambition in the seventeenth and
eighteenth centuries was likely to keep a commonplace book. The great minds of
the period—Milton, Bacon, Locke—were zealous believers in the memoryenhancing powers of the commonplace book. In its most customary form,
“commonplacing,” as it was called, involved transcribing interesting or
inspirational passages from one’s reading, assembling a personalized
encyclopedia of quotations. There is a distinct self-help quality to the early
descriptions of commonplacing’s virtues: maintaining the books enabled one to
“lay up a fund of knowledge, from which we may at all times select what is
useful in the several pursuits of life.”
John Locke first began maintaining a commonplace book in 1652, during his
first year at Oxford. Over the next decade he developed and refined an elaborate
system for indexing the book’s content. Locke thought his method important
enough that he appended it to a printing of his canonical work, An Essay
Concerning Human Understanding. Locke’s approach seems almost comical in
its intricacy, but it was a response to a specific set of design constraints: creating
a functional index in only two pages that could be expanded as the commonplace
book accumulated more quotes and observations:
When I meet with any thing, that I think fit to put into my commonplace-book, I first find a proper head. Suppose for example that the head
be EPISTOLA, I look unto the index for the first letter and the following
vowel which in this instance are E. i. if in the space marked E. i. there is
any number that directs me to the page designed for words that begin
with an E and whose first vowel after the initial letter is I, I must then
write under the word Epistola in that page what I have to remark.
Locke’s method proved so popular that a century later, an enterprising
publisher named John Bell printed a notebook entitled “Bell’s Common-Place
Book, Formed generally upon the Principles Recommended and Practised by Mr
Locke.” The book included eight pages of instructions on Locke’s indexing
method, a system which not only made it easier to find passages, but also served
the higher purpose of “facilitat[ing] reflexive thought.” Bell’s volume would be
the basis for one of the most famous commonplace books of the late eighteenth
century, maintained from 1776 to 1787 by Erasmus Darwin, Charles’s
grandfather. At the very end of his life, while working on a biography of his
grandfather, Charles obtained what he called “the great book” from his cousin
Reginald. In the biography, the younger Darwin captures the book’s marvelous
diversity: “There are schemes and sketches for an improved lamp, like our
present moderators; candlesticks with telescope stands so as to be raised at
pleasure to any required height; a manifold writer; a knitting loom for stockings;
a weighing machine; a surveying machine; a flying bird, with an ingenious
escapement for the movement of the wings, and he suggests gunpowder or
compressed air as the motive power.”
The tradition of the commonplace book contains a central tension between
order and chaos, between the desire for methodical arrangement, and the desire
for surprising new links of association. For some Enlightenment-era advocates,
the systematic indexing of the commonplace book became an aspirational
metaphor for one’s own mental life. The dissenting preacher John Mason wrote
in 1745:
Think it not enough to furnish this Store-house of the Mind with good
Thoughts, but lay them up there in Order, digested or ranged under
proper Subjects or Classes. That whatever Subject you have Occasion to
think or talk upon you may have recourse immediately to a good
Thought, which you heretofore laid up there under that Subject. So that
the very Mention of the Subject may bring the Thought to hand; by
which means you will carry a regular Common Place-Book in your
Memory.
Others, including Priestley and both Darwins, used their commonplace books
as a repository for a vast miscellany of hunches. The historian Robert Darnton
describes this tangled mix of writing and reading:
Unlike modern readers, who follow the flow of a narrative from
beginning to end, early modern Englishmen read in fits and starts and
jumped from book to book. They broke texts into fragments and
assembled them into new patterns by transcribing them in different
sections of their notebooks. Then they reread the copies and rearranged
the patterns while adding more excerpts. Reading and writing were
therefore inseparable activities. They belonged to a continuous effort to
make sense of things, for the world was full of signs: you could read your
way through it; and by keeping an account of your readings, you made a
book of your own, one stamped with your personality.
Each rereading of the commonplace book becomes a new kind of revelation.
You see the evolutionary paths of all your past hunches: the ones that turned out
to be red herrings; the ones that turned out to be too obvious to write; even the
ones that turned into entire books. But each encounter holds the promise that
some long-forgotten hunch will connect in a new way with some emerging
obsession. The beauty of Locke’s scheme was that it provided just enough order
to find snippets when you were looking for them, but at the same time it allowed
the main body of the commonplace book to have its own unruly, unplanned
meanderings. Imposing too much order runs the risk of orphaning a promising
hunch in a larger project that has died, and it makes it difficult for those ideas to
mingle and breed when you revisit them. You need a system for capturing
hunches, but not necessarily categorizing them, because categories can build
barriers between disparate ideas, restrict them to their own conceptual islands.
This is one way in which the human history of innovation deviates from the
natural history. New ideas do not thrive on archipelagos.
In the bibliographic history of epic miscellany, another British title deserves
mention alongside Erasmus Darwin’s commonplace book: an immensely
popular Victorian how-to guide with the memorable title Enquire Within Upon
Everything. The frontispiece text for the book, first published in 1865, hints at
the immense collection of domestic resources it contained:
Whether you wish to model a flower in wax; to study the rules of
etiquette; to serve a relish for breakfast or supper; to supply a delicious
entrée for the dinner table; to plan a dinner for a large party or a small
one; to cure a headache; to make a will; to get married; to bury a
relative; whatever you may wish to do, to make, or to enjoy, provided
your desire has relation to the necessities of domestic life, I shall be
happy to assist you and therefore hope you will not fail to Enquire Within
—Editor.
Over a hundred editions of the guide were published, and it remained a
common staple of British households well into the twentieth century. One musty
copy of the book lingered into the 1960s, in the home of a pair of
mathematicians living in the suburbs of London. The couple had a young son
who was drawn to the “suggestion of magic” in the book’s title, and who spent
hours exploring this “portal to the world of information.” The title stuck in the
back of his mind, along with that wondrous feeling of exploring an immense
trove of data. More than a decade later, he was working as a software consultant
in a Swiss research lab, and found himself overwhelmed by the flow of
information and the personnel churn in the organization. As a side project, he
began tinkering with an application that would allow him to keep track of all that
data. When it came time to give his program a name, his mind drifted back to
that strange Victorian household encyclopedia from his youth. He called his
application Enquire.
The application allowed you to store small blocks of information about people
or projects as nodes in a connected network. It was easy to attach two-way
pointers between nodes, so if you pulled up a person’s name, you could instantly
see all the projects he or she was working on. The application proved to be
genuinely informative, but the programmer soon switched jobs and abandoned
the code. He started up another version, which he called Tangle, a few years
later, but it never got off the ground. But then, almost ten years after he had first
programmed Enquire, he began sketching out a more ambitious application that
could make connections between documents stored on different computers,
using hypertext links. For a while he struggled with the right name for his
nascent platform, calling it an information “mine” or “mesh.” Eventually, he hit
upon a different metaphor for the platform’s dense network. He called it the
World Wide Web.
In his own account of the Web’s origins, Tim Berners-Lee makes no attempt
to collapse the evolution of his marvelous idea into a single epiphany. The Web
came into being as an archetypal slow hunch: from a child’s exploration of a
hundred-year-old encyclopedia, to a freelancer’s idle side project designed to
help him keep track of his colleagues, to a deliberate attempt to build a new
information platform that could connect computers across the planet. Like
Darwin’s great understanding of life’s tangled web, Berners-Lee’s idea needed
time—at least a decade’s worth—to mature:
Journalists have always asked me what the crucial idea was, or what the
singular event was, that allowed the Web to exist one day when it hadn’t
the day before. They are frustrated when I tell them there was no
“Eureka!” moment . . . Inventing the World Wide Web involved my
growing realization that there was a power in arranging ideas in an
unconstrained, weblike way. And that awareness came to me through
precisely that kind of process. The Web arose as the answer to an open
challenge, through the swirling together of influences, ideas, and
realizations from many sides, until, by the wondrous offices of the
human mind, a new concept jelled. It was a process of accretion, not the
linear solving of one problem after another.
Berners-Lee’s slow, accretive development of the Web takes us to the next
scale of innovation. Cultivating hunches extends beyond the private dominion of
memory and the commonplace book. Most people do not have the luxury that
Darwin had, of spending an entire life in pursuit of his intellectual fancies. For
most people, ideas happen in and around their work environments, with all the
daily pressures, distractions, accountability, and constant supervision that work
life so often implies. In this respect, Berners-Lee was supremely lucky in the
work environment he had settled into, the Swiss particle physics lab CERN. It
took him ten years to nurture his slow hunch about a hypertext information
platform. He spent most of those years working at CERN, but it wasn’t until
1990—a decade after he had first begun working on Enquire—that CERN
officially authorized him to work on the hypertext project. His day job was “data
acquisition and control”; building a global communications platform was his
hobby. Because the two shared some attributes, his superiors at CERN allowed
Berners-Lee to tinker with his side project over the years. Thanks to a handful of
newsgroups on the Internet, Berners-Lee was able to supplement and refine his
ideas by conversing with other early hypertext innovators. That combination of
flexibility and connection gave Berners-Lee critical support for his idea. He
needed a work environment that carved out a space for slow hunches, cordoned
off from all the immediate dictates of the day’s agenda. And he needed
information networks that let those hunches travel to other minds, where they
could be augmented and polished.
If there is an innovation antimatter to CERN’s hunch-sustaining matter, it
might well be the Federal Bureau of Investigation in the summer of 2001. There
were two crucial networks that failed to make the proper connections in the
months leading up to 9/11: the information network of the Automated Case
Support system, and the neural networks in the brains of the key participants.
Even back in 2001, retrieving documents with an unlikely combination of terms
—say, for example, “flight schools” and “radical Islamic fundamentalists”—was
a routine matter; millions of users of Google, founded three years earlier, were
doing comparable queries of the entire Web, with near-instantaneous results.
Had the information network automatically suggested that the Radical
Fundamentalist Unit officials read the Phoenix memo after the Minnesota office
began its investigation into Moussaoui, the last few weeks of summer might
have played out very differently. But however smart the network itself could
have been, it still required a comparable connection to take place in the minds of
the participants. If David Frasca had read the memo that Ken Williams had
addressed to him, he might well have been able to connect the two hunches,
using the advanced pattern recognition technology of the human brain.
The failure of those two networks to connect the Phoenix and Minnesota
hunches was partly attributable to the practically medieval information
technology employed by the FBI. But even if the Bureau had miraculously
upgraded its network in the summer of 2001, the two hunches would likely have
remained apart, because the lack of connections in the Automated Case Support
system was a design principle, not merely the result of old-fashioned technology.
It was, in computer-science parlance, a feature, not a bug. The FBI’s information
network was a classic closed network: not only could outsiders not access
information in it, but also, the system was designed so that documents were
carefully shielded from other members of the organization, a legacy of an
institution predicated on secrets and “need to know” restrictions. The final report
of the Judiciary Committee investigation into the intelligence failings in the
months prior to 9/11 explicitly cited this design principle of the Bureau’s
information network as one of the key culprits, calling it “a ‘stove pipe’
mentality where crucial intelligence is pigeonholed into a particular unit and
may not be shared with other units.”
In a real sense, the FBI in the months leading up to 9/11 was a hunch-killing
system, which is more than a little ironic, given the important role that hunches
play in most accounts—real or fictional—of great investigators. In the FBI
culture, an analyst labeling a report “speculative” was enough to keep it from
advancing up the chain of command, while the outdated stovepipe architecture
kept Williams’s hunch from circulating to other field agents working on their
own hunches. Tim Berners-Lee’s monumental vision at CERN was a tangled
web of data, a “swirling together of influences, ideas, and realizations from
many sides.” The Automated Case Support system wasn’t just incompetent at
creative tangle; the system was explicitly designed to eliminate it.
In 1980, to allude to Enquire Within Upon Everything in the name of your
software package was more than a little audacious; Berners-Lee was just trying
to keep track of his colleagues at CERN, not organize all the world’s
information. But “enquire within . . .” could well be the slogan for Google,
which is why it is strangely appropriate that Google, in its own corporate
environment, has arguably done the most to adopt and expand the kind of slowhunch innovation that created the Web in the first place. Early in its history,
Google famously instituted a “20-percent time” program for all Google
engineers: for every four hours they spend working on official company projects,
the engineers are required to spend one hour on their own pet project, guided
entirely by their own passions and instincts. (Modeled on a similar program
pioneered by 3M known as “the 15-percent rule,” Google’s system is officially
called “Innovation Time Off.”) The only requirements are that they give
semiregular updates on their progress to their superiors. Most engineers end up
drifting from idea to idea, and the vast majority of those ideas never turn into an
official Google product. But every now and then, one of those hunches blooms
into something significant. AdSense, Google’s platform that allows bloggers and
Web publishers to run Google ads on their sites, was partially generated during
20-percent time. In 2009, AdSense was responsible for more than $5 billion of
Google’s earnings, nearly a third of their total for the year. Orkut, one of the
largest social network sites in India and Brazil, originated in the Innovation
Time Off of a Turkish Google engineer named Orkut Büyükkökten. Google’s
popular mail platform, Gmail, has roots in an Innovation Time Off project as
well. Marissa Mayer, Google’s vice president of Search Products and User
Experience, claims that over 50 percent of Google’s new products derive from
Innovation Time Off hunches.
The most telling contrast between Google and the FBI lies in the story of
Krishna Bharat, who now holds the title of “principal scientist” at Google. In the
weeks after 9/11, Bharat found himself overwhelmed by the amount of news
information available about the attacks and the imminent war in Afghanistan. It
occurred to him that it would be useful to create a software tool that could
organize all those stories into useful clusters of relevance, so that you could see
at a glance all the latest stories from around the Web about the search for bin
Laden, or the cleanup efforts at Ground Zero, or the Bush administration’s case
for military retaliation. Bharat decided to use his 20-percent time to build a
system called StoryRank—modeled after the original PageRank algorithm that
Google’s search engine relies on—to organize and cluster news items.
StoryRank eventually blossomed into Google News, one of the most popular
(and controversial) sources of news and commentary on the Web.
In a sense, the narrative of StoryRank’s evolution is the exact mirror image of
the narrative of the Phoenix memo. Like Tim Berners-Lee, Bharat was blessed
with an organizational culture that encouraged hunches and gave them the space
and time they needed to evolve. And Bharat took that nurturing environment and
used it to build a tool that could automatically assemble clusters of relevance and
association between documents—precisely the kind of system that could have
connected the dots between the Phoenix memo and the Moussaoui investigation.
Bharat had a hunch in his mind that there was a better way to organize the
information network of news, and what he built turned out to be a tool that could
be used to help related hunches complete one another.
Google News launched in September of 2002, which means StoryRank went
from a hunch in Krishna Bharat’s mind to a shipping product in one year. Nine
years after 9/11, the FBI is still using the Automated Case Support system.
IV.
SERENDIPITY
Like any other thought, a hunch is simply a network of cells firing inside your
brain in an organized pattern. But for that hunch to blossom into something more
substantial, it has to connect with other ideas. The hunch requires an
environment where surprising new connections can be forged: the neurons and
synapses of the brain itself, and the larger cultural environment that the brain
occupies.
For many years a debate raged over the nature of those neural connections:
Were they chemical or electrical in nature? Were there chemical soups in the
brain, or sparks? The answer turned out to be: both. Neurons send electrical
signals down the long cables of their axons, which connect to other neurons via
small synaptic gaps. When the electrical charge reaches the synapse, it releases a
chemical messenger—a neurotransmitter, like dopamine or serotonin—that
floats across to the receiving neuron and ultimately triggers another electrical
charge, which travels out to other neurons in the brain.
The hybrid electrochemical nature of nerve communication was first
established in another of the twentieth century’s most celebrated experiments. In
the early 1920s, the German scientist Otto Loewi isolated two still-beating frog
hearts in separate vessels containing a saline solution. In one heart, he attached
an electrode to the vagus nerve, which in an intact body starts in the brain stem
and extends throughout the body. Because the vagus nerve helps regulate the
parasympathetic system, stimulating the nerve with an electric charge slowed the
heartbeat down. Loewi then extracted some of the solution that surrounded the
heart and poured it over the second heart. Instantly, the second heart began to
beat more slowly as well, even though its vagus nerve had not been electrically
stimulated. Loewi’s ingenious experiment demonstrated that the instructions to
slow down the heartbeat had passed through the chemical soup of the saline
solution. By stimulating a different part of the frog’s vagus nerve, he could also
accelerate both heartbeats in the same fashion. We now know that the electrical
stimulation was releasing two distinct molecules into the soup: acetylcholine
(which slowed the heart down) and adrenaline (which stimulated it).
Loewi’s experiment, as influential as it was, is now remembered as much for
the curious way Loewi conceived of it. The idea for the experiment came to
Loewi in a dream—in two dreams, to be exact:
The night before Easter Sunday of that year I awoke, turned on the light,
and jotted down a few notes on a tiny slip of thin paper. Then I fell
asleep again. It occurred to me at six o’clock in the morning that during
the night I had written down something most important, but I was unable
to decipher the scrawl. The next night, at three o’clock, the idea returned.
It was the design of an experiment to determine whether or not the
hypothesis of chemical transmission that I had uttered seventeen years
ago was correct. I got up immediately, went to the laboratory, and
performed a simple experiment on a frog heart according to the nocturnal
design.
We conventionally associate dream inspiration with the creative arts, but the
canon of scientific breakthroughs contains many revolutionary ideas that
originated in dreams. The Russian scientist Dmitri Mendeleev created the
periodic table of the elements after a dream suggested to him that the table could
be ordered by atomic weight. It was in a dream in 1947 that Nobel laureate John
Carew Eccles originally conceived his theory of synaptic inhibitory action,
which helped explain how connected neurons can fire without triggering an
endless cascade of brain activity. Interestingly, Eccles’s initial hunch involved a
purely electrical system, but later experiments proved that the chemical GABA
was central to synaptic inhibition, putting him in agreement with Loewi’s
experiment of decades before.
There is nothing mystical about the role of dreams in scientific discovery.
While dream activity remains a fertile domain for research, we know that during
REM sleep acetylcholine-releasing cells in the brain stem fire indiscriminately,
sending surges of electricity billowing out across the brain. Memories and
associations are triggered in a chaotic, semirandom fashion, creating the
hallucinatory quality of dreams. Most of those new neuronal connections are
meaningless, but every now and then the dreaming brain stumbles across a
valuable link that has escaped waking consciousness. In this sense, Freud had it
backward with his notion of dreamwork: the dream is not somehow unveiling a
repressed truth. Instead, it is exploring, trying to find new truths by
experimenting with novel combinations of neurons.
A recent experiment led by the German neuroscientist Ullrich Wagner
demonstrates the potential for dream states to trigger new conceptual insights. In
Wagner’s experiment, test subjects were assigned a tedious mathematical task
that involved the repetitive transformation of eight digits into a different number.
With practice, the test subjects grew steadily more efficient at completing the
task. But Wagner’s puzzle had a hidden pattern to it, a rule that governed the
numerical transformations. Once discovered, the pattern allowed the subjects to
complete the test much faster, not unlike the surge of activity one gets at the end
of a jigsaw puzzle when all the pieces suddenly fall into place. Wagner found
that after an initial exposure to the numerical test, “sleeping on the problem”
more than doubled the test subjects’ ability to discover the hidden rule. The
mental recombinations of sleep helped them explore the full range of solutions to
the puzzle, detecting patterns that they had failed to perceive in their initial
training period. The work of dreams turns out to be a particularly chaotic, yet
productive, way of exploring the adjacent possible.
In a sense, dreams are the mind’s primordial soup: the medium that facilitates
the serendipitous collisions of creative insight. And hunches are like those early
carbon atoms, seeking out new kinds of connections to help them build new
chains and rings of innovation. Loewi’s dream about the frog heart experiment is
often invoked as a story of sudden epiphany—a twentieth-century version of
Newton’s apple—but the truth is that Loewi had been musing on the idea that
nerves might communicate chemically for seventeen years. In part, his epiphany
was made possible by the random connections of REM sleep. Yet it was also
made possible by a slow hunch that had been lingering in the back of his mind
for almost two decades.
This pattern of a slow hunch crystallizing into a dream-inspired epiphany
recurs in what may be the most famous reverie in the history of science. In 1865,
the German chemist Friedrich August Kekulé von Stradonitz had a daydream by
a crackling fire in which he saw a vision of Ouroboros, the serpent from Greek
mythology that devours its own tail. Kekulé had spent the past ten years of his
life exploring the connections of carbon-based molecules. The serpent image in
his dream gave him a sudden insight into the molecular structure of the
hydrocarbon benzene. The benzene molecule, he realized, was a perfect ring of
carbon, with hydrogen atoms surrounding its outer edges. Kekulé’s slow hunch
had set the stage for the insight, but for that hunch to turn into a world-changing
idea, he needed the most unlikely of connections: an iconic image from ancient
mythology. And Kekulé’s vision did indeed prove to be a breakthrough of epic
proportions: the ring structure of the benzene molecule became the basis for a
revolution in organic chemistry, opening up a new vista onto the mesmerizing
array of rings, lattices, and chains formed by that most connective of elements,
carbon. It took the combinatorial serendipity of a daydream—all those neurons
firing in unlikely new configurations—to help us understand the combinatorial
power of carbon, which was itself crucial to understanding the original
innovations of life itself.
The waking brain, too, has an appetite for the generative chaos that rules in the
dream state. Neurons share information by passing chemicals across the synaptic
gap that connects them, but they also communicate via a more indirect channel:
they synchronize their firing rates. For reasons that are not entirely understood,
large clusters of neurons will regularly fire at the exact same frequency.
(Imagine a discordant jazz band, each member following a different time
signature and tempo, that suddenly snaps into a waltz at precisely 120 beats per
minute.) This is what neuroscientists call phase-locking. There is a kind of
beautiful synchrony to phase-locking—millions of neurons pulsing in perfect
rhythm. But the brain also seems to require the opposite: regular periods of
electrical chaos, where neurons are completely out of sync with each other. If
you follow the various frequencies of brain-wave activity with an EEG, the
effect is not unlike turning the dial on an AM radio: periods of structured,
rhythmic patterns, interrupted by static and noise. The brain’s systems are
“tuned” for noise, but only in controlled bursts.
In 2007, Robert Thatcher, a brain scientist at the University of South Florida,
decided to study the vacillation between phase-lock and noise in the brains of
dozens of children. While Thatcher found that the noise periods lasted, on
average, for 55 milliseconds, he also detected statistically significant variation
among the children. Some brains had a tendency to remain longer in phase-lock,
others had noise intervals that regularly approached 60 milliseconds. When
Thatcher then compared the brain-wave results with the children’s IQ scores, he
found a direct correlation between the two data sets. Every extra millisecond
spent in the chaotic mode added as much as twenty IQ points. Longer spells in
phase-lock deducted IQ points, though not as dramatically.
Thatcher’s study suggests a counterintuitive notion: the more disorganized
your brain is, the smarter you are. It’s counterintuitive in part because we tend to
attribute the growing intelligence of the technology world with increasingly
precise electromechanical choreography. Intel doesn’t advertise its latest
microprocessors with the slogan: “Every 55 milliseconds, our chips erupt into a
blizzard of noise!” Yet somehow brains that seek out that noise seem to thrive, at
least by the measure of the IQ test.
Science does not yet have a solid explanation for the brain’s chaos states, but
Thatcher and other researchers believe that the electric noise of the chaos mode
allows the brain to experiment with new links between neurons that would
otherwise fail to connect in more orderly settings. The phase-lock mode (the
theory goes) is where the brain executes an established plan or habit. The chaos
mode is where the brain assimilates new information, explores strategies for
responding to a changed situation. In this sense, the chaos mode is a kind of
background dreaming: a wash of noise that makes new connections possible.
Even in our waking hours, it turns out, our brains gravitate toward the noise and
chaos of dreams, 55 milliseconds at a time.
William James, writing in the late 1880s, had no way of measuring
synchronized neuron firing, but his description of the “highest order of minds”
captures something of the chaos mode:
Instead of thoughts of concrete things patiently following one another,
we have the most abrupt cross-cuts and transitions from one idea to
another, the most rarefied abstractions and discriminations, the most
unheard-of combinations of elements . . . a seething caldron of ideas,
where everything is fizzling and bobbing about in a state of bewildering
activity, where partnerships can be joined or loosened in an instant,
treadmill routine is unknown, and the unexpected seems the only law.
The act of sexual reproduction is itself a kind of testament to the power of
random connections, even in the most monogamous relationships. The
overwhelming majority of nonmicroscopic life on earth produces offspring by
sharing genes with another organism. But the evolution of this reproductive
strategy remains something of a mystery. It would have been far easier for life to
have avoided the complicated genetic exchanges of meiosis and fertilization.
(Think of the elaborate system that the flowering plants had to evolve, luring
insects to take on the task of carrying pollen from flower to flower.)
Reproduction without sex is a simple matter of cloning; you take your own cells,
make a copy, and pass that on to your descendants. It doesn’t sound like much
fun to our mammalian ears, but it’s a strategy that has worked very well for
billions of years for bacteria. Asexual reproduction is faster and more energy
efficient than the sexual variety: you don’t need to go to the trouble of finding a
partner in order to create the next generation.
If natural selection rewarded organisms exclusively for sheer reproductive
power, sexual reproduction might never have evolved. Asexual organisms
reproduce on average twice as quickly as their sexual counterparts, in part
because without a male/female distinction, every organism is capable of
producing offspring directly. But evolution is not just a game of sheer quantity.
Overpopulation, after all, poses its own dangers, and a community of organisms
with identical DNA makes a prime target for parasites or predators. For these
reasons, natural selection also rewards innovation, life’s tendency to discover
new ecological niches, new sources of energy. This is what Stuart Kauffman
recognized when he first formulated the idea of the adjacent possible: that there
is something like an essential drive in the biosphere to diversify into new ways
of making a living. Scrambling together two distinct sets of DNA with each
generation made for a far more complicated reproductive strategy, but it paid
immense dividends in the rate of innovation. What we gave up in speed and
simplicity, we made up for in creativity.
The water flea Daphnia lives in most freshwater ponds and swamps. Its
spasmodic movements in the water are responsible for the “flea” description, but
in reality Daphnia are tiny crustaceans, no more than a few millimeters long.
Under normal conditions, Daphnia reproduce asexually, with females producing
a brood of identical copies of themselves in a tiny pouch. In this mode, the
Daphnia community is composed entirely of females. This reproductive strategy
proves to be stunningly successful: in warm summer months, Daphnia will often
be one of the most abundant organisms in a pond ecosystem. But when
conditions get tough, when droughts or other ecological disturbances happen, or
when winter rolls in, the water fleas make a remarkable transformation: they
start producing males and switch to reproducing sexually. In part, this switch is
attributable to the sturdier eggs produced by sexual reproduction, which are
more capable of surviving the long months of winter. But scientists believe that
the sudden adoption of sex is also a kind of biological innovation strategy: in
challenging times, an organism needs new ideas to meet those new challenges.
Reproducing asexually makes perfect sense during prosperous periods: if life is
good, keep doing what you’re doing. Don’t mess with success by introducing
new genetic combinations. But when the world gets more challenging—scarce
resources, predators, parasites—you need to innovate. And the quickest path to
innovation lies in making novel connections. This strategy of switching back and
forth between asexual and sexual reproduction goes by the name “heterogamy,”
and while it is unusual, many different organisms have adopted it. Slime molds,
algae, and aphids have all evolved heterogamous reproductive strategies. In each
organism, the Daphnia pattern repeats itself: the genetic recombinations of sex
emerge when conditions get difficult. Swapping genes with another organism is
itself more difficult than simple cloning, but the innovation rewards of sex
outweigh the risks of the more stable path. When nature finds itself in need of
new ideas, it strives to connect, not protect.
The English language is blessed with a wonderful word that captures the power
of accidental connection: “serendipity.” First coined in a letter written by the
English novelist Horace Walpole in 1754, the word derives from a Persian fairy
tale titled “The Three Princes of Serendip,” the protagonists of which were
“always making discoveries, by accident and sagacity, of things they were not in
quest of.” The contemporary novelist John Barth describes it in nautical terms:
“You don’t reach Serendip by plotting a course for it. You have to set out in
good faith for elsewhere and lose your bearings serendipitously.”
But serendipity is not just about embracing random encounters for the sheer
exhilaration of it. Serendipity is built out of happy accidents, to be sure, but what
makes them happy is the fact that the discovery you’ve made is meaningful to
you. It completes a hunch, or opens up a door in the adjacent possible that you
had overlooked. If you’re a geologist randomly exploring the Web, and the
particular isle of Serendip that you stumble across turns out to be an essay on
health-care reform, your discovery may well be interesting and informative, but
it will not be truly serendipitous unless it helps you fill in a piece of a puzzle
you’ve been poring over. That’s not to say that geologists can only find
serendipitous discoveries in texts about geology—quite the contrary, in fact.
Serendipitous discoveries often involve exchanges across traditional disciplines.
Think of the way Kekulé’s mythic serpent led to a revolution in organic
chemistry. It was genuinely serendipitous that Kekulé’s dreaming brain should
conjure up the image of Ouroboros at that moment. But had Kekulé not been
wrestling with the structure of the benzene molecule for years, that serpent shape
might not have triggered any useful associations in his mind. (Sometimes a
serpent swallowing its tail is just a serpent swallowing its tail, as Freud might
have said.) Serendipity needs unlikely collisions and discoveries, but it also
needs something to anchor those discoveries. Otherwise, your ideas are like
carbon atoms randomly colliding with other atoms in the primordial soup
without ever forming the rings and lattices of organic life.
The challenge, of course, is how to create environments that foster these
serendipitous connections, on all the appropriate scales: in the private space of
your own mind; within larger institutions; and across the information networks
of society itself.
At first blush, the idea of conjuring up serendipitous discoveries inside your
own mind seems like a contradiction in terms. Wouldn’t that be like losing your
bearings in your own driveway? Yet that’s exactly what Kekulé was doing by
the fire. He was connecting two distinct thoughts that each occupied a slot in his
memory banks: the riddle of benzene’s molecular structure, and the tailswallowing Ouroboros. The truth is, your mind contains a near-infinite number
of ideas and memories that at any given moment are lurking outside your
consciousness. Some tiny fraction of those thoughts are like Kekulé’s serpent:
surprising connections that might help you unlock a door in the adjacent
possible. But how do you get those particular clusters of neurons to fire at the
right time?
One way is to go for a walk. The history of innovation is replete with stories
of good ideas that occurred to people while they were out on a stroll. (A similar
phenomenon occurs with long showers or soaks in a tub; in fact, the original
“eureka” moment—Archimedes hitting upon a way of measuring the volume of
irregular shapes—occurred in a bathtub.) The shower or stroll removes you from
the task-based focus of modern life—paying bills, answering e-mail, helping
kids with homework—and deposits you in a more associative state. Given
enough time, your mind will often stumble across some old connection that it
had long overlooked, and you experience that delightful feeling of private
serendipity: Why didn’t I think of that before?
In his book The Foundations of Science, the French mathematician and
physicist Henri Poincaré devotes an autobiographical chapter to the question of
mathematical creativity. The chapter begins with a detailed account of how
Poincaré discovered the class of Fuchsian functions, one of the first influential
mathematical concepts of his career. He begins by attempting to prove that the
functions do not exist; for fifteen days he struggles at his desk with no success.
Then one evening he breaks from his ordinary routine and drinks black coffee.
Unable to sleep, his mind seethes with promising hunches. “Ideas rose in
crowds,” Poincaré writes. “I felt them collide until pairs interlocked, so to speak,
making a stable combination. By the next morning I had established the
existence of a class of Fuchsian functions, those which come from the
hypergeometric series.” His next insight—a connection between the functions
and non-Euclidean geometry—comes several weeks later, while boarding a bus
during a geological expedition in Normandy. On his return home, he commences
work on an unrelated arithmetical question and flounders for several days.
“Disgusted with my failure,” he writes, “I went to spend a few days at the
seaside, and thought of something else. One morning, walking on the bluff, the
idea came to me, with just the same characteristics of brevity, suddenness and
immediate certainty, that the arithmetic transformations of indeterminate ternary
quadratic forms were identical with those of non-Euclidean geometry.” He
returns home again and works through the implications, but encounters another
roadblock. Military service then dictates a trip to Fort Mont-Valérien in the
suburbs of Paris, where he has little time to think about mathematics at all. And
yet the final missing piece arrives nonetheless. “One day, going along the street,
the solution of the difficulty which had stopped me suddenly appeared to me. I
did not try to go deep into it immediately, and only after my service did I again
take up the question. I had all the elements and had only to arrange them and put
them together. So I wrote out my final memoir at a single stroke and without
difficulty.”
Poincaré’s account may be the most “pedestrian” story of scientific creativity
on record. Whenever he actually sits down at his desk, the innovations seem to
grind to a halt. But on foot, his ideas “rose in crowds.” Trying to explain the
phenomenon, Poincaré reaches for an atomic metaphor, with each partial idea or
hunch represented by an atom hooked to a wall. In normal situations, the atoms
remain in place, locked into a stable configuration. But when the mind wanders
(and, in Poincaré’s case, when the physical body wanders), the atoms become
untethered. “During a period of apparent rest and unconscious work, certain of
them are detached from the wall and put in motion. They flash in every direction
through the space . . . where they are enclosed, as would, for example, a swarm
of gnats or, if you prefer a more learned comparison, like the molecules of gas in
the kinematic theory of gases. Then their mutual impacts may produce new
combinations.”
While the creative walk can produce new serendipitous combinations of
existing ideas in our heads, we can also cultivate serendipity in the way that we
absorb new ideas from the outside world. Reading remains an unsurpassed
vehicle for the transmission of interesting new ideas and perspectives. But those
of us who aren’t scholars or involved in the publishing business are only able to
block out time to read around the edges of our work schedule: listening to an
audio book during the morning commute, or taking in a chapter after the kids are
down. The problem with assimilating new ideas at the fringes of your daily
routine is that the potential combinations are limited by the reach of your
memory. If it takes you two weeks to finish a book, by the time you get to the
next book, you’ve forgotten much of what was so interesting or provocative
about the original one. You can immerse yourself in a single author’s
perspective, but then it’s harder to create serendipitous collisions between the
ideas of multiple authors. One way around this limitation is to carve out
dedicated periods where you read a large and varied collection of books and
essays in a condensed amount of time. Bill Gates (and his successor at
Microsoft, Ray Ozzie) are famous for taking annual reading vacations. During
the year they deliberately cultivate a stack of reading material—much of it
unrelated to their day-to-day focus at Microsoft—and then they take off for a
week or two and do a deep dive into the words they’ve stockpiled. By
compressing their intake into a matter of days, they give new ideas additional
opportunities to network among themselves, for the simple reason that it’s easier
to remember something that you read yesterday than it is to remember something
you read six months ago.
In Poincaré’s language, the deep dive, like the long stroll, detaches the atoms
from the wall and puts them in motion. Most of us don’t have the luxury of
taking deep dive reading sabbaticals, of course, and reading a few thousand
pages is not everyone’s idea of a fun vacation. But there’s no reason why
organizations couldn’t recognize the value of a reading sabbatical, the way many
organizations encourage their employees to take time off for learning new skills.
If Google can give its engineers one day a week to work on anything they want,
surely other organizations can figure out a way to give their employees dedicated
time to immerse themselves in a network of new ideas.
Private serendipity can be cultivated by technology as well. For more than a
decade now, I have been curating a private digital archive of quotes that I’ve
found intriguing, my twenty-first-century version of the commonplace book.
Some of these passages involve very focused research on a specific project;
others are more random discoveries, hunches waiting to make a connection.
Some of them are passages that I’ve transcribed from books or articles; others
were clipped directly from Web pages. (In the past few years, thanks to Google
Books and the Kindle, copying and storing interesting quotes from a book has
grown far simpler.) I keep all these quotes in a database using a program called
DEVONthink, where I also store my own writing: chapters, essays, blog posts,
notes. By combining my own words with passages from other sources, the
collection becomes something more than just a file storage system. It becomes a
digital extension of my imperfect memory, an archive of all my old ideas, and
the ideas that have influenced me. There are now more than five thousand
distinct entries in that database, and more than 3 million words—sixty books’
worth of quotes, fragments, and hunches, all individually captured by me, stored
in a single database.
Having all that information available at my fingertips is not just a quantitative
matter of finding my notes faster. Yes, when I’m trying to track down an article I
wrote many years ago, it’s now much easier to retrieve. But the qualitative
change lies elsewhere: in finding documents that I’ve forgotten about altogether,
finding documents that I didn’t know I was looking for. What makes the system
truly powerful is the way that it fosters private serendipity.
DEVONthink features a clever algorithm that detects subtle semantic
connections between distinct passages of text. These tools are smart enough to
get around the classic search-engine failing of excessive specificity: searching
for “dog” and missing all the articles that only have the word “canine” in them.
Modern indexing software like DEVONthink’s learns associations between
individual words by tracking the frequency with which words appear near each
other. This can create almost lyrical connections between ideas. Several years
ago, I was working on a book about cholera in London and queried
DEVONthink for information about Victorian sewage systems. Because the
software had detected that the word “waste” is often used alongside “sewage,” it
directed me to a quote that explained the way bones evolved in vertebrate
bodies: namely, by repurposing the calcium waste products created by the
metabolism of cells. At first glance that might seem like an errant result, but it
sent me off on a long and fruitful tangent into the way complex systems—
whether cities or bodies—find productive uses for the waste they create. That
idea became a central organizing theme for one of the chapters in the cholera
book. (It will, in fact, reappear in this book in a different guise.)
Now, strictly speaking, who was responsible for that initial idea? Was it me,
or the software? It sounds like a facetious question, but I mean it seriously.
Obviously, the computer wasn’t conscious of the idea taking shape, and I
supplied the conceptual glue that linked the London sewers to cell metabolism.
But I’m not at all confident that I would have made the initial connection
without the help of the software. The idea was a true collaboration, two very
different kinds of intelligence playing off one another, one carbon-based, the
other silicon. When I’d first captured that quote about calcium and bone
structure, I’d had no idea that it would ultimately connect to the history of
London’s sewage system (or to a book about innovation). But there was
something about that concept that intrigued me enough to store it in the database.
It lingered there for years in the software’s primordial soup, a slow hunch
waiting for its connection.
I use DEVONthink as an improvisational tool as well. I write a paragraph
about something—let’s say it’s about the human brain’s remarkable facility for
interpreting facial expressions. I then plug that paragraph into the software, and
ask DEVONthink to find other passages in my archive that are similar. Instantly,
a list of quotes appears on my screen: some delving into the neural architecture
that triggers facial expressions, others exploring the evolutionary history of the
smile, others dealing with the expressiveness of our near-relatives, the
chimpanzees. Invariably, one or two of these triggers a new association in my
head—perhaps I’ve forgotten about the chimpanzee connection—and so I select
that quote, and ask the software to find a new batch of passages similar to it.
Before long, a larger idea takes shape in my head, built upon the trail of
associations the machine has assembled for me.
Compare that to the traditional way of exploring your files, where the
computer is like a dutiful, but dumb, butler: “Find me that document about the
chimpanzees!” That’s searching. The other feels radically different, so different
that we don’t quite have a verb for it: it’s riffing, or exploring. There are false
starts and red herrings, but there are just as many happy accidents and
unexpected discoveries. Indeed, the fuzziness of the results is part of what makes
the software so powerful. The serendipity of the system emerges out of two
distinct forces. First, there is the connective power of the semantic algorithm,
which is smart but also slightly unpredictable, thus creating a small amount of
randomizing noise that makes the results more surprising. But that randomizing
force is held in check by the fact that I have curated all these passages myself,
which makes each individual connection far more likely to be useful to me in
some way. When you start a new query in DEVONthink and look down at the
initial results, at first glance they can sometimes seem jumbled and
disconnected, but then you read through them in more detail, and inevitably
something tantalizing catches your eye. “Jumbled” and “disconnected” is of
course also how we describe the strange explorations of our dreams, and the
comparison is an apt one. DEVONthink takes the strange but generative
combinations of the dream state and turns them into software.
If you visit the “serendipity” entry in Wikipedia, you are one click away from
entries on LSD, Teflon, Parkinson’s disease, Sri Lanka, Isaac Newton, Viagra,
and about two hundred other topics of comparable diversity. That eclecticism is
particularly acute at Wikipedia, of course, but it derives from the fundamentally
“tangled” nature of Tim Berners-Lee’s original hypertext architecture. No
medium in history has ever offered such unlikely trails of connection and chance
in such an intuitive and accessible form. Yet in recent years, a puzzling meme
has emerged on op-ed pages with a strange insistence: the rise of the Web, its
proponents argue, has led to a decline in serendipitous discovery. Consider this
representative elegy to the “endangered joy of serendipity,” authored by a
journalism professor named William McKeen:
Think about the library. Do people browse anymore? We have become
such a directed people. We can target what we want, thanks to the
Internet. Put a couple of key words into a search engine and you find—
with an irritating hit or miss here and there—exactly what you’re looking
for. It’s efficient, but dull. You miss the time-consuming but enriching
act of looking through shelves, of pulling down a book because the title
interests you, or the binding . . . Looking for something and being
surprised by what you find—even if it’s not what you set out looking for
—is one of life’s great pleasures, and so far no software exists that can
duplicate that experience.
In a similar piece, the New York Times technology editor, Damon Darlin,
complained that the “digital age is stamping out serendipity.” Darlin
acknowledged the vast influx of suggested reading that now arrives on our
screen every morning via social network services like Twitter and Facebook, but
claimed those links didn’t constitute serendipity. “[They’re] really group-think,”
Darlin argued. “Everything we need to know comes filtered and vetted. We are
discovering what everyone else is learning, and usually from people we have
selected because they share our tastes.”
When critics complain about the decline of serendipity, they habitually point
to two “old media” mechanisms that allegedly have no direct equivalent on the
Web. McKeen mentions the first one: browsing the stacks in a library (or a
bookstore), “pulling down a book because the title interests you, or the binding.”
Old-style browsing does indeed lead to unplanned discoveries. But thanks to the
connective nature of hypertext, and the blogosphere’s exploratory hunger for
finding new stuff, it is far easier to sit down in front of your browser and stumble
across something completely brilliant but surprising than it is to walk through a
library, looking at the spines of books. Does everyone use the Web this way? Of
course not. But it is much more of a mainstream pursuit than randomly exploring
the library stacks, pulling down books because you like the binding, ever was.
This is the irony of the serendipity debate: the thing that is being mourned has
actually gone from a fringe experience to the mainstream of the culture.
The second analog-era mechanism that encourages serendipity involves the
physical limitations of the print newspaper, which forces you to pass by a
collection of artfully curated stories on a variety of topics, before you open up
the section that most closely matches your existing passions and knowledge. The
legal scholar Cass Sunstein refers to this as an example of the “architecture of
serendipity.” On the way to the sports section or the comics or the business page,
you happen to collide with a story about the abuses of African diamond mines,
and something in the headline catches your eye. A thousand words later, you’ve
learned something powerful about people living halfway around the world
whose existence you had never contemplated before. And perhaps there is some
kind of serendipitous click in that collision: you’d been looking for a new
charitable cause to support, or contemplating buying your spouse a diamond
ring. And then this story drops in your lap, and helps you complete the thought.
You weren’t looking for a story about diamond mines, but it was exactly what
you needed.
This is indeed a superb example of serendipity, and there is no doubt that
newspapers facilitated comparable accidental discoveries countless times over
countless breakfast tables during their heyday. The question is whether the
transition to the Web makes this sort of discovery more or less frequent. If you
compare the front pages of the print and online versions of a newspaper, the
Web actually appears to have the upper hand. The Internet scholar Ethan
Zuckerman compared the front page of the New York Times with that of its Web
cousin and found that the print version had twenty-three references on its front
page to articles in the paper (either in the form of lead articles themselves, or
short summaries teased below the fold). The front page at NYTimes.com, in
Zuckerman’s study, contained 315 links to articles and other forms of content. If
the architecture of serendipity lies in stumbling across surprising connections
while scanning the front page, then the Web is more than ten times as
serendipitous as the classic print newspaper.
Sunstein would no doubt argue that many people bypass the front door of their
online newspaper, going directly to the sports or business-section page that
they’ve bookmarked, or to some other filter tailor-made to their preexisting
interests. No doubt millions of people use comparable filters every morning. One
could reasonably question whether people like this who have gone out of their
way to avoid encountering the “big picture” of the newspaper front page were
ever likely to stumble across the diamond-mining story at the breakfast table
with a print paper, or ramble through the stacks of their local library. But
Sunstein and Darlin and McKeen are indeed correct when they argue that the
Internet gives us topical filters that were unheard of in the days of mass media.
But those filters are only part of the story. Filters reduce serendipity (unless your
particular interest lies in being surprised, which is part of the appeal of
beautifully miscellaneous blogs like Boing Boing). But beyond bookmarking,
filters are a second-generation addition to the architecture of the Web. They are
not native to it. What is native to the Web’s architecture are two key features that
have been great supporters of serendipity: a global, distributed medium in which
anyone can be a publisher, and a hypertext document structure in which it is
trivial to jump from a newspaper article to an academic essay to an encyclopedia
entry in a matter of seconds. The information diversity of the Web ensures that
there is an endless supply of surprising information to stumble across, and the
links of hypertext ensure that we can get to that information at lightning speed,
or follow trails of improvised association that would have been painfully slow to
follow in the age of print media. Ironically, the problem with the Web is that
there’s too much noise, too much chaos—that’s why the filters were invented in
the first place. We have filters because the Web has unleashed too much
diversity and surprise, not because we have too little.
I happen to believe that the Web, as a medium, has pushed the culture toward
more serendipitious encounters. The simple fact that information “browsing” and
“surfing” are now mainstream pursuits makes a strong case for a rise in
serendipity, compared to cultures dominated by books or mass media. But
whether or not you accept the premise that the average media consumer
experiences more serendipitous discoveries thanks to the Web, there can be little
doubt that the Web is an unrivaled medium for serendipity if you are actively
seeking it out. If you want to build a daily reading list of eclectic and diverse
perspectives, you can stitch one together in your RSS reader or your bookmarks
bar in a matter of minutes, for no cost, while sitting on your couch. Just as
important, you can use the Web to fill out the context when you do stumble
across some interesting new topic. The great oracle of the digital age, Google, is
often invoked as a serendipity killer, because search queries function as a kind of
on-demand filter that eliminates the 99.999 percent of the Web that is not
relevant to the searcher’s current interest. But when critics put Google on the
side of filters, they assume that most queries are variations on the theme: “I’m
passionately interested in x and would like to learn more about it.” No doubt
some unthinkably large number of Google users enter queries that take that basic
shape every day. But there’s another type of query that is just as valuable:
“Someone just told me about x and I know nothing about it, but it sounds
interesting. Tell me more.” This is the subtle way in which Google supports the
serendipitous aspects of the Web. Yes, it’s true that by the time you’ve entered
something into the Google search box, you’re already invested in the topic. (This
is why Web pioneer John Battelle calls it the “database of intentions.”) But often
that investment is directly correlated with your ignorance about the topic at
hand: someone mentions in passing the poetry of John Ashbery, or the television
show Arrested Development, or the tail-swallowing Ouroboros, and you think:
“What’s the deal with that? It sounds really interesting.” Imagine it’s 1980 and
you’re sitting at your breakfast table, reading the morning paper, and on the way
to the sports page you stumble across an article on the front page about this
provocative new idea of global warming that you’ve not yet encountered. You
can read the article, to be sure, but when the article leaves you hankering for
more information and context, where do you go? Turn on the television and hope
that one of the three networks or PBS is running a news item or a documentary
on the topic at that exact second? Get in the car, drive fifteen minutes down to
your public library and check out a book on the subject? Go through all the
magazines in your house, scouring their table-of-contents pages for any climatechange-related articles?
Let’s say you live in a particularly information-rich household for the
standards of 1980, and you happen to have a copy of the Encyclopoedia
Britannica. But of course the version you bought is actually the 1976 edition,
and global warming doesn’t make it into the Britannica until 1994, despite the
fact that the term is common enough to be mentioned in ordinary parlance
throughout the nineties.
Today, of course, you would query either Google or Wikipedia for the search
term “global warming.” And you would instantly have more information (and
more perspectives) at your fingertips than would have been imaginable when
you were thumbing through the Britannica in 1980. Yes, these results are
targeted to your expressed interest in a specific topic, but that interest is often
something you’ve just stumbled across, a hint more than a passion. And because
those pages are built out of hyperlinks, just a few clicks can land you in an
entirely new region of interest that you’d never dreamed of visiting. Google and
Wikipedia give those passing hints something to attach to, a kind of information
anchor that lets you settle down around a topic and explore the surrounding area.
They turn hints and happy accidents into information. If the commonplace book
tradition tells us that the best way to nurture hunches is to write everything
down, the serendipity engine of the Web suggests a parallel directive: look
everything up.
The premise that innovation prospers when ideas can serendipitously connect
and recombine with other ideas, when hunches can stumble across other hunches
that successfully fill in their blanks, may seem like an obvious truth, but the
strange fact is that a great deal of the past two centuries of legal and folk wisdom
about innovation has pursued the exact opposite argument, building walls
between ideas, keeping them from the kind of random, serendipitous connections
that exist in dreams and in the organic compounds of life. Ironically, those walls
have been erected with the explicit aim of encouraging innovation. They go by
many names: patents, digital rights management, intellectual property, trade
secrets, proprietary technology. But they share a founding assumption: that in
the long run, innovation will increase if you put restrictions on the spread of new
ideas, because those restrictions will allow the creators to collect large financial
rewards from their inventions. And those rewards will then attract other
innovators to follow in their path.
4
The problem with these closed environments is that they inhibit serendipity
and reduce the overall network of minds that can potentially engage with a
problem. This is why a growing number of large organizations—businesses,
nonprofits, schools, government agencies—have begun experimenting with
work environments that encourage the architecture of serendipity. Traditionally,
organizations that have a strong demand for innovation have created a kind of
closed playpen for hunches: the research-and-development lab. Ironically, R&D
labs have historically functioned as a kind of idea lockbox; the hunches evolving
in those labs tended to be the most heavily guarded secrets in the entire
organization. Allowing these early product ideas to circulate more widely would
allow rival firms to copy or exploit them. Some organizations—including Apple
—have gone to great length to keep R&D experiments sequestered from other
employees inside the organization.
But that secrecy, as we have seen, comes with great cost. Protecting ideas
from copycats and competitors also protects them from other ideas that might
improve them, might transform them from hints and hunches to true innovations.
And indeed there is a grow-ing movement in some forward-thinking companies
to turn their R&D labs inside out and make them far more transparent than the
traditional model. Organizations like IBM and Procter & Gamble, who have a
long history of profiting from patented, closed-door innovations, have embraced
open innovation platforms over the past decade, sharing their leading-edge
research with universities, partners, suppliers, and customers.
In early 2010, Nike announced a new Web-based marketplace it called the
GreenXchange, where it publicly released more than 400 of its patents that
involve environmentally friendly materials or technologies. The marketplace
was a kind of hybrid of commercial self-interest and civic good. By making its
good ideas public, Nike made it possible for outside firms to improve on those
innovations, creating new value that Nike itself might ultimately be able to put to
use in its own products. In a sense, Nike was widening the network of minds
who were actively thinking about how to make its ideas more useful, without
putting anyone else on its payroll. But Nike’s organizational values also include
a commitment to environmental sustainability, and the company recognized that
many of its eco-friendly patents might be useful in different contexts. Nike is a
big corporation, with many products in many categories, but there are limits to
its reach. Some of its innovations might well turn out to be advantageous to
industries or markets where it has no competitive involvement whatsoever. By
keeping its eco-friendly ideas behind a veil of secrecy, Nike was holding back—
without any real commercial justification—ideas that might, in another context,
contribute to a sustainable future. In collaboration with Creative Commons, Nike
released its patents under a modified license permitting use in “non-competitive”
fields. (They also created a standardized, pre-negotiated contract for the patents,
thereby reducing the transaction costs of haggling over each patent license
individually.) The example scenario they invoked at the launch of
GreenXchange would have warmed the heart of Stephen Jay Gould: an
environmentally sound rubber originally invented for use in running shoes that
could be adapted by a mountain bike company to create more sustainable tires.
Apparently, Gould’s tires-to-sandals principle works both ways. Sometimes you
make footwear by putting tires to new use, sometimes you make tires by putting
footwear to new use. Green Xchange is trying to give multinational corporations
some of the same freedom to reinvent and recycle that Gould’s sandal-makers
enjoy sifting through the Nairobi junkyards.
The other organizational technique for facilitating serendipitous connections is
the “brainstorm” session, an approach pioneered by the advertising executive
Alex Osborn in the 1930s. Brainstorming opens up the flow of ideas and
hunches in a more generative fashion than is customary in a regimented
workplace meeting. Yet a number of recent studies have suggested that
brainstorming is less effective than its practitioners would like. One trouble with
brainstorming is that it is finite in both time and space: a group gathers for an
hour in a room, or for a daylong corporate retreat, they toss out a bunch of crazy
ideas, and then the meeting disperses. Sometimes a useful connection emerges,
but too often the relevant hunches aren’t in sync with one another. One
employee has a promising hunch in one office, and two months later, another
employee comes up with the missing piece that turns that hunch into a genuine
insight. Brainstorming might bring those two fragments together, but the odds
are against it. Imagine some kind of alternate reality where the FBI holds a
corporate retreat in late August of 2001, and invites the field agents from
Arizona and Minnesota to sit in a room together and brainstorm new potential
threats against the United States. No doubt it would have been the first corporate
retreat on record that actually changed the fate of world history, but with more
than ten thousand field agents across the nation, the odds against getting the right
people from Arizona and Minnesota together at the right time would have been
astronomical. But imagine if the FBI had been using a networked version of a
DEVONthink archive instead of the archaic Automated Case Support system.
The top brass at the Radical Fundamentalist Unit would still have read the search
warrant request for Moussaoui’s laptop and thought to themselves, “This sounds
like a pretty shaky hunch.” But a quick DEVONthink query would have pointed
them to the Phoenix memo, to another hunch about flight training and terrorism.
Those two unlikely ideas would have collided, without the field agents in
Phoenix and Minnesota even speaking to each other, much less sitting down for
a brainstorming session.
The secret to organizational inspiration is to build information networks that
allow hunches to persist and disperse and recombine. Instead of cloistering your
hunches in brainstorm sessions or R&D labs, create an environment where
brainstorming is something that is constantly running in the background,
throughout the organization, a collective version of the 20-percent-time concept
that proved so successful for Google and 3M. One way to do this is to create an
open database of hunches, the Web 2.0 version of the traditional suggestion box.
A public hunch database makes every passing idea visible to everyone else in the
organization, not just management. Other employees can comment or expand on
those ideas, connecting them with their own hunches about new products or
priorities or internal organizational changes. Some systems even allow
employees to vote on their colleagues’ suggestions, not unlike the user rankings
that power collective news sites like Digg or Reddit. Google has a companywide e-mail list where employees can suggest new features or products; each
suggestion can then be rated on a scale of 0 (“Dangerous or harmful”) to 5
(“Great idea! Make it so.”). Salesforce.com maintains a popular Idea Exchange
where its customers can suggest new features for the company’s software
products. The Idea Exchange doesn’t just allow interesting hunches to circulate
and connect. It also tracks their maturation into shipping code: the front door of
the Exchange includes prominent links to submitted ideas currently being
considered for inclusion in future releases, as well as ideas that were
successfully integrated into past releases. Too often, real-world suggestion boxes
feel like a black hole; you drop your idea in the slot, and never hear about it
again. In a public forum like Idea Exchange, not only do you get to see and
improve other people’s suggestions, but you get tangible evidence that your
ideas can make a difference.
These kinds of information networks can do a masterful job of tapping both
individual and collective intelligence: the individual employee has a provocative
and useful hunch, and the group helps complete the hunch by connecting it to
other ideas that have circulated through the system, and helps separate out that
hunch from the thousands of other less useful ones by voting it to the top of the
charts. By making the ideas public, and by ensuring that they remain stored in
the database, these systems create an architecture for organizational serendipity.
They give good ideas new ways to connect.
V.
ERROR
In the summer of 1900 a twenty-seven-year-old aspiring inventor named Lee de
Forest moved to Chicago, rented a one-room apartment on Washington
Boulevard, and took a day job translating foreign articles on wireless technology
for Western Electrician magazine. The translation work was informative: a
major exposition on wireless technology that had just been held in Paris
guaranteed a constant flow of interesting new research papers across the
Atlantic. But de Forest’s true passion lay in the cabinet of wonders he had
assembled in his bedroom on Washington Boulevard: batteries, spark gap
transmitters, electrodes—all the building blocks that would be assembled in the
coming decade to invent the age of electronics.
For a budding innovator in wireless telegraphy at the turn of the century, the
spark gap transmitter was the most essential of gadgets. Hertz and Marconi’s
original explorations of the electromagnetic spectrum had relied on spark gaps.
The device employed two electrodes separated by a small gap. A battery
attached to the electrodes supplied a surge of electricity, which caused a spark to
jump from one electrode to the other, triggering a pulse of electromagnetic
activity that could be detected and amplified by antennae miles away. Spark gap
machines emitted a terse blast of monotone noise, perfect for sending Morse
code.
On the night of September 10, 1900, de Forest was experimenting with his
spark gap machine in the corner of his Washington Boulevard bedroom. Across
the room, the red flame of a Welsbach burner flickered fifteen feet away. De
Forest triggered a surge of voltage through the spark gap, and as the machine
crackled, he could see the flame of the burner instantly change from red to white
heat. De Forest later estimated that the flame’s intensity had increased by several
candlepower. Somehow, for reasons that de Forest could not explain, the
electromagnetic pulse of the spark gap was intensifying the energy of a flame
fifteen feet away. Watching that flame shift from red to white planted the seed of
an idea in de Forest’s head: that a gas could be employed as a wireless detector,
one that might be more sensitive than anything Marconi or Tesla had created to
date.
De Forest had stumbled across a classic slow hunch. In his autobiography, de
Forest described the gas-flame detector as “a subject that had ever since been in
the back of my mind.” In the end, that hunch would mature into an invention that
ultimately changed the landscape of the twentieth century, an invention that
made radio, television, and the first digital computers possible. In 1903, he
began a series of failed experiments with placing two electrodes in gas-filled
glass bulbs. He continued tinkering with the model, until, several years later, he
hit upon the idea of placing a third electrode in the bulb, attached to an antenna
or external tuner. After a number of iterations, he used a piece of wire that had
been bent back and forth several times as the middle electrode; de Forest called
it the grid. Early tests showed that the device, which de Forest dubbed the
Audion, proved far superior to other technology at amplifying audio signals
without degrading the tuner’s ability to separate out signals at different
frequencies.
De Forest’s creation would eventually be called a triode. Its three-electrode
architecture would form the basis of the vacuum tubes that began to be massproduced in the following decade. Radio receivers, telephone switchboards,
television sets—all the communications revolutions of the first half of the
century relied on some variation of de Forest’s design to boost their signals.
Initially employed for amplification, the vacuum tube turned out to have an
unforeseen use as an electronic switch, enabling the highspeed logic gates of the
first digital computers in the 1940s. When de Forest twisted the wire into the
shape of a grid and placed it between those two electrodes, he was unwittingly
opening up the adjacent possible for the Analytical Engine that Charles Babbage
had failed to produce sixty years before. The power of that new portal was
apparent instantly: the first computer built with vacuum tubes, the mammoth
ENIAC, ran calculations that helped develop the hydrogen bomb.
The invention of the Audion sounds like a classic story of ingenuity and
persistence: a maverick inventor holed up in his bedroom lab notices a striking
pattern and tinkers with it for years as a slow hunch, until he hits upon a
contraption that changes the world. But telling the story that way misses one
crucial fact: that at almost every step of the way, de Forest was flat-out wrong
about what he was inventing. The Audion was not so much an invention as it
was the steady, persistent accumulation of error. The strange communication
between the spark gap transmitter and the Wersbach gas burner flame turned out
to have nothing to do with the electromagnetic spectrum. (The flame was
responding to ordinary sound waves emitted by the spark gap transmitter.) But
because de Forest had begun with this erroneous notion that the gas flame was
detecting the radio signals, all his iterations of the Audion involved some lowpressure gas inside the device, which severely limited their reliability. It took
another decade for researchers at General Electric and other firms to realize that
the triode performed far more effectively in a true vacuum. (Hence the term
“vacuum tube.”) Even de Forest himself willingly admitted that he didn’t
understand the device he had invented. “I didn’t know why it worked,” he
remarked. “It just did.”
De Forest may have been the most erratic of the twentieth century’s great
inventors, but the error-prone history of his greatest success is hardly anomalous.
The history of being spectacularly right has a shadow history lurking behind it: a
much longer history of being spectacularly wrong, again and again. And not just
wrong, but messy. A shockingly large number of transformative ideas in the
annals of science can be attributed to contaminated laboratory environments.
Alexander Fleming famously discovered the medical virtues of penicillin when
the mold accidentally infiltrated a culture of Staphylococcus he had left by an
open window in his lab. In the 1830s, Louis Daguerre spent years trying to coax
images out of iodized silver plates. One night, after another futile attempt, he
stored the plates in a cabinet packed with chemicals; to his wonder the next
morning, the fumes from a spilled jar of mercury produced a perfect image on
the plate—and the daguerreotype, forerunner of modern photography, was born.
In the summer of 1951, a World War II Navy veteran named Wilson
Greatbatch was working at an animal behavior farm affiliated with the
psychology department at Cornell, where he was studying under the G.I. Bill.
Greatbatch had long been a ham radio enthusiast; as a teenager, he had built his
own shortwave radio by cobbling together the descendants of de Forest’s
Audion. His love of gadgets had drawn him to the Cornell farm because the
psychology department needed someone to attach experimental instruments to
the animals, measuring their brain waves, heartbeats, and blood pressure. One
day, Greatbatch happened to sit at lunch with two visiting surgeons and got into
a conversation about the dangers of irregular heartbeats. Something in their
description of the ailment triggered an association in Greatbatch’s mind. He
imagined the heart as a radio that was failing to transmit or receive a signal
properly. He knew the history of modern electronics had been all about
regulating the electrical signals passed between devices with ever more
miraculous precision. Could you take all that knowledge and apply it to the
human heart?
Greatbatch stored the idea in the back of his head for the next five years,
where it lingered as a slow hunch. He moved to Buffalo, started teaching
electrical engineering, and moonlighted at the Chronic Disease Institute. A
physician at the institute recruited Greatbatch to help him engineer an oscillator
that would record heartbeats using the new silicon transistors that were
threatening to replace the vacuum tube. One day, while working on the device,
Greatbatch happened to grab the wrong resistor. When he plugged it into the
oscillator it began to pulse in a familiar rhythm. Thanks to Greatbatch’s error,
the device was simulating the beat of a human heart, not recording it. His mind
flashed back to his conversation on the farm five years before. Here, at last, was
the beginning of a device that could restore the faulty signal of an irregular heart,
by shocking it back into sync at precise intervals. Within two years, Greatbatch
and a Buffalo surgeon named William Chardack deployed the first implantable
cardiac pacemaker on the heart of a dog. By 1960, the Greatbatch-Chardack
pacemaker was pulsing steadily in the chests of ten human beings. Variations of
Greatbatch’s original design have now saved or prolonged millions of lives
around the world.
Greatbatch’s pacemaker is an instance where a great idea came—literally—
from a novel combination of spare parts. Sometimes those novel combinations
arrive courtesy of the random collisions of city streets or the dreaming brain. But
sometimes they come from simple mistakes. You reach into the bag of resistors
and pull out the wrong one, and four years later, you’re saving someone’s life.
Yet error on its own is rarely enough. Greatbatch had his epiphany while hearing
the reliable pulse of his oscillator because he’d been thinking about the irregular
heartbeats as a signal transmission problem for five years. This, too, is a
recurring pattern in the history of being wrong. The inventions of radiography,
vulcanized rubber, and plastic all depended on generative mistakes that were
generative precisely because they connected to slow hunches in the minds of
their creators.
The British economist William Stanley Jevons, who had firsthand experience
as an inventor himself, described the prominence of error in his Principles of
Science, first published in 1874:
It would be an error to suppose that the great discoverer seizes at once
upon the truth, or has any unerring method of divining it. In all
probability the errors of the great mind exceed in number those of the
less vigorous one. Fertility of imagination and abundance of guesses at
truth are among the first requisites of discovery; but the erroneous
guesses must be many times as numerous as those that prove well
founded. The weakest analogies, the most whimsical notions, the most
apparently absurd theories, may pass through the teeming brain, and no
record remain of more than the hundredth part.
“The errors of the great mind exceed in number those of the less vigorous
one.” This is not merely statistics. It is not that the pioneering thinkers are
simply more productive than less “vigorous” ones, generating more ideas
overall, both good and bad. Some historical studies of patent records have in fact
shown that overall productivity correlates with radical breakthroughs in science
and technology, that sheer quantity ultimately leads to quality. But Jevons is
making a more subtle case for the role of error in innovation, because error is not
simply a phase you have to suffer through on the way to genius. Error often
creates a path that leads you out of your comfortable assumptions. De Forest was
wrong about the utility of gas as a detector, but he kept probing at the edges of
that error, until he hit upon something that was genuinely useful. Being right
keeps you in place. Being wrong forces you to explore.
Thomas Kuhn makes a comparable argument for the role of error in The
Structure of Scientific Revolutions. Paradigm shifts, in Kuhn’s argument, begin
with anomalies in the data, when scientists find that their predictions keep
turning out to be wrong. When Joseph Priestley first placed a mint plant in a bell
jar to deprive it of oxygen, he expected that the plant would die, just as mice or
spiders perished in the same circumstances. But he was wrong: the plant thrived.
In fact, it thrived even if you burned all the oxygen out of the jar before placing
the plant in it. Priestley’s error energized him to investigate this strange
behavior, and it ultimately led him to one of the founding discoveries of what we
now call ecosystem science: the realization that plants expel oxygen as part of
photosynthesis, and indeed have created much of the earth’s atmosphere. As
William James put it, “The error is needed to set off the truth, much as a dark
background is required for exhibiting the brightness of a picture.” When we’re
wrong, we have to challenge our assumptions, adopt new strategies. Being
wrong on its own doesn’t unlock new doors in the adjacent possible, but it does
force us to look for them.
The trouble with error is that we have a natural tendency to dismiss it. When
Kevin Dunbar analyzed the data from his in vivo studies of microbiology labs,
one of his most remarkable findings was just how many experiments produced
results that were genuinely unexpected. More than half of the data collected by
the researchers deviated significantly from what they had predicted they would
find. Dunbar found that the scientists tended to treat these surprising outcomes
as the result of flaws in their experimental method: some kind of contamination
of the original tissue perhaps, or a mechanical malfunction, or an error at the
data-processing phase. They assumed the result was noise, not signal.
Transforming error into insight turned out to be one of the key functions of the
lab conference. In Dunbar’s research, outsiders working on different problems
were much less likely to dismiss the apparent error as useless noise. Coming at
the problem from a different perspective, with few preconceived ideas about
what the “correct” result was supposed to be, allowed them to conceptualize
scenarios where the mistake might actually be meaningful. As the science writer
Jonah Lehrer has observed, this pattern appears in one of the great scientific
breakthroughs of twentieth-century physics, the discovery of cosmic background
radiation, which was mistaken for meaningless static by the astronomers Arno
Penzias and Robert Wilson for more than a year, until a chance conversation
with a Princeton nuclear physicist planted the idea that the noise was not the
result of faulty equipment, but rather the still lingering reverberation of the Big
Bang. Two brilliant scientists with great technological acumen stumble across
evidence of the universe’s origin—evidence that would ultimately lead to a
Nobel Prize for both them—and yet their first reaction is: Our telescope must be
broken.
About thirty years ago, a Berkeley psychology professor named Charlan Nemeth
began investigating the relationship between noise, dissent, and creativity in
group environments. One of Nemeth’s early experiments assembled small
groups of test subjects and showed them a series of slides, each of which was
dominated by a single color. The subjects were asked to evaluate the color and
the brightness of each slide. After they had analyzed the slides, Nemeth asked
them to free-associate on the color they had perceived in the slides.
There are few actions as commonly connected to the pursuit of creativity as
free-associating. Trying to come up with a new slogan for a detergent?
Struggling for a new perspective on your memories of childhood trauma?
Compiling thoughts for a sonnet? Free-associating, we are told, will help us find
our answer.
But psychologists have long been in on the joke that humans free-associate in
absurdly predictable ways. Take a hundred Americans off the street and ask
them to free-associate on the word “green” and forty of them will say “grass.”
Another forty will offer up another color—“red” or “yellow” or “blue”—or the
word “color” itself. The more creative associations only emerge when you get to
the bottom 20 percent of responses, the long tail of free association, where words
like “Ireland,” or “money,” or “leaves” appear. Ask them to free-associate on the
word “blue” and you’ll see the same pattern: 80 percent will suggest either
another color or the word “sky,” and the last 20 percent of associations will be
scattered across dozens of less predictable responses: “jeans,” “lake,” or
“lonely.”
Psychologists have assembled immense probability tables that document the
patterns of free association for hundreds of words. These norms of association
give them a stable yardstick for measuring creative thinking in different
environments. Some situations cause people to get even more predictable with
their associations, offering up “grass” and “blue” like obedient robots. But other
situations can push their associations down the tail of the distribution, into the
more eclectic zone of “Ireland” and “money.” Individuals who are unusually
creative tend to generate more original associations when tested.
Charlan Nemeth’s experiment was a perfect embodiment of this predictability.
Blue slides triggered utterly conventional word associations: “sky” and “green”
and “color” dominated, while the more innovative associations were restricted to
the bottom 20 percent.
But then Nemeth ran another version of the experiment, this time with a twist.
She showed the same slides to small groups of subjects—only, in this version,
she secretly seeded each group with a handful of actors who were instructed to
describe each slide inaccurately, as if it were a different color. The real test
subjects correctly described the blue slides as blue and were surprised to find
that their peers somehow saw the very same color and perceived it as green.
When Nemeth took this cohort (that is, the test subjects minus the actors) and
asked them to free-associate on the color names they had mentioned, the words
they came up with were markedly different from the earlier group’s. Some of
them dutifully suggested “sky,” like normal respondents, but the sort of
associations that usually resided in the creative tail of the distribution—“jazz” or
“jeans”—were far more numerous. In other words, when subjects were exposed
to inaccurate descriptions of the slides, they became more creative. Associations
that traditionally lay on the fringes of the probability table suddenly became
mainstream. Nemeth had deliberately introduced noise into the decision-making
process, and what she found ran directly counter to our intuitive assumptions
about truth and error. The groups that had been deliberately contaminated with
erroneous information ended up making more original connections than the
groups that had only been given pure information. The “dissenting” actors
prodded the other subjects into exploring new rooms in the adjacent possible,
even though they were, technically speaking, adding incorrect data to the
environment.
Nemeth has gone on to document the same phenomenon at work in dozens of
different environments: mock juries, boardrooms, academic seminars. Her
research suggests a paradoxical truth about innovation: good ideas are more
likely to emerge in environments that contain a certain amount of noise and
error. You would think that innovation would be more strongly correlated with
the values of accuracy, clarity, and focus. A good idea has to be correct on some
basic level, and we value good ideas because they tend to have a high signal-tonoise ratio. But that doesn’t mean you want to cultivate those ideas in noise-free
environments, because noise-free environments end up being too sterile and
predictable in their output. The best innovation labs are always a little
contaminated.
The next time you visit a zoo or a natural history museum and survey the
extraordinary diversity of the organisms on our planet, pause for a second to
remind yourself that all this variation—the elephant tusks and peacock tails and
human neocortices—was made possible, in part, by error. Without noise,
evolution would stagnate, an endless series of perfect copies, incapable of
change. But because DNA is susceptible to error—whether mutations in the code
itself or transcription mistakes during replication—natural selection has a
constant source of new possibilities to test. Most of the time, these errors lead to
disastrous outcomes, or have no effect whatsoever. But every now and then, a
mutation opens up a new wing of the adjacent possible. From an evolutionary
perspective, it’s not enough to say “to err is human.” Error is what made humans
possible in the first place.
The prominence of random mutation in our evolutionary history has long been
associated with Darwin’s original theory, but the truth is that Darwin himself
had a hard time accepting the premise that undirected random variation could
produce the marvelous innovations of life. When Darwin first outlined the theory
of natural selection as the “preservation of favourable variations and rejection of
injurious variations” in On the Origin of Species, he lacked a convincing theory
about where all those variations came from. In Origin, he generally writes about
them as though they are random, in part because he is explicitly trying to shed
the Lamarckian notion of directed variation, where new innovations—the
giraffe’s long neck being the canonical example—are generated by activity
during the organism’s lifetime, and then passed down to the next generation. But
during the decade that followed, Darwin retreated from the cliff of random
variation and developed a theory called pangenesis, first published in his 1868
book, The Variation of Animals and Plants under Domestication. Pangenesis
dialed back the noise of Darwin’s original theory, introducing a complex
mechanism for heredity that created a kind of directed variation. In Darwin’s
theory, each cell in the body released hereditary particles called gemmules that
collected in the germ cells of the organism. A particular organ or limb that was
heavily used during the lifetime of the animal would release more gemmules,
and thus shape the physiology of the next generation. Pangenesis was well
received at the time Darwin proposed it, but the modern science of genetics
would ultimately reveal it to be entirely false. It would prove to be the most
egregious mistake of his scientific career. In a sense, Darwin’s greatest error was
his failure to understand the protean force of error.
Too much error is deadly, of course, which is why your cells contain elaborate
mechanisms for repairing damaged DNA and for ensuring that the transcoding
process is accurate down to the last nucleotide. An organism that constantly
rescrambled the genetic code passed down to its descendants would be more
innovative in its offspring, but only in the sense that those offspring would find
many novel ways to perish before or shortly after birth. No parents want genetic
mutations in their child. But as a species we have been dependent on mutation.
That dependence is why some scientists have argued that natural selection has
gravitated toward a small but stable error rate in DNA transcoding, that
evolution has, in a sense, “tuned” the error rate to the optimal balance between
too much mutation and too much stability. One might think, given the severe
threat associated with transcoding errors, that there would be extraordinary
selection pressure to make the DNA repair system foolproof. Parents who made
perfect copies of their germ cells would have healthier offspring, while parents
with faulty DNA repair would have fewer surviving offspring, thanks to their
higher mutation rates. Over time, the genes for foolproof DNA repair would
spread through the society at large. The complexity of the DNA repair system
suggests that evolution did largely follow this path, only it stopped short of
eliminating error altogether. Our cells appear to be designed to leave the door for
mutation ever so slightly open, just enough to let a small trickle of change and
variation in, without catastrophic effects for the population as a whole. Recent
studies suggest that the mutation rate in human germ cells is roughly one in
thirty million base pairs, which means each time parents pass their DNA on to a
child, that genetic inheritance comes with roughly 150 mutations. Much of the
machinery in our cells is devoted to preserving and reproducing the signal of the
genetic code. But evolution has still made room for noise.
Is that error rate the result of selection pressures, or just a reflection of the fact
that evolution is not perfect? Humans have relatively good vision, as mammals
go, but we can’t read magazine text from five hundred feet. That’s not
necessarily a sign that there is something adaptive about that limitation; it’s more
likely that it’s hard to engineer an eye that can see that well; and, as powerful as
evolution is, it can’t do everything. Presumably we would have been more
evolutionarily “fit” if we’d been able to run a hundred miles per hour, too, but
the restrictions of our bone and muscle structure kept us from being able to
outrace the cheetahs. Why couldn’t the same be true of our imperfect DNA
repair system?
It may well be that perfect replication is simply an ideal limit that natural
selection can only approach asymptotically. For our purposes, it doesn’t really
matter whether selection has actively tuned our DNA repair systems for a certain
level of noise or whether they simply fell short of their “goal” of perfect
reproduction. One way or another, the noise had to be preserved, because
without it, evolution would grind to a halt. But the tuning hypothesis has had
some tantalizing research on its side of late. Bacteria have much higher mutation
rates than multicellular life-forms, which suggests that the tolerance for error
varies according to the specific circumstances of different organisms. One study
by Susan Rosenberg at Baylor College found that bacteria increased their
mutation rates dramatically when confronted with the “stress” of low energy
supplies. When the living is good, Rosenberg’s research suggests, bacteria have
less of a need for high mutation rates, because their current strategies are well
adapted to their environment. But when the environment grows more hostile, the
pressure to innovate—to stumble across some new way of eking out a living in a
resource-poor setting—shifts the balance of risk versus reward involved in
mutation. The risk of your offspring dying from some deadly mutation doesn’t
look quite as bad if they’re going to die of starvation anyway. And if one of
those mutations helps the bacteria use the limited energy resources more
efficiently, the new gene will quickly spread through the population as the
nonmutated bacteria die off.
In a sense, Rosenberg’s mutating bacteria are following a strategy similar to
what the water fleas adopted in their oscillation between sexual and asexual
reproduction. When the going gets tough, life tends to gravitate toward more
innovative reproductive strategies, sometimes by introducing more noise into the
signal of genetic code, and sometimes by allowing genes to circulate more
quickly through the population.
Sex and error turn out to have a long interconnected history, which is probably
not news to those who remember their college love life. One of the key
advantages to sexual reproduction is that it enables mutated genes to break off
from the genes that produce higher rates of mutation. Picture a bacterium that
possesses a gene that inhibits its DNA repair slightly, increasing its overall
mutation rate. Most of those mutations will be inconsequential or downright
lethal, but imagine one day it hits the jackpot and stumbles across a mutation
that increases its reproductive fitness—say, for the sake of argument, that it
enables the organism to detect food sources more efficiently. Our fortunate
bacterium splits in two and passes its genes on to the next generation. The
trouble is, that next generation gets a mixed inheritance: it inherits the new
scavenging gene, but it also inherits the gene that produces higher mutation
rates. Because negative mutations are much more likely than positive ones, over
generations the advantages of the scavenging gene get overwhelmed by the noise
introduced by the gene that causes higher mutation rates. But if our lucky
bacterium could suddenly switch to sexual reproduction, as the water flea does,
the outcome might be very different, because in sexual reproduction you only
pass along half your genes to your offspring. The next generation can inherit her
father’s knack for scavenging and her mother’s gift for accurate DNA repair.
We have already explored some of the reasons why evolution would gravitate
toward the far more complicated system of sexual reproduction: it allows
potentially useful innovations to spread through the population and occasionally
collide and join forces with other innovations. But when you think about sex in
the context of those mutation and scavenging genes, it becomes clear that so
much of life on earth embraced sexual reproduction for another reason: because
sex helped harness the generative power of error while mitigating the risks. Sex
keeps the door to the adjacent possible open by just a crack, so that we can adapt
to the changing pressures or opportunities of our environment. By keeping the
opening so narrow, it also keeps mutation rates in check, which is one crucial
reason the asexual bacteria have such markedly higher error rates than the
multicellular life. Sex lets us learn from the mistakes of our genes.
It’s this complicated relationship between accuracy and error, between signal
and noise, that explains Charlan Nemeth’s research on free association and jury
deliberation. When one of our peers calls the blue painting green, or comes to
the defense of a suspect who is clearly guilty, he or she is, technically speaking,
introducing more inaccurate information to the environment. But that noise
makes the rest of us smarter, more innovative, precisely because we’re forced to
rethink our biases, to contemplate an alternate model in which the blue paintings
are, in fact, green. Being correct is like the phase-lock states of the human brain,
all the neurons firing in perfect synchrony. We need the phase-lock state for the
same reason we need truth: a world of complete error and chaos would be
unmanageable, on a social and a neurochemical level. (Not to mention genetic.)
But leaving some room for generative error is important, too. Innovative
environments thrive on useful mistakes, and suffer when the demands of quality
control overwhelm them. Big organizations like to follow perfectionist regimes
like Six Sigma and Total Quality Management, entire systems devoted to
eliminating error from the conference room or the assembly line, but it’s no
accident that one of the mantras of the Web startup world is fail faster. It’s not
that mistakes are the goal—they’re still mistakes, after all, which is why you
want to get through them quickly. But those mistakes are an inevitable step on
the path to true innovation. Benjamin Franklin, who knew a few things about
innovation himself, said it best: “Perhaps the history of the errors of mankind, all
things considered, is more valuable and interesting than that of their discoveries.
Truth is uniform and narrow; it constantly exists, and does not seem to require so
much an active energy, as a passive aptitude of soul in order to encounter it. But
error is endlessly diversified.”
VI.
EXAPTATION
Two years before Pliny the Elder died, during a daring rescue of friends after the
eruption of Mount Vesuvius, the legendary Roman historian and scholar
completed his proto-encyclopedia, Naturalis Historiae. In it he tells the story of
a device winemakers had recently invented, a new kind of press that employed a
screw to “concentrate pressure upon broad planks placed over the grapes, which
are covered also with heavy weights above.” There is some debate among
scholars over whether Pliny may have been rooting for the home team in
attributing the invention to his compatriots, since evidence for the use of screw
presses in producing wines and olive oils dates back several centuries, to the
Greeks. But whatever the exact date of its origin, the practical utility of the
screw press, unlike so many great ideas from the Greco-Roman period, ensured
that it survived intact through the Dark Ages. When the Renaissance finally
blossomed, more than a millennium after Pliny’s demise, Europe had to
rediscover Ptolemaic astronomy and the secrets of building aqueducts. But they
didn’t have to relearn how to press grapes. In fact, they’d been tinkering steadily
with the screw press all along, improving on the model, and optimizing it for the
mass production of wines. By the mid-1400s, the Rhineland region of Germany,
which historically had been hostile to viticulture for climate reasons, was now
festooned with vine trellises. Fueled by the increased efficiency of the screw
press, German vineyards reached their peak in 1500, covering roughly four times
as much land as they do in their current incarnation. It was hard work producing
drinkable wine in a region that far north, but the mechanical efficiency of the
screw press made it financially irresistible.
Sometime around the year 1440, a young Rhineland entrepreneur began
tinkering with the design of the wine press. He was fresh from a disastrous
business venture manufacturing small mirrors with supposedly magical healing
powers, which he intended to sell to religious pilgrims. (The scheme got
derailed, in part by bubonic plague, which dramatically curtailed the number of
pilgrims.) The failure of the trinket business proved fortuitous, however, as it
sent the entrepreneur down a much more ambitious path. He had immersed
himself in the technology of Rhineland vintners, but Johannes Gutenberg was
not interested in wine. He was interested in words.
As many scholars have noted, Gutenberg’s printing press was a classic
combinatorial innovation, more bricolage than breakthrough. Each of the key
elements that made it such a transformative machine—the movable type, the ink,
the paper, and the press itself—had been developed separately well before
Gutenberg printed his first Bible. Movable type, for instance, had been
independently conceived by a Chinese blacksmith named Pi Sheng four
centuries before. But the Chinese (and, subsequently, the Koreans) failed to
adapt the technology for the mass production of texts, in large part because they
imprinted the letterforms on the page by hand rubbing, which made the process
only slightly more efficient than your average medieval scribe. Thanks to his
training as a goldsmith, Gutenberg made some brilliant modifications to the
metallurgy behind the movable type system, but without the press itself, his
meticulous lead fonts would have been useless for creating mass-produced
Bibles.
An important part of Gutenberg’s genius, then, lay not in conceiving an
entirely new technology from scratch, but instead from borrowing a mature
technology from an entirely different field, and putting it to work to solve an
unrelated problem. We don’t know exactly what chain of events led Gutenberg
to make that associative link; few documentary records remain of Gutenberg’s
life between 1440 and 1448, the period during which he assembled the primary
components of his invention. But it is clear that Gutenberg had no formal
experience pressing grapes. His radical breakthrough relied, instead, on the
ubiquity of the screw press in Rhineland wine-making culture, and on his ability
to reach out beyond his specific field of expertise and concoct new uses for an
older technology. He took a machine designed to get people drunk and turned it
into an engine for mass communication.
Evolutionary biologists have a word for this kind of borrowing, first proposed in
an influential 1971 essay by Stephen Jay Gould and Elisabeth Vrba: exaptation.
An organism develops a trait optimized for a specific use, but then the trait gets
hijacked for a completely different function. The classic example, featured
prominently in Gould and Vrba’s essay, is bird feathers, which we believe
initially evolved for temperature regulation, helping nonflying dinosaurs from
the Cretaceous period insulate themselves against cold weather. But when some
of their descendants, including a creature we now call Archaeopteryx, began
experimenting with flight, feathers turned out to be useful for controlling the
airflow over the surface of the wing, allowing those first birds to glide.
The initial transformation is almost accidental: a tool sculpted by evolutionary
pressures for one purpose turns out to have an unexpected property that helps the
organism survive in a new way. But once that new property gets put to use, once
Archaeopteryx starts using its feathers to glide, the trait evolves according to a
different set of criteria. All flight feathers, for instance, have pronounced
asymmetry to them: the vane on one side of the central shaft is larger than the
vane on the opposite side. This lets the feather act as a kind of airfoil, providing
lift during the flapping of wings. Birds that fly at unusually high velocities, like
hawks, have more extreme asymmetries than slower birds. Yet down feathers
that simply provide insulation are perfectly symmetrical. When your feathers are
there just to keep you warm, there’s no advantage to building slightly off-kilter
feathers. Mutations or other general variability in the gene pool inevitably
produces feathers that are slightly less symmetrical than average, but those traits
don’t intensify and spread across generations because they don’t convey any
reproductive advantage over normal feathers. But once flight speed becomes a
property with major implications for survival, those asymmetrical vanes turn out
to be extremely useful. Where asymmetry had previously drifted in and out of
the gene pool, natural selection now begins sculpting those feathers to make
them more aerodynamic. A feather adapted for warmth is now exapted for flight.
The concept of exaptation is crucial in rebutting the classic biblical argument
(now often termed “intelligent design”) against Darwinism, one that dates back
to the furor surrounding the publication of On the Origin of Species itself: if
extraordinary examples of natural engineering like eyes or wings are not the
product of an intelligent creator, then how could these traits have survived
through what must have been a pronounced developmental state of
nonfunctionality? As the wing evolves, by definition it has to go through a long
period where it’s utterly useless at flying. (As the saying goes: “What good is 5
percent of a wing?”) Because natural selection doesn’t “know” that it’s trying to
build a wing, it can’t push those emerging wings toward the ultimate goal of
flying the way a mechanical engineer can continue tinkering with a toy airplane
until it successfully takes to the air. If your aspiring wing doesn’t help you to fly,
and thus outmaneuver your predators or discover new sources of food, the new
mutations that made that appendage slightly more winglike won’t be more likely
to spread through the population. Natural selection doesn’t give good grades for
effort.
But when you think about evolutionary innovation in terms of exaptations, the
story becomes far less mysterious. Once again, chance and happy accidents are
central to the narrative: random mutations lead to the evolution of feathers
selected for warmth, and by chance those feathers turn out to be useful for
flying, particularly after they’ve been modified to create an airfoil. Sometimes
those exaptations become possible because other exaptations are happening
within the species: the wing itself is thought to be an exaptation of a dinosaur
wrist bone originally adapted for greater flexibility. When Gould offered up his
tires-to-sandals metaphor, he was essentially talking about the way in which
exaptations have defined the paths of evolutionary innovation: new abilities and
traits come about not because there is some inexorable march toward more and
more complexity in the biosphere, but rather because natural selection has the
Nairobi cobbler’s instinct for taking old parts and putting them to new uses.
Oftentimes those new uses become possible thanks to external changes in an
organism’s environment. When the lobe-finned fish Sarcopterygii first began
exploring life at the water’s edge, 400 million years ago, the creature had a small
swim fan at the end of its fin, supported by narrow rays of bone. As its
descendants began to spend more time away from the water, exploiting the
copious energy sources of the plants and arthropods that had already conquered
life onland, the tip of the lobe-fin turned out to be useful for an activity that
acquatic life had rendered unthinkable: walking. Before long, natural selection
had refashioned the swim fan into an autopod, the basic architecture of all
mammalian ankles and feet. Over time, the autopod itself would be exapted in
numerous ways: creating primate hands and fingers optimized for grasping, or
those Archaeopteryx wings. In some cases, the autopod was even exapted back
to its ancient swim-fan origins, as in the flippers of seals and sea lions.
If mutation and error and serendipity unlock new doors in the biosphere’s
adjacent possible, exaptations help us explore the new possibilities that lurk
behind those doors. A match you light to illuminate a darkened room turns out to
have a completely different use when you open a doorway and discover a room
with a pile of logs and a fireplace in it. A tool that helps you see in one context
ends up helping you keep warm in another. That’s the essence of exaptation.
It’s tempting to assume that the machinery of cultural innovation is closer to
that engineer tinkering with her model airplane than it is to the lucky
Archaeopteryx leaping off the treetop and discovering that its feathers are more
than just a down jacket. No one contests the role of intelligent design in the
history of human culture. But the history of human creativity abounds with
exaptations. In the early 1800s, a French weaver named Joseph-Marie Jacquard
developed the first punch cards to weave complex silk patterns with mechanical
looms. Several decades later, Charles Babbage borrowed Jacquard’s invention to
program the Analytical Engine. Punch cards would remain crucial to
programmable computers until the 1970s. Lee de Forest created the Audion with
one clear aim: to create a device that would detect electromagnetic signals and
amplify them. It never occurred to him that the triode architecture could just as
easily be applied to the problem of building a hydrogen bomb. In evolutionary
terms, the vacuum tube was originally adapted to make signals louder, but it was
eventually exapted to turn those signals into information: zeros and ones that
could be manipulated in astonishing ways. A Fender guitar amp from the fifties
that relied on a vacuum tube to boost the signal of the first rock-and-roll
guitarists was, ultimately, a variation on de Forest’s original amplification
theme. But those 17,000 vacuum tubes inside ENIAC, doing the math on the
physics of a hydrogen bomb—they were serving a function that never crossed de
Forest’s mind, however imaginative it might have been. Today, emerging patent
marketplaces like Nike’s GreenXchange are enabling commercial exaptations
that would have been unthinkable in the fortified environment of traditional
R&D labs.
The history of the World Wide Web is, in a sense, a story of continuous
exaptation. Tim Berners-Lee designs the original protocols with a specifically
academic environment in mind, creating a platform for sharing research in a
hypertext format. But when the first Web pages crawl out of that scholarly
primordial soup and begin to engage with ordinary consumers, Berners-Lee’s
invention turns out to possess a remarkable number of unanticipated qualities. A
platform adapted for scholarship was exapted for shopping, and sharing photos,
and watching pornography—along with a thousand other uses that would have
astounded Berners-Lee when he created his first HTML-based directories in the
early nineties. When Sergey Brin and Larry Page decided to use links between
Web pages as digital votes endorsing the content of those pages, they were
exapting Berners-Lee’s original design: they took a trait adapted for navigation
—the hypertext link—and used it as a vehicle for assessing quality. The result
was PageRank, the original algorithm that made Google into the behemoth that it
is today.
The literary historian Franco Moretti has persuasively documented the role of
exaptation in the evolution of the novel. An author conceives a new kind of
narrative device to address a specific, local need in a work he or she is writing.
Something about the device resonates with other authors, and it begins to
circulate through the literary gene pool. And then, as the literary environment
changes and new imaginative possibilities become necessary, the device turns
out to have a different function, far removed from its original use. The French
novelist Edouard Dujardin first uses the “stream of consciousness” technique in
his 1888 novel Les Lauriers sont coupés; in Dujardin’s rendition, the technique
is restricted to short periods of introspection between the main events of the
story, brief parentheses within the plot. But three decades later, James Joyce
would take the device and transform it into the most memorable and
mesmerizing perceptual modes, using the device in his novel Ulysses to capture
the churn and distractibility of mental life in a bustling city. When Dickens
conjured up his Inspector Bucket to weave together the multiplying strands of
metropolitan coincidence in Bleak House, he had no idea his contrivance would
help create a whole new genre of detective fiction, one that would extend all the
way from Wilkie Collins’s The Moonstone to Sherlock Holmes to Murder, She
Wrote. New genres need old devices.
Rhetorical or figurative exaptations are not the exclusive property of the arts.
The history of scientific and technological innovation abounds with them as
well. In The Act of Creation, Arthur Koestler argued that “all decisive events in
the history of scientific thought can be described in terms of mental crossfertilization between different disciplines.” Concepts from one domain migrate
to another as a kind of structuring metaphor, thereby unlocking some secret door
that had long been hidden from view. In his memoirs, Francis Crick reports that
he first hit upon the complementary replication system of DNA—each base A
matched with a T, and each C with a G—by thinking of the way a work of
sculpture can be reproduced by making an impression in plaster, and then using
that impression, when dry, as a mold to create copies. Johannes Kepler credited
his laws of planetary motion to a generative metaphor imported from religion; he
imagined the sun, stars, and the dark space between them as the celestial
equivalents of the Father, Son, and Holy Ghost. When computer science
pioneers like Doug Engelbart and Alan Kay invented the graphical interface,
they imported a metaphor from the real-world environment of offices: instead of
organizing information on the screen as a series of command-line inputs, the
way a programmer would, they borrowed the iconography of a desktop with
pieces of paper stacked on it. Kekulé didn’t think the benzene molecule was
literally a snake from Greek mythology, but his knowledge of that ancient
symbol helped him solve one of the essential problems of organic chemistry.
In the early 1970s, a Berkeley sociologist named Claude Fischer began
investigating the social effects of living in dense urban centers. The topic was
one that had long interested urban theorists, most famously in Louis Wirth’s
classic essay from 1938, “Urbanism as a Way of Life,” which argued that
metropolitan living led toward social disorganization and alienation, the social
ties and comforts of smaller communities breaking down in the tumult of the big
city. Wirth’s argument had not aged well—it turned out that densely populated
neighborhoods had very complex and rich social bonds if one looked for them—
and so Fischer set out to determine what social patterns were truly precipitated
by the environment of large cities. His research led him to one overwhelming
conclusion, published in a seminal paper in 1975: big cities nurture subcultures
much more effectively than suburbs or small towns.
Lifestyles or interests that deviate from the mainstream need critical mass to
survive; they atrophy in smaller communities not because those communities are
more repressive, but rather because the odds of finding like-minded people are
much lower with a smaller pool of individuals. If one-tenth of one percent of the
population are passionately interested in, say, beetle collecting or improv theater,
there might only be a dozen such individuals in a midsized town. But in a big
city there might be thousands. As Fischer noted, that clustering creates a positive
feedback loop, as the more unconventional residents of the suburbs or rural areas
migrate to the city in search of fellow travelers. “The theory . . . explains the
‘evil’ and ‘good’ of cities simultaneously,” Fischer wrote. “Criminal
unconventionality and innovative (e.g., artistic) unconventionality are both
nourished by vibrant subcultures.” Poetry collectives and street gangs might
seem miles apart on the surface, but they each depend on the city’s capacity for
nurturing subcultures.
The same pattern holds true for trades and businesses in large cities. As Jane
Jacobs observed in The Death and Life of Great American Cities: “The larger a
city, the greater the variety of its manufacturing, and also the greater both the
number and the proportion of its small manufacturers.”
Towns and suburbs, for instance, are natural homes for huge
supermarkets and for little else in the way of groceries, for standard
movie houses or drive-ins and for little else in the way of theater. There
are simply not enough people to support further variety, although there
may be people (too few of them) who would draw upon it were it there.
Cities, however, are the natural homes of supermarkets and standard
movie houses plus delicatessens, Viennese bakeries, foreign groceries,
art movies, and so on, all of which can be found co-existing, the standard
with the strange, the large with the small. Wherever lively and popular
parts of cities are found, the small much outnumber the large.
Both Fischer and Jacobs emphasize the fertile interactions that occur between
subcultures in a dense city center, the inevitable spillover that happens whenever
human beings crowd together in large groups. Subcultures and eclectic
businesses generate ideas, interests, and skills that inevitably diffuse through the
society, influencing other groups. As Fischer puts it, “The larger the town, the
more likely it is to contain, in meaningful numbers and unity, drug addicts,
radicals, intellectuals, ‘swingers,’ health-food faddists, or whatever; and the
more likely they are to influence (as well as offend) the conventional center of
the society.”
Cities, then, are environments that are ripe for exaptation, because they
cultivate specialized skills and interests, and they create a liquid network where
information can leak out of those subcultures, and influence their neighbors in
surprising ways. This is one explanation for superlinear scaling in urban
creativity. The cultural diversity those subcultures create is valuable not just
because it makes urban life less boring. The value also lies in the unlikely
migrations that happen between the different clusters. A world where a diverse
mix of distinct professions and passions overlap is a world where exaptations
thrive.
Those shared environments often take the form of a real-world public space,
what the sociologist Ray Oldenburg famously called the “third place,” a
connective environment distinct from the more insular world of home or office.
The eighteenth-century English coffeehouse fertilized countless Enlightenmentera innovations; everything from the science of electricity, to the insurance
industry, to democracy itself. Freud maintained a celebrated salon Wednesday
nights at 19 Berggasse in Vienna, where physicians, philosophers, and scientists
gathered to help shape the emerging field of psychoanalysis. Think, too, of the
Paris cafés where so much of modernism was born; or the legendary Homebrew
Computer Club in the 1970s, where a ragtag assemblage of amateur hobbyists,
teenagers, digital entrepreneurs, and academic scientists managed to spark the
personal computer revolution. Participants flock to these spaces partly for the
camaraderie of others who share their passions, and no doubt that support
network increases the engagement and productivity of the group. But
encouragement does not necessarily lead to creativity. Collisions do—the
collisions that happen when different fields of expertise converge in some shared
physical or intellectual space. That’s where the true sparks fly. The modernism
of the 1920s exhibited so much cultural innovation in such a short period of time
because the writers, poets, artists, and architects were all rubbing shoulders at
the same cafés. They weren’t off on separate islands, teaching creative writing
seminars or doing design reviews. That physical proximity made the space rich
with exaptation: the literary stream of consciousness influencing the dizzying
new perspectives of cubism; the futurist embrace of technological speed in
poetry shaping new patterns of urban planning.
Exaptation also prospers on another scale: the shared media environment of a
physical community. In the late 1970s, the British musician and artist Brian Eno
moved to New York City for the first time. He took over a flat in a converted
town house in the heart of the Village. The city was at the height—or more like
the nadir—of its rioting, Son of Sam-fearing, bankruptcy-flirting madness. Still,
having spent time in 1970s London and Berlin, Eno was well acclimated to
urban anarchy. In fact, the most jarring contrast to his European past was the
turbulent mix of voices on the radio. After years of listening to the somber,
professional voices of the BBC, the outlandish rants of American radio seemed
to Eno like a new universe of insanity.
And so he started taping them. Like many experimental musicians at that
point, Eno had been exploring the possibilities of using tape loops as a musical
instrument. (“The tape recorder was always the instrument I felt most
comfortable with,” he once said in an interview. “Keyboards after that, with bass
as a distant third.”) The Beatles had reserved the longest track on the White
Album for Lennon’s tape-loop collage “Revolution #9,” and the protosynthesizer Mellotron, developed in the mid-sixties, had separate tape loops set
up to be triggered by individual keys on the keyboard. But none of those
experiments had ever really employed the spoken voice as a harmonic or
percussive element. The drones and murmurs of “Revolution #9” were, after all,
barely musical by traditional standards. But Eno’s hours with the evangelists and
the anarchists and the shock-jocks-in-embryo had lodged those voices in his
head, and as he began work on a collaboration with David Byrne, he started to
toy with the idea of exploring their musical possibilities. The result was My Life
in the Bush of Ghosts, an utterly original mix of African rhythm sections and
oddball acoustic instruments, but notably missing Byrne’s taut New Wave vocal
stylings—so prominently featured in the Talking Heads albums the two had
previously collaborated on. Instead of traditional singing, Byrne and Eno built
the songs around the layered, looped ensemble of spoken words that Eno had
grabbed from the airwaves. It was a case study in creative exaptation: words
designed in one medium to spread the word of Jesus, or to thunder against the
military-industrial complex, migrated over into a new environment and became,
against all odds, music.
My Life in the Bush of Ghosts marked the birth of a certain historically crucial
kind of musical borrowing: it was not just a new music, but a whole new way of
thinking about what music could be built out of. (Not unlike the way Marcel
Duchamp and his fellow surrealists had changed our understanding of what art
could be made of fifty years before.) Several years later, when Public Enemy
producer Hank Shocklee sat down to record the album It Takes a Nation of
Millions to Hold Us Back, he deliberately mimicked the layered, percussive
vocal samples of Eno and Byrne’s production. It Takes a Nation went on to
become one of the most sonically influential records of its decade, reverberating
through the wider culture—in everything from cell phone jingles to billboard
chart-toppers to avant-garde experimentation—just as Highway 61 Revisited and
Pet Sounds had done a generation before. Eno’s original innovation was
brilliant, to be sure, and from a distance it almost looks like the classic “lone
genius” eureka moment: the innovator locked away in his lab, stumbling across
an idea that would transform the wider culture. But it is crucial to the story that
Eno was not, technically speaking, alone with his tape recorder: he was tapped
into a network of wildly different voices, all of them ranting at different
frequencies. Eno didn’t need a coffeehouse. He had AM radio.
In the late nineties, a Stanford Business School professor named Martin Ruef
decided to investigate the relationship between business innovation and
diversity. Ruef was interested in the coffeehouse model of diversity, not the
“melting pot” political kind: the diversity of professions and disciplines, not of
race or sexual orientation. Ruef interviewed 766 graduates of the school who had
gone on to have entrepreneurial careers. He created an elaborate system for
scoring innovation based on a combination of factors: the introduction of new
products, say, or the filing of trademarks and patents. And then he tracked each
graduate’s social network—not just the number of acquaintances but the kind of
acquaintances they had. Some graduates had large social networks that were
clustered within their organization; others had small insular groups dominated by
friends and family. Some had wide-ranging connections with acquaintances
outside their inner circle of friends and colleagues.
What Ruef discovered was a ringing endorsement of the coffeehouse model of
social networking: the most creative individuals in Ruef’s survey consistently
had broad social networks that extended outside their organization and involved
people from diverse fields of expertise. Diverse, horizontal social networks, in
Ruef’s analysis, were three times more innovative than uniform, vertical
networks. In groups united by shared values and long-term familiarity,
conformity and convention tended to dampen any potential creative sparks. The
limited reach of the network meant that interesting concepts from the outside
rarely entered the entrepreneur’s consciousness. But the entrepreneurs who built
bridges outside their “islands,” as Ruef called them, were able to borrow or coopt new ideas from these external environments and put them to use in a new
context. A similar study, conducted by a University of Chicago business school
professor named Ronald Burt, looked at the origin of good ideas inside the
organizational network of the Raytheon Corporation. Burt found that innovative
thinking was much more likely to emerge from individuals who bridged
“structural holes” between tightly knit clusters. Employees who primarily shared
information with people in their own division had a harder time coming up with
useful suggestions for Raytheon’s business, when measured against employees
who maintained active links to a more diverse group.
To a certain extent, Ruef’s and Burt’s research is a validation of the celebrated
“strength of weak ties” argument first proposed by Mark Granovetter, and
popularized by Malcolm Gladwell in The Tipping Point. But looking at the weak
ties of an extended social network through the lens of exaptation changes the
picture in an important way: it is not merely that weak ties allow information to
travel throughout a network more efficiently—that is, without becoming trapped
on the remote island of a close-knit group. From the perspective of innovation,
it’s even more important that the information arriving from one of those weak
ties is coming from a different context, what the innovation scholar Richard
Ogle calls an “idea-space”: a complex of tools, beliefs, metaphors, and objects of
study. A new technology developed in one idea-space can migrate over to
another idea-space through these long-distance connections; in that new
environment, the technology may turn out to have unanticipated properties, or
may trigger a connection that leads to a new breakthrough. The value of the
weak tie lies not just in the speed with which it transmits information across a
network; it also promotes the exaptation of those ideas. Gutenberg was trained as
a metallurgist, but he had weak ties to the vintners of Rhineland Germany.
Without that link, he would have been merely a pioneering typesetter, making an
incremental improvement on Pi Sheng’s movable type. By not restricting
himself exclusively to the island of metallurgy, he became something much
more important: a printer.
The model of weak-tie exaptation also helps us understand the classic story of
twentieth-century scientific epiphany: Watson and Crick’s discovery of the
double-helix structure of DNA. As Ogle and others have noted, in the small
scientific community working on the problem of DNA in the early 1950s, the
person who had the clearest and most direct view of the molecule itself was
neither James Watson nor Francis Crick. It was, instead, a biophysicist at
London University named Rosalind Franklin, who was using state-of-the-art Xray crystallography to study the mysterious strands of DNA. But Franklin’s
vision was limited by two factors. First, there was the imperfect state of the Xray technology, which only gave her hints about the helix structure and base-pair
symmetry. But Franklin was also limited by the conceptual island on which she
based her work. Her approach was purely inductive: master the X-ray
technology and then use the information collected to build a model of DNA.
(“We’re going to let the data tell us the structure,” she famously told Crick.) But
to “see” the double-helix in the early 1950s took something more than just
analyzing it in an X-ray machine. To solve the mystery, Watson and Crick had to
piece it together with tools drawn from multiple disciplines: biochemistry,
genetics, information theory, and mathematics, not to mention Franklin’s X-ray
images. Even Crick’s sculpture metaphor proved crucial to cracking the code.
Next to Franklin, Watson and Crick seemed almost dilettantes and dabblers:
Crick had switched from physics to biology in his graduate years; neither had a
comprehensive grasp of biochemistry. But DNA was not a problem that could be
solved within a single discipline. Watson and Crick had to borrow from other
domains to make sense of the molecule. As Ogle puts it, “Once key ideas from
idea-spaces that otherwise had little contact with one another were connected,
they began, quasi-autonomously, to make new sense in terms of one another,
leading to the emergence of a whole that was more than the sum of its parts.” It
is a fitting footnote to the story that Watson and Crick were notorious for taking
long, rambling coffee breaks, where they tossed around ideas in a more playful
setting outside the lab—a practice that was generally scorned by their more
fastidious colleagues. With their weak-tie connections to disparate fields, and
their exaptative intelligence, Watson and Crick worked their way to a Nobel
Prize in their own private coffeehouse.
The coffeehouse model of creativity helps explain one of those strange
paradoxes of twenty-first-century business innovation. Even as much of the
high-tech culture has embraced decentralized, liquid networks in their approach
to innovation, the company that is consistently ranked as the most innovative in
the world—Apple—remains defiantly top-down and almost comically secretive
in its development of new products. You won’t ever see Steve Jobs or Jonathan
Ive crowdsourcing development of the next-generation iPhone. If open and
dense networks lead to more innovation, how can we explain Apple, which on
the spectrum of openness is far closer to Willy Wonka’s factory than it is to
Wikipedia? The easy answer is that Jobs and Ive simply possess a collaborative
genius that has enabled the company to ship such a reliable stream of
revolutionary products. No doubt both men are immensely talented at what they
do, but neither of them can design, build, program, and market a product as
complex as the iPhone on their own, the way Jobs and Steve Wozniak crafted
the original Apple personal computer in the now-legendary garage. Apple
clearly has unparalleled leadership, but there must also be something in the
environment at Apple that is allowing such revolutionary ideas to make it to the
marketplace.
As it turns out, while Apple has largely adopted a fortress mentality toward
the outside world, the company’s internal development process is explicitly
structured to facilitate clash and connection between different perspectives. Jobs
himself has taken to describing their method via the allegory of the concept car.
You go to an auto show and see some glamorous and wildly innovative concept
car on display and you think, “I’d buy that in a second.” And then five years
later, the car finally comes to market and it’s been whittled down from a Ferrari
to a Pinto—all the truly breakthrough features have been toned down or
eliminated altogether, and what’s left looks mostly like last year’s model. The
same sorry fate could have befallen the iPod as well: Ive and Jobs could have
sketched out a brilliant, revolutionary music player and then two years later
released a dud. What kept the spark alive?
The answer is that Apple’s development cycle looks more like a coffeehouse
than an assembly line. The traditional way to build a product like the iPod is to
follow a linear chain of expertise. The designers come up with a basic look and
feature set and then pass it on to the engineers, who figure out how to actually
make it work. And then it gets passed along to the manufacturing folks, who
figure out how to build it in large numbers—after which it gets sent to the
marketing and sales people, who figure out how to persuade people to buy it.
This model is so ubiquitous because it performs well in situations where
efficiency is key, but it tends to have disastrous effects on creativity, because the
original idea gets chipped away at each step in the chain. The engineering team
takes a look at the original design and says, “Well, we can’t really do that—but
we can do 80 percent of what you want.” And then the manufacturing team says,
“Sure, we can do some of that.” In the end, the original design has been watered
down beyond recognition.
Apple’s approach, by contrast, is messier and more chaotic at the beginning,
but it avoids this chronic problem of good ideas being hollowed out as they
progress through the development chain. Apple calls it concurrent or parallel
production. All the groups—design, manufacturing, engineering, sales—meet
continuously through the product-development cycle, brainstorming, trading
ideas and solutions, strategizing over the most pressing issues, and generally
keeping the conversation open to a diverse group of perspectives. The process is
noisy and involves far more open-ended and contentious meetings than
traditional production cycles—and far more dialogue between people versed in
different disciplines, with all the translation difficulties that creates. But the
results speak for themselves.
Many of history’s great innovators managed to build a cross-disciplinary
coffeehouse environment within their own private work routines. It is an oft-told
story that Darwin delayed publishing his theory of evolution because he feared
the controversy it would unleash, particularly after the death of his beloved
daughter Annie traumatized his religious wife, Emma. But Darwin also had an
immense number of side interests to distract him from his opus: he studied coral
reefs, bred pigeons, performed elaborate taxonomical studies of beetles and
barnacles, wrote important papers on the geology of South America, spent years
researching the impact of earthworms on the soil. None of these passions were
central to the argument that would eventually be published as On the Origin of
Species, but each contributed useful links of association and expertise to the
problem of evolution. The same eclectic pattern appears in countless other
biographies. Joseph Priestley bounced between chemistry, physics, theology, and
political theory. Even in the years before he became a political statesman,
Benjamin Franklin conducted electricity experiments, theorized the existence of
the Gulf Stream, designed stoves, and of course made a small fortune as a
printer. While John Snow was solving the mystery of cholera in the streets of
London in the 1850s, he was also inventing state-of-the-art technology for the
administration of ether, publishing research on lead poisoning and the
resuscitation of stillborn children, yet all the while tending to his patients as a
general practitioner. Legendary innovators like Franklin, Snow, and Darwin all
possess some common intellectual qualities—a certain quickness of mind,
unbounded curiosity—but they also share one other defining attribute. They
have a lot of hobbies.
The historian Howard Gruber likes to call such concurrent projects “networks
of enterprise,” but I prefer to describe them using a contemporary term that has
been much maligned of late: multitasking. This is not, of course, the
multitasking of the modern computer screen: switching from e-mail to
spreadsheet to Twitter in a matter of seconds. What I’m describing is much more
leisurely than that frenetic, digital-age mode; the individual tasks themselves
might linger on for days or weeks before giving way to the next project. But
there is steady variation nonetheless, not just in the subject matter but in the kind
of work performed in each task. For John Snow, there were fundamentally
different modes of intellectual activity involved in his many projects: building
mechanical contraptions to control the temperature of chloroform required
different skills and a different mind-set from tending to patients or writing
papers for The Lancet. It is tempting to call this mode of work “serial tasking,”
in the sense that the projects rotate one after the other, but emphasizing the serial
nature of the work obscures one crucial aspect of this mental environment: in a
slow multitasking mode, one project takes center stage for a series of hours or
days, yet the other projects linger in the margins of consciousness throughout.
That cognitive overlap is what makes this mode so innovative. The current
project can exapt ideas from the projects at the margins, make new connections.
It is not so much a question of thinking outside the box, as it is allowing the
mind to move through multiple boxes. That movement from box to box forces
the mind to approach intellectual roadblocks from new angles, or to borrow tools
from one discipline to solve problems in another.
The standard story about Snow is that he solved the mystery of cholera’s
waterborne transmission by doing shoe-leather epidemiological detective work
during the 1854 Soho outbreak, but the truth is he had built a convincing
rendition of the waterborne theory well before 1854. One reason he was able to
see around the biases of the reigning “miasma” theory of the day—which
maintained that cholera was caused by the inhalation of noxious vapors—is that
his work with anesthesia had given him a hands-on knowledge of the way that
gases diffused through the atmosphere. Snow reasoned that a disease transmitted
by poisonous gas would leave a distinct pattern in the geographic spread of
mortality: massive death in the immediate proximity of the bad smells, tapering
off very quickly as one moved away from the original source. By the same
token, Snow’s training as a physician helped him shed the miasma blinders as
well: from tending to patients ill with cholera, Snow observed that the effects of
the disease on the human body indicated that the agent had been ingested, not
inhaled, given that it did almost all of its direct damage in the digestive system
and left the lungs largely unaffected. In a real sense, for Snow to make his great
breakthrough in understanding cholera, he had to think like a molecular chemist
and like a physician. As a slow multitasker, he had those interpretative systems
readily available to him when his focus turned to the mystery of cholera. As we
saw with the feathers of Archaeopteryx, Snow couldn’t have anticipated that his
mechanical tinkering with chloroform inhalers would prove useful in ridding the
modern world of a deadly bacterium, but that is the unpredictable power of
exaptations. Chance favors the connected mind.
VII.
PLATFORMS
On April 12, 1836, HMS Beagle took leave of the Keeling Islands, after a twoweek idyll that had given Darwin the crucial evidence he needed to support the
first great idea of his young career. As the ship left the placid green waters of the
lagoon, heading home to England via the island of Mauritius, Captain FitzRoy
plumbed the depths on the periphery of the atoll with a line more than 7,000 feet
long. He encountered no bottom. FitzRoy’s measurements confirmed, in
Darwin’s words, that the “island forms a lofty submarine mountain, with sides
steeper even than those of the most abrupt volcanic cone.” The data was crucial
to Darwin, because he was building a theory in his mind about “lofty submarine
mountains” and their geological legacy.
The theory had emerged years before as a hunch: that his mentor Charles
Lyell’s theory of atoll formation had a critical flaw that revolved around the
statistical likelihood that a mountain would just happen to settle only a few feet
above sea level. The elevation variation in volcanic islands was immense: some
tapered off a dozen feet above sea level; others, like Mauna Kea, surged ten
thousand feet into the sky. Most volcanic peaks lay thousands of feet below the
surface. Yet Darwin, like most geologists of his age, knew that the oceans were
populated by a huge number of tropical atolls that had all somehow
simultaneously landed within a few feet of sea level. It was like scattering a
hundred footballs across a field and having twenty of them cluster exactly on the
forty-three-yard line. Darwin didn’t have the theory of plate tectonics, but he
knew that landmasses were rising and descending around the world. But it made
no sense that these epic forces were somehow being arrested, in a significant
number of cases, by the dividing line of sea level. A volcano being pushed
upward by immense planetary conveyor belts should, by all rights, quickly burst
through the ocean’s surface and continue climbing, as Mauna Kea and countless
other island volcanoes did. By the same logic, a mountain sliding into the sea
should keep sliding. Why were so many of these mountains getting stuck?
We don’t know exactly when the answer came to Darwin. It may well have
occurred to him standing on the white sands of a Keeling Islands beach. More
likely, knowing Darwin, the idea rolled in slowly, inch by inch, and some small
piece of it came to him standing in those green waters. The idea was simple, but
strangely hard to visualize. It began with one defining principle: the ground
beneath Darwin’s feet was not the product of geological forces. An organism
had engineered it.
That organism was Scleractinia, more commonly known as reef-building
coral. Alive, an individual Scleractinia is a soft polyp, no more than a few
millimeters long. Reef-building corals grow in vast colonies, with new polyps
appearing as buds on the sides of their “parents.” It is one of the strange ironies
of marine biology that the coral’s essential contribution to the undersea
ecosystem takes place after its death. The polyp builds a calcium-based
exoskeleton during its life, producing a mineral called aragonite, which is sturdy
enough to remain intact centuries after its original host has perished. A coral
reef, then, is a kind of vast underwater mausoleum: millions of skeletons united
to form the pocked, labyrinthine sprawl of a reef.
During his fortnight on the Keeling Islands, Darwin had observed that the soil
of the island was entirely devoid of traditional rocks. As he put it in his diary,
“Throughout the whole group of Islands, every single atom, even from the most
minute particle to large fragments of rocks, bear [sic] the stamp of once having
been subjected to the power of organic arrangement.” The vast majority of those
particles and rocks were aragonite skeletons, the remains of a coral polyp that
had died decades or centuries before. This alone was evidence that Lyell’s
theory was flawed: if Darwin was standing at the tip of a dormant undersea
volcano, the rocks at his feet would have been basalt or obsidian or pumice,
rocks created from the cooling of molten lava. The rocks would have been
forged in a fiery core of magma, not excreted by minuscule polyps.
The fact that the soil of an Indian Ocean atoll was organic in nature,
engineered by coral and not the product of volcanic activity, did not, on its own,
offer a satisfactory answer to the mystery of the atoll’s existence. Why should a
colony of coral form such a perfect oval in the middle of an immense ocean,
hundreds of miles from another landmass? To solve that mystery, Darwin drew
on Lyell’s original theory, but he added an essential twist. He turned a still frame
into a moving picture. To understand atoll formation, Darwin realized, you had
to imagine a volcanic island slowly subsiding into the sea. As the banks of the
volcano disappeared beneath the ocean waves, those slopes would become prime
breeding ground for coral colonies, which thrive in shallow water at depths up to
around 150 feet. (Their diet relies substantially on photosynthetic algae that
cannot survive too far from the sunlit surface of the water.) Eventually the
summit of the mountain slides into the sea, leaving a circle of shallow water
defined by the periphery of the volcanic crater. Because the mountain is
subsiding so slowly, the coral are able to build their reefs faster than the
mountain can descend. Like overzealous developers, the coral colonies keep
adding new floors to the structure they’ve erected at the top of the volcano,
limited only by the water’s surface. As the original peak descends further and
further into the sea, the older reefs die off, but continue to give structural support
to the new, thriving reefs above them. Darwin had no way of measuring this
precisely, but he predicted that fossil coral would extend as far as five thousand
feet below sea level before hitting a volcanic foundation, a number that was
confirmed more than a century later with modern drilling technology.
As the Beagle departed, Darwin captured the miraculous nature of this
explanation in his diary. “We must look at a Lagoon [island] as a monument
raised by myriads of tiny architects,” he wrote, “to mark the spot where a former
land lies buried in the depths of the ocean.”
Published several years later as a monograph, Darwin’s theory of atoll
formation marked his first significant contribution to science, and it has largely
stood the test of time. The idea itself drew on a coffeehouse of different
disciplines: to solve the mystery, he had to think like a naturalist, a marine
biologist, and a geologist all at once. He had to understand the life cycle of coral
colonies, and observe the tiny evidence of organic sculpture on the rocks of the
Keeling Islands; he had to think on the immense time scales of volcanic
mountains rising and falling into the sea. And, of course, he needed FitzRoy’s
technical expertise with the sounding line. To understand the idea in its full
complexity required a kind of probing intelligence, willing to think across those
different disciplines and scales. Darwin described it best in the chapter on his
Keeling Islands investigations from The Voyage of The Beagle: “We feel
surprise when travelers tell us of the vast dimensions of the Pyramids and other
great ruins, but how utterly insignificant are the greatest of these, when
compared to these mountains of stone accumulated by the agency of various
minute and tender animals. This is a wonder which does not at first strike the eye
of the body, but, after reflection, the eye of reason.”
From Darwin’s perspective, those “minute and tender animals” had built a
platform, in the most prosaic sense of the word. Darwin was walking on that
saucer-shaped summit, and not treading water in the middle of the Indian Ocean,
because those animals had engineered a platform for him to stand on. But a coral
reef is a platform in a much more profound sense: the mounds, plates, and
crevices of the reef create a habitat for millions of other species, an undersea
metropolis of immense diversity. To date, attempts to measure accurately the full
diversity of reef ecosystems have been foiled by the complexity of these
habitats; scientists now believe that somewhere between a million and ten
million distinct species live in coral reefs around the world, despite the fact that
those reefs only occupy one-tenth of one percent of the planet’s surface. This is
the Darwin Paradox: that such nutrient-poor waters could generate so much
marvelous, improbable, heterogeneous life.
For forty years, ecologists have used the term “keystone species” to designate
an organism that has a disproportionate impact on its ecosystem—a carnivore,
for instance, who is the only predator of another species that would otherwise
overwhelm the habitat with unchecked population growth. Remove the keystone
predator and the habitat falls apart. But about twenty years ago, a scientist
named Clive Jones at the Cary Institute of Ecosystem Studies decided that
ecology needed another term to describe a very specific kind of keystone
species: the kind that actually creates the habitat itself. Jones called these
organisms “ecosystem engineers.” Beavers are the classic example of ecosystem
engineers. By felling poplars and willows to build dams, beavers singlehandedly transform temperate forests into wetlands, which then attract and
support a remarkable array of neighbors: pileated woodpeckers drilling nesting
cavities into dead trees; wood ducks and Canada geese settling in abandoned
beaver lodges; herons and kingfishers and swallows enjoying the benefits of the
“artificial” pond, along with frogs, lizards, and other slow-water species like
dragonflies, mussels, and aquatic beetles. As do those underwater colonies of
coral, the beaver creates a platform that sustains an amazingly diverse
assemblage of life.
Platform building is, by definition, a kind of exercise in emergent behavior.
The tiny Scleractinia polyp isn’t actively trying to create an underwater Las
Vegas, but nonetheless out of its steady labor—imbibing algae and erecting
those aragonite skeletons—a higher-level system emerges. What had been a
largely desolate stretch of nutrient-poor seawater is transformed into a glittering
hub of activity. The beaver builds a dam to better protect itself against its
predators, but that engineering has the emergent effect of creating a space where
kingfishers and dragonflies and beetles can make a life for themselves. The
platform builders and ecosystem engineers do not just open a door in the
adjacent possible. They build an entire new floor.
The cafeteria at the Johns Hopkins University Applied Physics Laboratory in
Laurel, Maryland, had long been a site of productive shoptalk between the
physicists, technicians, mathematicians, and proto-hackers who worked there.
But the Monday lunchtime chatter on October 7, 1957, was unusually heated,
thanks to the weekend headlines announcing the Soviet launch of Sputnik 1, the
first man-made earth-orbiting satellite. Two young physicists, William Guier and
George Weiffenbach, found themselves in a spirited discussion about the
microwave signals that would likely be emanating from Sputnik. After
canvassing some of their colleagues, it appeared that no one had bothered to
come in over the weekend to see if Sputnik ’s signals could be picked up by the
APL’s equipment. Weiffenbach, as it turned out, was in the middle of a Ph.D. on
microwave spectroscopy and had a 20 MHz receiver sitting in his office.
Guier and Weiffenbach spent the afternoon hunched over the receiver,
listening for Sputnik’s audio fingerprint. To combat the doubters, who would
inevitably question whether the whole launch was an elaborate hoax, a product
of communist propaganda, the Soviets had engineered Sputnik so that it would
transmit an unusually accessible signal: an unbroken tone broadcast within 1
kHz of 20 MHz. By the end of the afternoon, Weiffenbach and Guier had a clear
lock on it. The sound itself was a staccato pulse of electronic bleeps, but the
context transformed it into the most marvelous music the two men had ever
heard. It seemed unbelievable: sitting in a room in suburban Maryland, listening
to man-made signals coming from space. Word began to spread through the APL
that the young physicists had captured Sputnik’s signal, and a steady stream of
visitors appeared at Weiffenbach’s door to eavesdrop on the satellite’s warble.
Realizing that they were listening to history, Guier and Weiffenbach hooked
up the receiver to an audio amplifier and began recording the signal on
audiotape. They included time stamps with each recording. As they listened and
recorded, the two men realized that they could use the Doppler effect to calculate
the speed at which the satellite was moving through space. First observed more
than a century before by the Austrian physicist Christian Doppler, the Doppler
effect describes the predictable way a waveform’s frequency changes when the
source or the receiver is in motion. Imagine a speaker playing a single note, let’s
say the A above middle C, which sends out sound waves with a frequency of 440
Hz. If you mount the speaker on the hood of a car and have it driven toward you,
the waves stack up on top of each other, making the interval between each of
them shorter. When those compressed waves arrive in your eardrum, their
perceived frequency is higher than 440 Hz. When the car backs up, the Doppler
effect reverses, and the perceived note drops below A. You can hear the Doppler
effect at work every time an ambulance drives past you with blaring sirens; as it
passes you by, the sound of its siren appears to slide down in pitch.
The Doppler effect has proved to be a remarkably versatile concept: it has
been used to detect the expansion of the universe, to track thunderstorms, and to
perform ultrasounds. Because Sputnik was emitting a signal at a steady
frequency, and because the microwave receiver was stationary, Guier and
Weiffenbach realized that they could calculate the movement of the satellite
based on the small but steady changes in the waveform they were capturing. Late
that night, they remembered an additional mathematical trick: by analyzing the
slope of the Doppler shift, they could determine the point in Sputnik’s orbit that
was closest to the APL laboratories. Almost by accident, they had hit upon a
technique not just for calculating the satellite’s speed, but for actually mapping
the trajectory of its orbit. In a matter of hours, the two young scientists had gone
from listening to measuring to tracking the Russian satellite.
Over the subsequent weeks, a loose network of scientists at APL coalesced
around Guier and Weiffenbach’s hunch, filling in details, researching the
theoretical literature on orbiting bodies, and proposing technology
improvements. Eventually, the APL’s director approved funds to run the
numbers on the lab’s new UNIVAC computer. Within a few months of that first
transmission, they had a complete description of Sputnik’s orbit, inferred entirely
from that simple 20 MHz signal. Guier and Weiffenbach had embarked on a
quest that would define their professional careers, the “adventure of their lives,”
as they later called it. In the spring of 1958, Frank T. McClure, the legendary
deputy director of the Applied Physics Laboratory, called Guier and
Weiffenbach into his office. McLure had a confidential question to ask the men:
If you could use the known location of a receiver on the ground to calculate the
location of a satellite, McClure asked, could you reverse the problem? Could you
calculate the location of a receiver on the ground if you knew the exact orbit of
the satellite? Guier and Weiffenbach ran the logic through their heads for a few
minutes, and then answered in the affirmative. In fact, deducing the location
from a known orbit—instead of a stationary ground position—would make the
results significantly more accurate. Without explaining his ultimate interest in
the question, McClure told the two men to run a quick feasibility analysis. After
a few furious days of crunching the numbers, Guier and Weiffenbach reported
back: the “inverse problem,” as they called it, was eminently solvable.
Soon, Guier and Weiffenbach would learn why the inverse problem was so
important to McClure: the military was developing its Polaris nuclear missiles,
designed to be launched from submarines. Calculating accurate trajectories for a
missile attack required precise knowledge of the launch site’s location. This was
easy enough to determine on land—say, for a missile silo in Alaska—but it was
fiendishly difficult in the case of a submarine floating somewhere in the Pacific
Ocean. McClure’s idea was to take the ingenious Sputnik solution and flip it on
its head. The military would establish the unknown location of its submarines by
tracking the known location of satellites orbiting above the earth. Just as sailors
had used the stars to navigate for thousands of years, the military would steer its
ships using the artificial stars of satellite technology.
The project was dubbed the Transit system. Just three years after Sputnik’s
launch, there were five U.S. satellites in orbit, providing navigational data to the
military. When Korean Air Lines Flight 007 was shot down in 1983 after drifting
into Soviet airspace thanks to faulty, ground-based navigation beacons, Ronald
Reagan declared that satellite-based navigation should be a “common good”
open to civilian use. Around that time, the system took on its current name:
Global Positioning System, or GPS. Half a century later, roughly thirty GPS
satellites blanket the earth with navigational signals, providing guidance for
everything from mobile phones to digital cameras to Airbus A380s.
If you wish to see firsthand the unpredictable power of an emergent platform,
you need only look at what has happened to GPS over the past five years. The
engineers that built the system—starting with Guier and Weiffenbach—created
an entire ecosystem of unexpected utility. Frank McClure recognized that you
could harness Guier and Weiffenbach’s original insight to track nuclear
submarines, but he had no inkling that fifty years later the same system would
help teenagers to play elaborate games in urban centers, or climbers to explore
treacherous mountain ranges, or photographers to upload their photos to Flickr
maps. Like the Internet itself, GPS has turned out to have immense commercial
value, and many for-profit firms were involved in building out the infrastructure
that made it a reality. But the ideas at the foundation of GPS—the notion of a
satellite itself, the atomic clocks satellites rely on for accurate timing, and, of
course, Guier and Weiffenbach’s original insight with Sputnik—all came out of
the public sector. The generative nature of the GPS platform nicely mirrors the
original environment that gave birth to it. When Guier and Weiffenbach were
asked to explain how they had hit upon their Sputnik revelation, they credited the
intellectual habitat of the Applied Physics Lab more than their own particular
talents:
APL was a superb environment for inquisitive young kids, and
particularly so in the Research Center. It was an environment that
encouraged people to think broadly and generally about task problems,
and one in which inquisitive kids felt free to follow their curiosity.
Equally important, it was an environment wherein kids, with an initial
success, could turn to colleagues who were broadly expert in relevant
fields, and particularly because of the genius of the Laboratory
Directorship, colleagues who were also knowledgeable about hardware,
weapons, and weapons needs.
In its own small way, the APL was a platform that encouraged and amplified
hunches, that allowed those hunches to be connected with other minds that had
relevant expertise. Out of that dense network, one of the most generative
technological platforms of the twenty-first century took root. The APL was not a
purely open platform, of course. There were military secrets involved, after all;
and even if Guier and Weiffenbach had wanted to share their Sputnik discovery
with the world, it was much harder to distribute that breakthrough in an age
when the hot new computer—the UNIVAC—took up an entire room. But behind
those closed doors, William Guier and George Weiffenbach were the
beneficiaries of an environment that encouraged the chance collisions between
different fields, an environment that let two “kids” stumble across an idea at the
cafeteria and build an entire career around it.
Most hotbeds of innovation have similar physical spaces associated with
them: the Homebrew Computing Club in Silicon Valley; Freud’s Wednesday
salon at 19 Berggasse; the eighteenth-century English coffeehouse. All these
spaces were, in their own smaller-scale fashion, emergent platforms.
Coffeehouse proprietors like Edward Lloyd or William Unwin were not trying to
invent the modern publishing industry or the insurance business; they weren’t at
all interested in fostering scientific advancement or political turmoil. They were
just businessmen, trying to make enough sterling to feed their families, just like
those beavers constructing lodges to keep their offspring safe. But the spaces
Lloyd and Unwin built turned out to have these unusual properties: they made
people think differently, because they created an environment where different
kinds of thoughts could productively collide and recombine.
The most generative platforms come in stacks, most conspicuously in the layered
platform of the Web. (The phrase “platform stack” itself is part of the common
parlance of modern programming.) The Web can be imagined as a kind of
archaeological site, with layers upon layers of platforms buried beneath every
page. Tim Berners-Lee was able to single-handedly design a new medium
because he could freely build on top of the open protocols of the Internet
platform. He didn’t have to engineer an entire system for communicating
between computers spread across the planet; that problem had been solved
decades before. All he had to do was build a standard framework for describing
hypertext pages (HTML) and sharing them via existing Internet channels
(HTTP). Even HTML was based on another existing platform, SGML, which
had been developed at IBM in the 1960s. Fourteen years later, when Hurley,
Chen, and Karim sat down to create YouTube, they built the service by stitching
together elements from three different platforms: the Web itself, of course, but
also Adobe’s Flash platform, which handled all the video playback, and the
programming language Javascript, which allowed end users to embed video clips
on their own sites. Their ability to build on top of these existing platforms
explains why three guys could build YouTube in six months, while an army of
expert committees and electronics companies took twenty years to make HDTV
a reality.
Culture, too, relies on stacked platforms of information. Kuhn’s paradigms of
research are the scientific world’s equivalent of a software platform: a set of
rules and conventions that govern the definition of terms, the collection of data,
and the boundaries of inquiry for a particular field. Kuhn’s argument has often
been mistaken as a defense of a purely relativistic account of science, where
empirical “truth” is always in quotation marks because paradigms replace each
other over time. (The apparent solidity of scientific truth, in this account, is
merely a kind of hologram produced by the apparatus of the paradigm.) But
modern scientific paradigms are rarely overthrown. Instead, they are built upon.
They create a platform that supports new paradigms above them. Darwin’s
theory of natural selection was a “dangerous” idea—in Daniel Dennett’s phrase
—because it challenged Biblical and human-centric accounts of life’s history,
but the true measure of its scientific power lies in how many new fields were
stacked on top of it over the course of the twentieth century: the Mendelian and
population genetics that emerged from the “modern synthesis” in the 1940s; the
molecular genetics revolution triggered by Watson and Crick’s discovery of
DNA; newer fields like evolutionary psychology and “evolutionary
development.” Often, new scientific fields form by propping themselves over
multiple platforms. The field that ultimately explained Darwin’s Paradox—
ecosystems ecology—stands on the shoulders of population genetics, systems
theory, and biochemistry, among others.
Even the creative arts evolve via stacked platforms. This may seem surprising,
given how readily we draw upon the image of the private artistic genius, holed
up in his study, conjuring a whole new world in his head from scratch. For
understandable reasons, we like to talk about artistic innovations in terms of the
way that they break the rules, open up new doors in the adjacent possible that
lesser minds never even see. But genius requires genres. Flaubert and Joyce
needed the genre of the bildungsroman to contort and undermine in Sentimental
Education and A Portrait of the Artist as a Young Man. Dylan needed the
conventions of acoustic folk to electrify the world with Highway 61 Revisited.
Genres supply a set of implicit rules that have enough coherence that
traditionalists can safely play inside them, and more adventurous artists can
confound our expectations by playing with them. Genres are the platforms and
paradigms of the creative world. They are almost never willed into existence by
a single pioneering work. Instead, they fade into view, through a complicated set
of shared signals passed between artists, each contributing different elements to
the mix. The murder mystery has been coherent as a novelistic genre for a
hundred years, but when you actually chart its pedigree, it gets difficult to point
to a single donor: it’s a little Poe, a little Dickens, a little Wilkie Collins, not to
mention the dozens of contemporaries who didn’t make the canon, but who
nonetheless played a role in stabilizing the conventions of the genre. The same is
true of cubism, the sitcom, romantic poetry, jazz, magical realism, cinema verité,
adventure novels, reality TV, and just about any artistic genre or mode that has
ever mattered.
The creative stack is deeper than genres, though. Genres are themselves built
on top of more stable conventions and technologies. When Miles Davis
announced his break with the chord-and-improv conventions of bebop jazz in
“So What?”—the opening track of Kind of Blue—he was nonetheless working
within the conventions of the D Dorian scale the song employs, a mode that, as
its name suggests, dates back to the Dorian Greeks. And of course, Davis built
his new sound out of the older, stable platforms of the instruments themselves,
starting with the valved trumpet that Davis played. “Natural” trumpets—lacking
the complex valves that allow the trumpeter to switch keys on the fly—are
almost as old as the Dorian mode; the modern valved trump that Davis played
emerged as a standard in the nineteenth century, after decades of tinkering by
instrument makers across Europe. Davis could afford to explore the adjacent
possible of jazz, to help invent a whole new genre that others would build upon,
in part because he didn’t have to invent the D Dorian or the valved trumpet.
In the online world, the most celebrated recent case study in the innovative
power of stacked platforms has been the rapid evolution of the social networking
service Twitter. Twitter’s creators, Jack Dorsey, Evan Williams, and Biz Stone,
benefited from existing platforms just as the YouTube founders did: Twitter’s
legendary 140-character limit is based on the limitations of the SMS mobile
communications platform that they rely on to connect Web messages to mobile
phones. But the most fascinating thing about Twitter is how much has been built
on top of its platform in three short years. When it first emerged, Twitter was
widely derided as a frivolous distraction that was mostly good for telling your
friends what you had for breakfast. Now it is being used to organize and share
news about the Iranian political protests, to route around government censorship,
to provide customer support for large corporations, to share interesting news
items, and a thousand other applications that did not occur to the founders when
they dreamed up the service in 2006. This is not just a case of cultural
exaptation: people finding a new use for a tool designed to do something else. In
Twitter’s case, the users have been redesigning the tool itself. The convention of
replying to another user with the @ symbol was spontaneously invented by the
Twitter user base. Early Twitter users ported over a convention from the IRC
messaging platform, and began grouping a topic or event by the “hash-tag,” as in
“#30Rock” or “#inauguration.” The ability to search a live stream of tweets—
which is likely to prove crucial to Twitter’s ultimate business model, thanks to
its advertising potential—was developed by another start-up altogether. Thanks
to these innovations, following a live feed of tweets about an event—political
debates or Lost episodes—has become a central part of the Twitter experience.
But for the first year of Twitter’s existence, that mode of interaction would have
been technically impossible using Twitter. It’s like inventing a toaster oven and
then looking around a year later and discovering that all your customers have, on
their own, figured out a way to turn it into a microwave.
One of the most telling facts about the Twitter platform is that the vast
majority of its users interact with the service via software that has been created
by third parties. There are hundreds of iPhone and BlackBerry applications that
let you manage your Twitter feeds, all created by enterprising amateur coders or
small start-ups. There are services that help you upload photos and link to them
from your tweets; programs that map other Twitizens who are near you
geographically. Ironically, the tools you’re offered if you visit the Twitter.com
site have changed very little in the past two years. But there’s an entire Home
Depot of Twitter tools available everywhere else.
The diversity of the Twitter platform is no accident. It derives from a
deliberate strategy that Dorsey, Williams, and Stone embraced from the outset:
they built an emergent platform first, and then they built Twitter.com. An open
platform in software is often called an API, which stands for application
programming interface. An API is a kind of lingua franca that software
applications can use reliably to communicate with each other, a set of
standardized rules and definitions that allow programmers to build new tools on
top of another platform, or to weave together information from multiple
platforms. When Web users make geographic mashups using Google Maps, they
write programs that communicate with Google’s geographic data using their
mapping API.
Some APIs reveal only a small subset of a platform’s underlying code, in part
for simplicity’s sake, but also for proprietary reasons. Conventionally, a
developer will create a piece of software, and once it’s finished, expose a small
part of its functionality to outside developers via the API. The Twitter team took
the exact opposite approach. They built the API first, and exposed all the data
that was crucial to the service, and then they built Twitter.com on top of the API.
Conventional software assumes that API users are second-class citizens who
shouldn’t get full access to the software’s secret sauce for fear of losing
competitive advantage. Twitter’s creators recognized that there was another kind
of competitive advantage that came from complete openness: the advantage that
comes from having the largest and most diverse ecosystem of software
applications being built on your platform. Call it cooperative advantage. The
burden of coming up with good ideas for the product is no longer shouldered
exclusively by the company itself. On an open platform, good ideas can come
from anywhere.
The way for-profit companies like Twitter and Google have used open APIs to
spur innovation has been fascinating to watch. But the more intriguing
developments lie in the public sector. In the fall of 2008, Vivek Kundra, chief
technology officer for the District of Columbia, announced a program called
Apps for Democracy (replacing its somewhat more scandalous working title,
Hack the District). Software developers were invited to build applications that
drew upon the open data made available by the city government. The
applications could take just about any form imaginable—websites, Facebook
applications, iPhone apps—as long as they attempted to make some part of the
government data trove more useful for residents, visitors, businesses, or
government agencies. The winners would receive $10,000 in prize money.
The city provided just thirty days for developers to create their applications,
but even in that narrow window, forty-seven different applications were
submitted. The two winning applications showcased historic walking tours
around the D.C. area and provided extensive demographic information for
residents thinking of moving to a new neighborhood. Other submissions
included tools for tracking government spending on specific projects, guides for
city bikers, and real-time parking information with data received directly from
on-street parking meters. One ingenious, and amusing, app, called
StumbleSafely, helped inebriated users to plot the safest pedestrian route home
from any bar in the city.
The D.C. experiment was such a success that versions of it are currently
proliferating in dozens of major cities around the world. When D.C. launched its
second iteration of Apps for Democracy, in the spring of 2009, Kundra wasn’t
around to award the prizes, but for a good reason: he had been appointed the
nation’s chief information officer by President Obama, helping to create the
ambitious Data.gov program, along with an Apps for America contest run by the
Sunlight Foundation. What these initiatives share is a willingness to learn from
the innovation platforms of Twitter, Google, and Facebook. When Al Gore set
about to “reinvent government” during the Clinton administration, one of that
project’s ambitious goals was to make the bureaucracy more innovative. But
Gore’s solutions were, almost without exception, inward-facing: creating new
organizational structures inside the government, cutting down on red tape;
encouraging cross-departmental collaboration. What Apps for Democracy
suggests is a more open-ended idea: some of the best ideas for government are
likely to come from outside the government. If the outside-developer community
could build something as essential to Twitter’s business as a search interface,
then why can’t citizen developers provide comparable innovations for their
government? Surely someone out there can come up with a better user
experience for filing tax forms.
Government bureaucracies have a long and richly deserved reputation for
squelching innovation, but they possess four key elements that may allow them
to benefit from the innovation engine of an emergent platform. First, they are
repositories of a vast amount of information and services that could be of
potential value to ordinary people, if only we could organize it all better. Second,
ordinary people have a passionate interest in the kind of information
governments deal with, whether it’s data about industrial zoning, health-care
services, or crime rates. Third, a long tradition exists of citizens committing time
and intellectual energy to tackling problems where there is a perceived civic
good at stake. And, finally, the fact that governments are not in the private sector
means that they do not feel any competitive pressure to keep their data
proprietary.
Since the supernova that was the Howard Dean campaign in 2004, it has been
clear that network technology can be harnessed to help our leaders run for office.
But we have not yet seen real evidence that these extraordinary technologies can
help those leaders govern more effectively once they get elected. But thinking of
government as a platform—to borrow a phrase from Web visionary Tim
O’Reilly—might be one way to carry out the promise of digital-age governance.
Political leadership involves some elements that aren’t best outsourced to a
liquid network; decision-making and oratory. But a good government is, at least
in part, a government that comes up with innovative solutions to the problems of
its citizens, or to the problems faced by bureaucracy itself. That’s where the
platform model can do its magic.
Part of that magic is economic: emergent platforms can dramatically reduce
the costs of creation. Those forty-seven apps generated in a month by the
original Apps for Democracy contest had a total cost to the D.C. government of
$50,000. Kundra estimated that, had the city government contracted out for those
applications using its traditional methods, the cost to the city would have been
more than $2,000,000. (Also, the process would have taken more than a year.)
The same math applies to private-sector Web innovation. If Hurley, Chen, and
Karim had been forced to concoct an online video standard from scratch, it
would have taken years and tens of millions of dollars just to get a working beta
version online. To this day, Twitter has not spent a dime building a mapping
application to track the location of tweets, because dozens of services exist that
do exactly that, created and promoted by third parties at zero cost to Twitter
itself.
Though they are not measured in monetary units, natural platforms display
similar patterns of economic efficiency. Pileated woodpeckers make their homes
by drilling large holes in dead trees. But woodpeckers don’t have the resources
to kill off trees on their own, so they’re largely dependent on stumbling across
trees that have died of natural causes. But in creating their forest wetlands,
beavers are constantly toppling trees, and so pileated woodpeckers flourish in the
engineered ecosystem created by the beavers. They get the benefit of the softer,
more pliable wood of a rotting tree, without the cost of having to fell the tree.
Interestingly, woodpeckers generally abandon the homes they’ve carved into the
tree after a year, making them ideal spaces for songbirds to nest. The songbirds
benefit from the cavities created by the woodpeckers without being burdened by
the costs of drilling through all that wood. The wetland created by the beaver,
like the thriving platform created by the Twitter founders, invites variation
because it is an open platform where resources are shared as much as they are
protected.
If you sail due east sixteen nautical miles from Delaware’s Indian River Inlet,
and dive eighty feet down into the open waters of the Atlantic Ocean, you will
discover an underwater city thriving on the seafloor: massive schools of
flounder, sea bass, and tautog darting through gently waving sea grasses. You
will also find roughly seven hundred subway cars, deposited there by the
Delaware Department of Natural Resources and Environmental Control over the
past decade. The trains have been planted off the Delaware shore to create an
artificial reef, providing a durable shelter for mussels and sponges that are
otherwise challenged by the sandy floors of the northeast seaboard. Artificial
reefs create significant breeding grounds for a diverse group of fish; the
Delaware reefs have seen a 400 percent increase in biomass since the first cars
were sunk. (Artificial reefs also have the secondary effect of preventing beach
erosion.) No longer needed for mass transportation, the abandoned subway cars
have taken on a new occupation in their retirement years. They are now
ecosystem engineers.
Platforms have a natural appetite for trash, waste, and abandoned goods. The
sea bass and mussels making a home in a decommissioned A train, like the
songbirds nesting in the abandoned homes of the pileated woodpeckers, mirror a
pattern Jane Jacobs detected years ago in urban development: innovation thrives
in discarded spaces. Emergent platforms derive much of their creativity from the
inventive and economical reuse of existing resources, and, as any urbanite will
tell you, the most expensive resource in a big city is real estate. “If you look
about, you will see that only operations that are well established, high-turnover,
standardized or heavily subsidized can afford, commonly, to carry the costs of
new construction,” Jacobs wrote. “Chain stores, chain restaurants and banks go
into new construction. But neighborhood bars, foreign restaurants and pawn
shops go into older buildings. Supermarkets and shoe stores often go into new
buildings; good bookstores and antiques dealers seldom do.” One implication of
this is that riskier or smaller-scale enterprises tend to have difficulty getting
traction in planned environments that lack the economic wear and tear of the
traditional urban fabric, where buildings, blocks, and whole neighborhoods lose
their original inhabitants and industries, sometimes to catastrophic effects. (The
closest suburban approximation is the marginal space of the garage, where
Hewlett-Packard, Apple, and Google all set their original roots.) The shopping
mall is only fifty years old, and so is relatively young by the millennial scale of
some cities, but thus far even the most down-on-its-luck mall has retained its
original function: as a place where consumers gather to buy things for personal
use. They have not yet been reclaimed by troupes of performance artists, or
Internet start-ups, or heavy industry. There are streets in the West Village of
Manhattan, where Jacobs lived for so many years, that now resemble shopping
malls. But over the past two centuries those old buildings have hosted an entire
cavalcade of different uses: they have served as the hub of an industrial port; as
the primary supply point of meat for a city of eight million people; as a refuge
for beatniks and dropouts; as the epicenter of the gay rights movement. Jacobs’s
point was that the frenetic energy of a large city, the urban version of creative
destruction, creates a natural supply of older, less-desirable environments that
can be imaginatively reoccupied by the small or the eccentric, the subcultures
that Fischer found so essential to urban life. Artists, poets, and entrepreneurs are
the vibrant fish swimming among the coral of the Keeling Islands: they find it
easier to live in an exoskeleton that has long since been abandoned by its
original host. As Jacobs observed:
As for really new ideas of any kind—no matter how ultimately profitable
or otherwise successful some of them might prove to be—there is no
leeway for such chancy trial, error and experimentation in the highoverhead economy of new construction. Old ideas can sometimes use
new buildings. New ideas must use old buildings.
Platforms recycle much more than just architecture. Marine ecologists who
have studied the flow of energy through coral reef ecosystems have found that
coral reefs do an astounding job of recycling nutrients. Scientists have long
recognized the importance of the symbiotic relationship between the coral and a
microscopic algae called zooxanthella. The two organisms effectively rely on
each other’s waste products: the algae captures energy from the sun and outputs
oxygen and sugars as waste, which the coral polyps use to power their own
growth. At the same time, the corals expel carbon dioxide, nitrates, and
phosphates as waste, each of which fuels the growth of zooxanthellae. As the
population of zooxanthellae expands, more solar energy is captured and thus
available to be shared with the broader ecosystem of the reef. The zooxanthella
and the coral are like two neighbors who miraculously turn out to have a
pressing need for each other’s garbage, and thus meet every night to swap trash
cans.
But the nutrient recycling of a coral reef extends far beyond the collaboration
between coral and zooxanthella. In 2001, a team of German ecologists led by
Claudio Richter used endoscopes to examine the tiny internal cavities of coral
reefs in the Red Sea. Hidden in those diminutive grottoes was a vast population
of sponges that have adapted to the dark interior of the coral reef because it
provides them sanctuary from their natural predators, sea urchins and parrotfish.
The sponges consume another key photosynthetic organism, phytoplankton, as it
drifts through the aragonite caves of the reef. Like the zooxanthellae, the
sponges then expel waste products that the coral can use as nutrients. Those
long-hidden sponges embody two principles of platform recycling: by co-opting
the abandoned space of the coral skeleton, they reduce the costs of fortifying
themselves against predators. And in return, they expel nutrients that allow their
host to excrete even more aragonite, creating new habitats for more sponges.
The entire coral reef ecosystem is characterized by similarly intricate and
interdependent food webs, the full complexity of which scientists are only now
beginning to map. Once you understand the way biological platforms build on
the waste products generated within the system, Darwin’s Paradox ceases to be a
paradox at all. The symbiotic relationship between coral and zooxanthella
increases the total energy captured from the sun, and the tight nutrient cycles
created by the productive reuse of energy sources by so many densely
interconnected species means that the habitat can do much more with less. You
get a watery metropolis with astonishing diversity in an environment that by
rights should be as desolate as the sandy atoll above sea level. It is not
competition that drives that process, but rather the inventive collaborations of
density. The reef platform does not have the luxurious supply of nutrients that
tidal estuaries do, delivered daily by the freshwater rivers that carve topsoil out
of riverbanks upstream. But the reef platform thrives nonetheless, thanks to the
ecosystem engineering of the coral, and the marvelous recycling of both shelter
and biological waste that makes the platform so vital.
5 Above the waterline, on
those vacant atolls, a markedly different landscape appears, much closer to the
wasteful ecosystems of deserts. Most of the solar energy that saturates desert
environments gets lost, assimilated only by the few succulents that can survive
in such a hostile climate. Those plants pass along enough energy to sustain a
limited number of insects, which in turn supply food for the occasional reptile or
bird, all of which ultimately feeds the bacteria. But most of the energy never gets
put to use by organic life.
If Brent Constantz has his way, the coral reef’s genius for recycling and platform
building will end up transforming the physical platforms of human settlements.
In the late seventies, while pursuing a Darwinesque double major in biology and
geology at the University of California, Santa Barbara, Constantz became
fascinated by the coral polyp’s extraordinary powers of biomineralization, its
ability to build an immense structure of calcium carbonate durable enough to last
for millions of years. Human beings may be justifiably proud of venerable
engineering achievements like the Pyramids or the Great Wall of China, but
those monuments pale in comparison to the Great Barrier Reef, the largest
biological structure on the planet. As an undergrad, Constantz daydreamed about
harnessing the coral’s engineering skills to create entire buildings out of prefab
templates. Instead of pouring concrete or attaching steel beams, the templates
would simply be lowered into seawater, where the reef-building process would
magically conjure up a building. It was a fantasy in those years, but Constantz
kept that strange vision in the back of his mind for decades.
By 1985, Constantz was most of the way through a Ph.D. at U.C. Santa Cruz,
and had become an expert in the techniques of biomineralization. On his way to
a research expedition funded by a NSF grant, he stopped over for a few days to
see his parents at their home near Palo Alto. Watching a football game with his
father, a physician, he picked up a medical journal and stumbled across an article
about the massive health expenses associated with osteoporosis, a disease that
disrupts bone mineral density, causing painful and debilitating fractures. A few
weeks later, he was standing on the Rangiroa atoll in the middle of the Pacific
Ocean, measuring the speed with which the corals built their skeletons, and his
mind flashed back to the osteoporosis article. “If you could somehow capture
these skeleton growth processes,” he thought, “you could really help all those
old ladies with broken hips.” Two year later, he started his first company, which
mimicked the coral’s growth mechanism to create bone cement to repair
fractures. Today, the cements that Constantz created are employed in most
orthopedic operating rooms throughout the United States and Europe.
Constantz went on to found two other successful biomedical companies, but
that original hunch about building physical infrastructure out of coral skeletons
lingered in the back of his mind. While teaching at Stanford in the mid-2000s, he
joined the cross-disciplinary faculty of the Woods Institute for the Environment,
where for the first time he learned about the mammoth environmental impact of
manufacturing Portland cement, the third largest source of human-created carbon
dioxide emissions on the planet. In his mind, a new network of ideas began to
take shape, reviving his old undergraduate dream of growing aquatic cities.
Coral reefs created cement-like structures without polluting the environment,
and Constantz had three successful companies to show that mimicking the coral
growth mechanics could create useful new materials. What if you took those
mechanics and used them for building highway overpasses instead of repairing
hip fractures?
The slow hunch he had been nurturing for twenty-five years had finally found
the right connection. He took his vision of a “green” cement to one of Silicon
Valley’s legendary venture capitalists, Vinod Khosla, who agreed to fund the
company (which Constantz named Calera) without seeing so much as a business
plan or PowerPoint deck. Constantz built a laboratory in Los Gatos, where they
began “growing” carbonate cement in transport trailers filled with seawater. He
soon discovered that the system generated eight times as much cement if you
pumped the water full of carbon dioxide, like some oversized, salty club soda.
One day, when Khosla came to inspect the lab, Constantz turned to his investor
and asked, “Where can we get large quantities of carbon dioxide?” Khosla
looked at him in disbelief. As one of the world’s most prominent clean-tech
investors, Khosla was well aware that the planet was teeming with industrial
plants who were desperately trying to find a place to put their carbon dioxide.
Entire markets were emerging around technologies of carbon sequestration,
locking up CO2 by injecting it into oil and gas reserves, or burying it deep in the
ocean. But Constantz had stumbled across a much more powerful idea. You
didn’t have to bury all that CO2
. You could use it to build stuff.
The Calera story is still very much in progress. It remains an open question
whether the cities of our future will be built underwater by virtual coral reefs on
a diet of factory exhaust. It sounds fanciful when described that way, of course,
but no more fanciful than the idea of the Great Barrier Reef would have seemed
a billion years ago. Nature has long built its platforms by recycling the available
resources, including the waste generated by other organisms. Two things we
have in abundance on this planet right now are pollution and seawater. Why not
try to build a city out of them?
The stacked platform of the Web depends on recycling as well. The word
“ecosystem” has become a fashionable term to describe the diverse collection of
sites and services associated with Web 2.0. Like most jargon, the metaphor
points to an important truth, if you think of the flows of information across the
Web as being analogous to the flows of energy through a natural ecosystem. But
also like most jargon, the metaphor is too general, and its broad scope actually
makes it harder for us to see the most important thing about the evolution of the
Web over the past fifteen years. The Web is not simply an ecosystem; it is a
specific type of ecosystem. It started as a desert, and it has been steadily
transforming into a coral reef.
Part of the beauty and power of Tim Berners-Lee’s architecture for the Web
lies in its simplicity: websites were made up of hypertext pages that could
connect to other information on the Web through one primary conduit: the link.
Imagine it’s 1995, and you decide to post a short review of a new restaurant in
Boston’s Back Bay neighborhood on your “home page,” as we used to call them
back then. In posting that restaurant review, you are contributing new
information to the Web’s ecosystem. Like zooxanthellae capturing energy from
the sun, you are taking information originally created outside the environment
(in the neural networks of your own brain) and adding it to the information
resources available on the Web.
The question is, what happened to that information once you added it to the
system? You could link to the home page of the restaurant itself, if you were
lucky enough to find one in those early days. From that point on, your site would
be connected to that other page, and subsequent visitors to your site could follow
that connection with a single mouse-click. In some basic sense, by linking to the
original restaurant site, you would be recycling the information stored there,
making your review more informative. Another food lover might stumble across
your review and link to it from her own site, or forward the URL for your review
in an e-mail message to a few friends. But for the most part, the information
added to the system would remain trapped on your original page, like a lonely
cactus waiting for a handful of insects to stumble across it.
Fast-forward to the present. You’re sitting in the same restaurant, having just
finished a delightful bowl of vichyssoise, and you pull out your mobile phone
and compose a 140-character rave review of the soup, with a link to the
restaurant’s website, and you post it to Twitter before the check has even
arrived. Just as before, you are adding new information to the Web’s ecosystem
with that tweet. But what happens to that information after you press “submit”
on your phone?
For starters, it circulates through the ecosystem in a way that was unthinkable
in 1995. Within seconds of your composing the note, it is pushed out to all your
Twitter followers, in some cases sent directly to their mobile phones. Thanks to
the “re-tweeting” convention spontaneously adopted by the Twitter community,
that original vichyssoise tweet is easily forwarded along to other foodies on
Twitter. But that’s just the start of the journey. Thanks to the geographic data
attached to the post by your GPS-powered mobile device, the real-world social
network Foursquare automatically distributes the vichyssoise tweet to all its
users who have recently visited nearby bars, restaurants, or other public spaces.
(Even coffeehouses!) The tweet pops up immediately as a pushpin on the
countless Twittermaps that developers have created over the past few years. The
hyperlocal news platform Outside.in (which I helped create a few years ago)
parses the geo-data and detects the name of the restaurant in the tweet, and
automatically attaches it to pages devoted to discussion of the restaurant itself,
along with pages that cover all the news and commentary about the Back Bay
neighborhood, and pages devoted to the Boston restaurant scene. A Boston
newspaper that has built neighborhood-specific news pages using Outside.in’s
open publisher platform runs that tweet on a page devoted to food gossip in the
Back Bay. Google detects the link to the restaurant’s website and registers the
link as a “vote” endorsing the quality of that page, which causes it to rise higher
in the search-results page when people query Google with its name. The tweet
even shows up in the inbox of the restaurant’s proprietor, who has established a
Google Alert that automatically e-mails him when anything appears online that
mentions his restaurant by name. On many of these pages—on the newspaper
sites, on Google—local ads appear for other businesses in the neighborhood,
drawn like moths to the bright flame of the geographic data embedded in the
tweet.
Most of that whole sequence unfolds within minutes, without you having to
think about anything other than composing those 140 characters and
remembering to press “submit.”
The story here is not the old chestnut of living in a connected age where
information flows more quickly than ever before. The information is not simply
flowing in this system; it’s being recycled and put to new uses, transformed by a
diverse network of other species in the ecosystem, each with its own distinct
function. You write a tweet about what you had for lunch—the original sin of
Twitter banality—and within minutes that information is being harnessed to
assist a staggering number of different tasks: neighbors forging new personal
connections, foodies seeking a delicious cup of potato and leek soup, restaurant
owners getting unvarnished feedback from their patrons, Google organizing all
the world’s information, newspapers improving their neighborhood coverage at
lower cost, and local businesses seeking the attention of the people in their
immediate community. Not bad for 140 characters.
But of course the point is that those 140 characters had help. At every step of
their journey, they were standing on layers of stacked platforms. The simplicity
of sending out a message to a social network of followers depends on the Twitter
API and underlying database; that they instantly reach mobile phones as text
messages relies on the SMS communications protocol (along with the network
of cell towers and satellites); Outside.in distributes its neighborhood data using
the open RSS platform; the geo-data embedded in the original tweet relies on the
adapted military intelligence technology of GPS; the Twittermaps all involve
API calls to Google’s map service; and, of course, the entire operation is
sustained by the coral-and-zooxanthella foundation of underlying protocols like
HTTP and TCP/IP. All those services and standards were essential to the web of
information that benefited from those 140 characters, but not one of them
required a business development deal, or a licensing fee, or even an oldfashioned handshake. You can build on all of them without asking for
permission, and when you don’t have to ask for permission, innovation thrives.
When Guier, Weiffenbach, and McClure were designing their system to help
American submarines launch Polaris missiles against the Soviet Union, it never
occurred to them that someday someone would use their platform to rave about a
bowl of potato and leek soup to nearby strangers. Stacked platforms are like that:
you think you’re fighting the Cold War, and it turns out you’re actually helping
people figure out where to have lunch.
In a funny way, the real benefit of stacked platforms lies in the knowledge you
no longer need to have. You don’t need to know how to send signals to satellites
or parse geo-data to send that tweet circulating through the Web’s ecosystem.
Miles Davis didn’t have to build a valved trumpet or invent the D Dorian mode
to record Kind of Blue. The songbird sitting in an abandoned woodpecker’s nest
doesn’t need to know how to drill a hole into the side of a poplar, or how to fell a
hundred-foot tree. That is the generative power of open platforms. The songbird
doesn’t carry the cost of drilling and felling because the knowledge of how to do
those things was openly supplied by other species in the chain. She just needs to
know how to tweet.
Conclusion
THE FOURTH QUADRANT
On the somewhat desolate corner of Grand Street and Morgan Avenue in the
Williamsburg neighborhood of Brooklyn, a five-story building stands, built in
the watered-down Romanesque style favored by industrial architects a century
ago. Today it is home to a mix of uses: twentysomething roommates sharing loft
spaces on the fringes of one of New York’s hottest neighborhoods, amid a
handful of small businesses, most of them in the information industries. A
hundred years ago, the building had a single tenant: the Sackett-Wilhelm
Lithography Company. If you stand at the front door on Grand Street, or scan the
bars on the first-floor windows and the graffiti on the old loading docks, nothing
indicates the historic nature of the site. But historic it is: the Sackett-Wilhelm
Lithography Company housed the first working version of a machine that would
do more to transform the settlement patterns of human beings than any other
twentieth-century invention, with the possible exception of the automobile.
In 1902, the Sackett-Wilhelm company had a profitable and growing business
printing color publications, like the popular humor magazine Judge. But they
faced one vexing problem: the air. Small changes in humidity could complicate
the printing process on multiple levels: the paper would expand as it absorbed
water molecules floating in the factory air; ink would flow at different rates, and
dry more slowly. Unusually humid days could slow the entire production down
dramatically, making it difficult for the Sackett-Wilhelm executives to promise
reliable delivery times to its clients.
Human beings had been artificially moderating air temperature since the
invention of fire. The nineteenth century had witnessed a growing trend toward
mechanical heating systems. A few exotic schemes had attempted to cool
building interiors, but all of them involved drawing air over massive quantities
of ice. (Madison Square Theater in Manhattan used four tons of ice each night to
make summertime evenings tolerable for their patrons.) But none of these
approaches tackled the problem of humidity. After two consecutive heat waves
in the summers of 1900 and 1901, the Sackett-Wilhelm owners contacted the
New York office of the Buffalo Forge Company, which specialized in
mechanical heating systems for large industry. They were the experts in making
the air warmer. Could they make it less wet?
It was a fortuitous query, because the founder of the Buffalo Forge Company,
William F. Wendt, had just caved in to the demands of an ambitious twenty-fiveyear-old electrical engineer named Willis Carrier and created a “research
program” where Carrier could take on more speculative projects. Carrier’s lab
was the perfect place to tackle a problem like dehumidifying air, and Carrier
threw himself into the project with enthusiasm. After experimenting with a
handful of failed schemes suggested by his colleagues, Carrier followed his own
instincts and built a contraption that passed chilled water through a heating coil
that usually conveyed steam. Using dew-point charts from the Weather Bureau,
he built a system that cooled the air to the dew-point temperature that would
produce the 55-percent humidity that the Sackett-Wilhelm company considered
optimal. By the late summer of 1902, a system engineered by Carrier was
operational in the Sackett-Wilhelm plant. It drew water from an artesian well,
with additional cooling provided by an ammonia refrigerating machine. The
overall cooling effect on a hot summer day was the equivalent of melting
108,000 pounds of ice in a single twenty-four-hour period.
Carrier would continue tinkering with his system over the following years.
The Sackett-Wilhelm system had been a success, but the steel coils were prone
to rust after regular use. One night, waiting for a train in Philadelphia, watching
a heavy fog roll across the platform, he had a sudden flash of insight. His airconditioning system could be a miniature fog machine: by drawing air across a
fine spray of water inside the device, he could use the water itself as a
condensing surface. Thanks to those tenacious hydrogen bonds, the molecules of
water vapor in the spray would pull the moisture out of the air, regulating the
humidity and eliminating the rust problem. (As Carrier put it in his
autobiography: “Water won’t rust.”) Carrier applied for a patent for his
“Apparatus for Treating Air” in September of 1904. On the second day of 1906,
the patent was granted. Before long, Carrier and a band of entrepreneurial
engineers from Buffalo Forge broke off and formed the Carrier Engineering
Corporation, devoted exclusively to the manufacture of air-conditioning systems.
The business made Carrier a wealthy man, as air conditioning went from a
curiosity to a luxury item to a middle-class necessity. In 2007, the Carrier
Corporation, now part of United Technologies, did $15 billion in sales. Thanks
to Carrier’s brilliant idea, the second half of the twentieth century saw a mass
migration within the United States to the Sunbelt and to Deep South climates
that had been nearly intolerable before the widespread adoption of air
conditioning. It is not exaggerating matters to say that Carrier’s idea ultimately
rearranged the social and political map of America.
Carrier’s story is the archetypal myth of modern innovation. A clever individual,
working in a private research lab, driven by ambition and the promise of great
riches, hits upon a brilliant idea in a sudden flash of insight and the world
changes. Yes, Carrier’s story is slightly more complicated than this cartoon
version suggests. He was more focused originally on humidity than on
temperature; the ultimate solution took several years to crystallize; and some of
his technical solutions built on the ideas of those who had come before him. But
this is quibbling. Carrier’s narrative fits the classic mold of the genius
entrepreneur. It lacks almost all of the patterns that we have seen over the
preceding chapters: no liquid networks (if you don’t count the fog); no
coffeehouse exaptations; no brilliant mistakes. And it ends with a triumphant
patent grant.
All of which leads to the inevitable question: Is Willis Carrier an anomaly or
not?
The question has real political and social stakes, because the doxa of market
capitalism as an unparalleled innovation engine has long leaned on stories like
Willis Carrier’s miraculous cooling device as a cornerstone of its faith.
6
In many
respects, these beliefs made sense, because the implicit alternatives were the
planned economies of socialism and communism. State-run economies were
fundamentally hierarchies, not networks. They consolidated decision-making
power in a top-down command system, which meant that new ideas had to be
approved by the authorities before they could begin to spread through the
society. Markets, by contrast, allowed good ideas to erupt anywhere in the
system. In modern tech-speak, markets allowed innovation to flourish at the
edges of the network. Planned economies were more like the old mainframe
computer systems that predated the Internet, where every participant had to get
authorization from a central machine to do new work. When Friedrich von
Hayek launched his influential argument in the 1940s about the importance of
price signals in market economies, he was observing a related phenomenon: the
decentralized pricing mechanism of the marketplace allows an entrepreneur to
gauge the relative value of his or her innovation. If you come up with an
interesting new contraption, you don’t need to persuade a government
commission of its value. You just need to get someone to buy it.
Entire institutions and legal frameworks—not to mention a vast tower of
conventional wisdom—have been built around the Carrier model of innovation.
But what if he’s the exception and not the rule?
There are three main approaches for settling a question as complicated as this.
You can dive deeply into a single story and try to persuade your audience that it
is representative of a larger societal truth. (This is the strategy I adopted in
telling the stories of John Snow and Joseph Priestley—and the innovation
environments that shaped their work—in my previous two books.) The
advantage of this approach is that it allows you to examine a case study in
exhaustive detail. The disadvantage, of course, is that your audience has to take
it on faith that the case study you’ve chosen is indeed representative of a wider
truth. The second approach, which I have taken in the preceding chapters of this
book, is to build an argument around dozens of anecdotes, drawn from different
contexts and historical periods. The anecdotal approach sacrifices detail for
breadth. Yet it, too, runs the risk of being accused of cherry-picking. If there are
a hundred Willis Carriers for every Tim Berners-Lee, it doesn’t really prove
anything to string together a book of Berners-Lee stories. (In fact, it may well be
misleading to do so.)
To see around the potential distortions of the case-study and anecdotal
approaches, you need to see the entire field of innovation through a single lens.
You can’t tell whether Willis Carrier is an anomaly by studying the fine points
of his biography. You need a wider view. So let us perform an experiment on the
data available on the history of innovation. Take roughly two hundred of the
most important innovations and scientific breakthroughs from the past six
hundred years, starting with Gutenberg’s press: everything from Einstein’s
theory of relativity to the invention of air conditioning to the birth of the World
Wide Web. Plot each breakthrough somewhere in one of the four quadrants of
this diagram:
Classify innovations that involved a small, coordinated team within an
organization—or, even better, a single inventor—as “individual.” Classify as
“networked” all the innovations that evolved through collective, distributed
processes, with a large number of groups working on the same problem.
Inventors who planned to capitalize directly from the sale or licensing of their
invention should be classified as “market”; those who wished their ideas to flow
freely into the infosphere belong to the “non-market” side. The result is four
quadrants: the first correlating to the private corporation or the solo
entrepreneur; the second to a marketplace where multiple private firms interact;
the third to the amateur scientist or hobbyist who shares his or her ideas freely;
and, finally, the fourth quadrant, which corresponds to open-source or academic
environments, where ideas can be built upon and reimagined in large,
collaborative networks.
By taking this long view, we can begin to answer the question we began with:
Just how dominant is the Willis Carrier model of innovation?
7 Which quadrant
has the most impressive track record for generating good ideas?
To give us some bearings, our anchor tenant in the first quadrant—the market-
based individual—is Carrier himself, who single-handedly drove the invention
of air conditioning and who had clear commercial aspirations for his device.
(Gutenberg belongs there as well.) An example of a networked market
innovation would be the vacuum tube, the creation of which involved a
decentralized network with dozens of key participants, including Lee de Forest,
almost all of whom worked either as patent-prone entrepreneurs or research
scientists within larger corporations. Tim Berners-Lee’s creation of the World
Wide Web belongs to the individual, non-market quadrant, while the Internet
itself belongs to the fourth quadrant, given the vast number of public sector
individuals and organizations involved in its creation.
It should be noted that these classifications do not reflect the cumulative
nature of almost any innovation. Berners-Lee needed the open platform of the
Internet for his hypertext creation to take flight, and thus the many individuals
who built ARPANET and TCP/IP should be understood as essential contributors
to the Web. Had those platforms been more proprietary ones—say, by charging
licensing fees for the privilege of developing on top of them—it’s entirely
possible that Berners-Lee wouldn’t have bothered creating the Web in the first
place, given that it was a side project that his superiors knew next to nothing
about.
It is in the nature of good ideas to stand on the shoulders of the giants who
came before them, which means that by some measure, every important
innovation is fundamentally a network affair. But, for the sake of clarity, let’s
not blur the line between “individual” and “network” by admitting to the
discussion the prior innovations that inspired or supported the new generation of
ideas. Yes, it is important that Gutenberg borrowed the screw-press technology
from the winemakers, but one cannot say that the printing press was a collective
innovation the way, for example, the Internet clearly was. So Gutenberg and
Berners-Lee get classified on the individual side of the spectrum.
There is no reliable mathematical formula for making these classifications,
and to a certain extent each of them involves an element of subjectivity. But I
think that, seen together as a group, they reveal an interesting pattern—
interesting enough, I would argue, for us to tolerate a little noise in the data. We
are accustomed to looking at certain historical developments—mostly
demographic—in this condensed, time-lapse format. We watch the growth of
cities, or markets, or national populations unfold in charts where each tick
measures a century. There are truths made visible by these time-lapse views that
present-tense surveys or individual, narrative histories cannot properly shine
light on. (Malthus’s Principles of Population, which so inspired Darwin and
Wallace, offered an early glimpse of that special effect.) But we rarely measure
cultural changes this way. So much of the history of ideas is like Darwin’s work
as a naturalist during the long years that preceded the publication of Origin:
analyzing an individual species, defining its key characteristics, and putting it in
the proper box. That’s a fine approach for understanding why a specific idea
came into being at a particular moment in time. But if you want to wrestle with
the question one link farther up the chain—how do good ideas tend to come
about—you need to take on the problem from a different angle. There’s a place
for counting barnacles. But sometimes you need to zoom out and take the longer
view.
In taking this approach, I am exapting a technique that the literary historian
Franco Moretti calls “distant reading.” In a series of influential books and essays
published over the past decade, Moretti has broken from the traditional English
Department approach of “close reading,” in which individual literary texts are
analyzed in exhaustive detail. It doesn’t really matter whether the close reading
in question is an old-school tribute to an artist’s singular talents or a politicized
deconstruction—you can read the text closely to reveal the genius of the author,
or his latent homophobia, but in each case you’re doing close reading, where
every sentence is a potential datapoint in your analysis. (“At bottom,” Moretti
writes, “it’s a theological exercise—very solemn treatment of very few texts
taken very seriously.”) Distant reading takes the satellite view of the literary
landscape, looking for larger patterns in the history of the stories we tell each
other. In one typically inventive analysis, Moretti tracked the evolution of
subgenres in popular British novels from 1740 to 1915, an immense taxonomy
of narrative forms—spy novels, picaresques, gothic novels, nautical tales,
mysteries, and dozens of other distinct forms. He plotted the life span of each
sub-genre as a dominant species in the British literary ecosystem. The result is
on page 223.
British Novelistic Genres, 1740—1900
What happens when you take the distant approaching to reading novels is that
you’re able to see patterns that simply aren’t visible on the scale of paragraphs
and pages, or even entire books. You could read a dozen “silver fork” novels and
bildungsromans and yet miss the most striking fact revealed by Moretti’s chart:
that the diversity of forms is strikingly balanced by their uncannily similar life
spans, which Moretti attributes to underlying generational turnover. Every
twenty-five to thirty years a new batch of genres becomes dominant, as a new
generation of readers seeks out new literary conventions. If you’re trying to
understand the meaning of an individual work, you have to read closely. But if
you’re interested in the overall behavior of the literary system—its own patterns
of innovation—sometimes you have to read from a long way off.
In the study of scientific or technological innovation, the equivalent of close
reading is the meticulous biography of the great inventor, or the history of a
single technology: the radio, say, or the personal computer. As valuable as those
approaches can be, they have their limitations. Close reading leaves you with the
idiosyncrasies of each individual or invention, the local color—but not the
general laws. When you view the history of innovation from a distance, what
you lose in detail you gain in perspective. Classifying two hundred good ideas
into four broad quadrants certainly makes it harder to learn anything specific
about each individual innovation. But it does allow us to answer the question we
began with: What kind of environments make innovation possible in the first
place?
1400-1600
Because innovation is subject to historical changes—many of which are
themselves the result of influential innovations in the transmission of
information—the four quadrants display distinct shapes at different historical
periods. Start with this view of the breakthrough ideas from 1400 to 1600,
beginning with Gutenberg’s press and continuing on to the dawn of the
Enlightenment (see page 227).
This is the shape that Renaissance innovation takes, seen from a great
(conceptual) distance. Most innovation clusters in the third quadrant: non-market
individuals. A handful of outliers are scattered fairly evenly across the other
three quadrants. This is the pattern that forms when information networks are
slow and unreliable, and entrepreneurial economic conventions are poorly
developed. It’s too hard to share ideas when the printing press and the postal
system are still novelties, and there’s not enough incentive to commercialize
those ideas without a robust marketplace of buyers and investors. And so the era
is dominated by solo artists: amateur investigators, usually well-to-do, working
on their own private obsessions. Not surprisingly, this period marks the birth of
the modern notion of the inventive genius, the rogue visionary who somehow
sees beyond the horizon that limits his contemporaries—da Vinci, Copernicus,
Galileo. Some of those solo artists (Galileo most famously) worked outside of
broader groups because their research posed a significant security threat to the
established powers of the day. The few innovations that did emerge out of
networks—the portable, spring-loaded watches that first appeared in Nuremberg
in 1480, the double-entry bookkeeping system developed by Italian merchants—
have their geographic origins in cities, where information networks were more
robust. First-quadrant solo entrepreneurs, crafting their products in secret to
ensure their eventual payday, turn out to be practically nonexistent. Gutenberg
was the exception, not the rule.
1600-1800
Scanning the next two centuries, we see that the pattern changes dramatically
(see page 229).
Solo, amateur innovation (quadrant three) surrenders much of its lead to the
rising power of networks and commerce (quadrant four). The most dramatic
change lies along the horizontal axis, in a mass migration from individual
breakthroughs (on the left) to the creative insights of the group (on the right).
Less than 10 percent of innovation during the Renaissance is networked; two
centuries later, a majority of breakthrough ideas emerge in collaborative
environments. Multiple developments precipitate this shift, starting with
Gutenberg’s press, which begins to have a material impact on secular research a
century and a half after the first Bible hits the stands, as scientific ideas are
stored and shared in the form of books and pamphlets. Postal systems, so central
to Enlightenment science, flower across Europe; population densities increase in
the urban centers; coffeehouses and formal institutions like the Royal Society
create new hubs for intellectual collaboration.
Many of those innovation hubs exist outside the marketplace. The great minds
of the period—Newton, Franklin, Priestley, Hooke, Jefferson, Locke, Lavoisier,
Linnaeas—had little hope of financial reward for their ideas, and did everything
in their power to encourage their circulation. A vertical movement toward
market incentives is noticeable, nonetheless. As industrial capitalism arises in
England in the eighteenth century, new economic structures raise the stakes for
commercial ventures: tantalizing rewards lure innovators into private enterprise,
and the codification of English patent laws in the early 1700s gives some
reassurance that good ideas will not be stolen with impunity. Despite this new
protection, most commercial innovation during this period takes a collaborative
form, with many individuals and firms contributing crucial tweaks and
refinements to the product. The history books like to condense these slower,
evolutionary processes into eureka moments dominated by a single inventor, but
most of the key technologies that powered the Industrial Revolution were
instances of what scholars call “collective invention.” Textbooks casually refer
to James Watt as the inventor of the steam engine, but in truth Watt was one of
dozens of innovators who refined the device over the course of the eighteenth
century.
1800-present
Let us pause for a moment on the cusp of the modern age and take a few bets as
to what pattern will form in the final two centuries of the millennium. I think
most of us would expect to see a dramatic consolidation of innovative activity in
the first quadrant, as capitalism enters its mature period, spanning the ages of
mass production and the consumer society. All the elements would seem to
predict an explosion of first-quadrant activity: an increasingly wealthy public
willing to spend money on new gadgets; strong enforcement of intellectual
property rights; the emergence of corporate research-and-development labs; and
a growing pool of private capital willing to finance speculative ventures. If the
competitive marketplace of modern capitalism is the great innovation engine of
our time, the first quadrant should by rights dominate the last two centuries of
activity.
But instead, another pattern appears (see page 229).
Against all odds, the first quadrant turns out to be the least populated on the
grid. Willis Carrier is an outlier after all. In the private sector, the proprietary
breakthrough achieved in a closed lab turns out to be a rarity. For every Alfred
Nobel, inventing dynamite in secret in the suburbs of Stockholm, there are a half
dozen collective inventions like the vacuum tube or the television, whose
existence depended upon multiple firms driven by the profit motive who
managed to create a significant new product via decentralized networking.
Folklore calls Edison the inventor of the lightbulb, but in truth the lightbulb
came into being through a complex network of interaction between Edison and
his rivals, each contributing key pieces to the puzzle along the way. Collective
invention is not some socialist fantasy; entrepreneurs like Edison and de Forest
were very much motivated by the possibility of financial rewards, and they tried
to patent as much as they could. But the utility of building on other people’s
ideas often outweighed the exclusivity of building something entirely from
scratch. You could develop small ideas in a locked room, cut off from the
hunches and insights of your competition. But if you wanted to make a major
new incursion into the adjacent possible, you needed company.
Even more striking, though, is the explosion of fourth-quadrant activity.
Why have so many good ideas flourished in the fourth quadrant, despite the
lack of economic incentives? One answer is that economic incentives have a
much more complicated relationship to the development and adoption of good
ideas than we usually imagine. The promise of an immense payday encourages
people to come up with useful innovations, but at the same time it forces people
to protect those innovations. Economists define “efficient markets” as markets
where information is evenly distributed among all the buyers and sellers in the
space. Efficiency is generally held to be a universal goal for any economy—
unless the economy happens to traffic in ideas. If ideas were fully liberated, then
entrepreneurs wouldn’t be able to profit from their innovations, because their
competitors would immediately adopt them. And so where innovation is
concerned, we have deliberately built inefficient markets: environments that
protect copyrights and patents and trade secrets and a thousand other barricades
we’ve erected to keep promising ideas out of the minds of others.
That deliberate inefficiency doesn’t exist in the fourth quadrant. No, these
non-market, decentralized environments do not have immense paydays to
motivate their participants. But their openness creates other, powerful
opportunities for good ideas to flourish. All of the patterns of innovation we
have observed in the previous chapters—liquid networks, slow hunches,
serendipity, noise, exaptation, emergent platforms—do best in open
environments where ideas flow in unregulated channels. In more controlled
environments, where the natural movement of ideas is tightly restrained, they
suffocate. A slow hunch can’t readily find its way to another hunch that might
complete it if there’s a tariff to be paid every time it tries to make a new
serendipitous connection; exaptations can’t readily occur across disciplinary
lines if there are sentries guarding those borders. In open environments,
however, those patterns of innovation can easily take hold and multiply.
Like any complex social reality, creating innovation environments is a matter
of trade-offs. All other things being equal, financial incentives will indeed spur
innovation. The problem is, all other things are never equal. When you introduce
financial rewards into a system, barricades and secrecy emerge, making it harder
for the open patterns of innovation to work their magic. So the question is: What
is the right balance? It’s certainly conceivable that the promise of hitting a
financial jackpot is so overwhelming that it more than makes up for the
inefficiencies introduced by intellectual property law and closed R&D labs. That
has generally been the guiding assumption for most modern discussions of
innovation’s roots, an assumption largely based on the free market’s track record
for innovation during that period. Because capitalist economies proved to be
more innovative than socialist and communist economies, the story went, the
deliberate inefficiencies of the market-based approach must have benefits that
exceed their costs.
But, as we have seen, this is a false comparison. The test is not how the
market fares against command economies. The real test is how it fares against
the fourth quadrant. As the private corporation evolved over the past two
centuries, a mirror image of it grew in parallel in the public sector: the modern
research university. Most academic research today is fourth-quadrant in its
approach: new ideas are published with the deliberate goal of allowing other
participants to refine and build upon them, with no restrictions on their
circulation beyond proper acknowledgment of their origin. It is not pure anarchy,
to be sure. You can’t simply steal a colleague’s idea without proper citation, but
there is a fundamental difference between suing for patent infringement and
asking for a footnote. Academics are paid salaries, of course, and successful
ideas can lead to much-sought-after tenured professorships, but the economic
rewards are minuscule compared to those of the private sector. And, crucially,
those rewards are not dependent on introducing an artificial inefficiency into the
information network. A historian who develops a brilliant new theory about the
origins of the Industrial Revolution may well land a chaired professorship at an
Ivy League school thanks to her theory, but the theory itself can freely circulate
through the environment, where it can be challenged, enlarged, exapted, and
recycled in countless ways. The university system may be big business these
days, and patents do play a role in some specialized fields, but for the most part
the university remains an information commons.
Universities have a reputation for ivory-tower isolation from the real world,
but it is an undeniable fact that most of the paradigmatic ideas in science and
technology that arose during the past century have roots in academic research.
This is obviously true for the “pure” sciences like theoretical physics, but it is
also true for lines of research that on their surface seem to have more
straightforwardly commercial applications. The oral contraceptive, for instance,
has generated billions of dollars for Big Pharma over the past half century, but
most of the critical research that led to its development happened in the
intellectual commons of university labs at Harvard, Princeton, and Stanford. In
the language of the last chapter, open networks of academic researchers often
create emergent platforms where commercial development becomes possible.
The next decade will likely see a wave of pharmaceutical products enabled by
genomic science, but that underlying scientific platform—most critically, the
ability to sequence and map DNA—was almost entirely developed by a
decentralized group of academic scientists working outside the private sector in
the 1960s and seventies. This is a pattern we see again and again in the modern
era: fourth-quadrant innovation creates a new open platform that commercial
entities can then build upon, either by repackaging and refining the original
breakthrough, or by developing emergent innovations on top of the underlying
platform.
Fourth-quadrant innovation has been assisted by another crucial development:
the increased flow of information. Information spillover required the geographic
density of cities in the Renaissance, while the postal system made small
distributed webs of creativity possible in the Enlightenment. But the Internet has
effectively reduced the transmission costs of sharing good ideas to zero. In
Galileo’s time, all the benefits of information spillover were as potent as they are
today. But it was far more difficult to create the kind of liquid network where
those serendipitous collisions and exaptations could take place. The
connectedness of modern life means that we face the opposite problem: it is
much harder to stop information from spilling over than it is to get it into
circulation. The consequence of this is that private-sector firms who are intent on
protecting their intellectual assets have to invest time and money in building
barricades of artificial scarcity. Participants in the fourth quadrant don’t have
those costs: they can concentrate on coming up with new ideas, not building
fortresses around the old ones. And because those ideas can freely circulate
through the infosphere, they can be refined and expanded by other minds in the
network.
We do not have a ready-made political vocabulary for the fourth quadrant,
particularly the noninstitutional forms of collaboration that have developed
around the open-source community. Because these open systems operate outside
the conventional incentives of capitalism and resist the usual strictures of
intellectual property, the mind reflexively wants to put them on the side of
socialism. And yet they are as far from the state-centralized economies that
Marx and Engels helped invent as they are from greed-is-good capitalism. They
themselves are not the product of market incentives, but they often create
environments where private firms thrive, a phenomenon Lawrence Lessig
alludes to in his concept of the “hybrid economy,” which blends elements from
the open networks of the intellectual commons with the more proprietary walls
and tariffs of the private sphere.
None of this is meant to imply that the marketplace is the enemy of
innovation, or that competition between rival firms doesn’t often lead to useful
new products. (The second quadrant, after all, bustles with dozens of brilliant
ideas that changed our lives for the better.) And top-heavy bureaucracies remain
innovation sinkholes. But, fortunately for us, the choice is not between
decentralized markets and command-and-control states. Much of the history of
intellectual achievement over these past centuries has lived in a less formal space
between those two regimes: in the grad seminar and the coffeehouse and the
hobbyist’s home lab and the digital bulletin board. The fourth quadrant should
be a reminder that more than one formula exists for innovation. The wonders of
modern life did not emerge exclusively from the proprietary clash between
private firms. They also emerged from open networks.
A few months after Darwin published On the Origin of Species in 1859, Karl
Marx wrote Friedrich Engels a letter that included a few lines endorsing
Darwin’s biological radicalism. “Although it is developed in the crude English
style, this is the book which contains the basis in natural history for our view.”
The “crude English style” was evidently Darwin’s strange unwillingness to
incessantly relate his scientific views back to Hegelian dialectics. (Many now
regard that as one of Darwin’s strengths as a writer.) Beyond the sneers, Marx
and Engels were clearly energized by the controversy Darwin had unleashed and
saw him as a kindred spirit in an age that seemed on the verge of multiple
revolutions—in science as well as in society. It is unclear whether Darwin felt
quite the same way about his Prussian admirers. Marx offered to dedicate
volume two of Das Kapital to Darwin, who demurred: “I should prefer the part
or volume not to be dedicated to me (although I thank you for the intended
honour), as that would, in a certain extent, suggest my approval of the whole
work, with which I am not acquainted.”
From a scientific point of view, Marx and Engels were smart to side with
Darwin so early in the debate over his “dangerous” idea. But they couldn’t have
been more wrong in their predictions about the way the theory would play out in
the politico-economic arena. They anticipated, correctly, that analogies would be
drawn between Darwin’s “survival of the fittest” and the competitive selection
of capitalist free-market economies. Marx and Engels just assumed those
analogies would be launched as critiques of capitalism. In 1865, Engels wrote to
a friend, “Nothing discredits modern bourgeois development so much as the fact
that it has not yet succeeded in getting beyond the economic forms of the animal
world.”
As it turned out, the exact opposite happened. Darwin’s theories were invoked
countless times in the twentieth century as a defense of the free-market system.
Aligning them with the animal world didn’t discredit markets, as Engels had
predicted. It made markets look natural. If Mother Nature made such a
splendidly diverse planet through an algorithm of ruthless competition between
selfish agents, why shouldn’t our economic systems follow the same rules?
Yet the true story of nature is not one of exclusively ruthless competition
between selfish agents, as Darwin himself realized. Origin of Species ends with
one of the most famous passages in the history of science, one that echoes the
journal entry he wrote on leaving the Keeling Islands more than twenty years
before:
It is interesting to contemplate a tangled bank, clothed with many plants
of many kinds, with birds singing on the bushes, with various insects
flitting about, and with worms crawling through the damp earth, and to
reflect that these elaborately constructed forms, so different from each
other, and dependent upon each other in so complex a manner, have all
been produced by laws acting around us . . . Thus, from the war of
nature, from famine and death, the most exalted object which we are
capable of conceiving, namely, the production of the higher animals,
directly follows. There is grandeur in this view of life . . .
Darwin’s words here oscillate between two structuring metaphors that govern
all his work: the complex interdependencies of the tangled bank, and the war of
nature; the symbiotic connections of an ecosystem and the survival of the fittest.
The popular caricature of Darwin’s theory emphasizes competitive struggle
above everything else. Yet so many of the insights his theory made possible have
revealed the collaborative and connective forces at work in the natural world.
We have been living with a comparable caricature in our assumptions about
cultural innovation. Look at the past five centuries from the long view, and one
fact confronts the eye immediately: market-based competition has no monopoly
on innovation. Competition and the profit motive do indeed motivate us to turn
good ideas into shipping products, but more often than not, the ideas themselves
come from somewhere else. Whatever its politics, the fourth quadrant has been
an extraordinary space of human creativity and insight. Even without the
economic rewards of artificial scarcity, fourth-quadrant environments have
played an immensely important role in the nurturing and circulation of good
ideas—now more than ever. In Darwin’s language, the open connections of the
tangled bank have been just as generative as the war of nature. Stephen Jay
Gould makes this point powerfully in the allegory of his sandal collection: “The
wedge of competition has been, ever since Darwin, the canonical argument for
progress in normal times,” he writes. “But I will claim that the wheel of quirky
and unpredictable functional shift (the tires-to-sandals principle) is the major
source of what we call progress at all scales.” The Nairobi entrepreneur selling
sandals in an open-air market may indeed be in competition with other cobblers,
but what makes his trade possible is the junkyard full of tires waiting to be freely
converted into footwear, and the fact that the good idea of converting tires into
sandals can be passed from cobbler to cobbler by simple observation, with no
licensing agreements to restrict the flow.
In 1813, a Boston mill owner, Isaac McPherson, found himself immersed in a
long and frustrating patent dispute with a Philadelphia-based inventor named
Oliver Evans, who had patented an automated grist mill several years before.
Evans’s engineering talent was matched only by his litigiousness. He was
notorious for aggressively enforcing his patents, and was among the first to
exploit the new restrictive powers of the federal patent system after its creation
in 1790. The originality of Evans’s patented invention was highly debatable; the
grist-mill system relied on bucket elevators, conveyor belts, and Archimedean
screws—all of which were clearly innovations that had long been in the public
domain. When Evans sued McPherson for violating his patents, the Boston
industrialist decided to reach out to the first patent commissioner of the United
States, a former politician and inventor himself, now living in rural Virginia.
And so, in the summer of 1813, McPherson wrote a letter to Thomas Jefferson,
asking for his interpretation of Oliver Evans’s claim.
Jefferson wrote back on August 13. Reading his letter now, one cannot help
but be amazed by the range of Jefferson’s intelligence. His focus narrows into
intense technical detail on the specifics of Evans’s invention, and then widens to
their ancient prehistory. (“The screw of Archimedes is as ancient, at least, as the
age of that mathematician, who died more than 2,000 years ago. Diodorus
Siculus speaks of it, L. i., p. 21, and L. v., p. 217, of Stevens’ edition of 1559,
folio; and Vitruvius, xii.”) He reviews the relevant law with the sharp eye of a
legal scholar, opining on the sections that he thinks are fundamentally flawed.
But the most stirring passages arise when Jefferson waxes philosophical on the
nature of ideas themselves:
Stable ownership is the gift of social law, and is given late in the progress
of society. It would be curious then, if an idea, the fugitive fermentation
of an individual brain, could, of natural right, be claimed in exclusive and
stable property. If nature has made any one thing less susceptible than all
others of exclusive property, it is the action of the thinking power called
an idea, which an individual may exclusively possess as long as he keeps
it to himself; but the moment it is divulged, it forces itself into the
possession of every one, and the receiver cannot dispossess himself of it.
Its peculiar character, too, is that no one possesses the less, because every
other possesses the whole of it. He who receives an idea from me,
receives instruction himself without lessening mine; as he who lights his
taper at mine, receives light without darkening me. That ideas should
freely spread from one to another over the globe, for the moral and
mutual instruction of man, and improvement of his condition, seems to
have been peculiarly and benevolently designed by nature, when she
made them, like fire, expansible over all space, without lessening their
density in any point, and like the air in which we breathe, move, and
have our physical being, incapable of confinement or exclusive
appropriation. Inventions then cannot, in nature, be a subject of property.
Ideas, Jefferson argues, have an almost gravitational attraction toward the
fourth quadrant. The natural state of ideas is flow and spillover and connection.
It is society that keeps them in chains.
Does this mean we have to do away with intellectual property law? Of course
not. The innovation track record of the fourth quadrant doesn’t mean that patents
should be abolished and all forms of information allowed to run free. But it
should definitely put the lie to the reigning orthodoxy that without the artificial
scarcity of intellectual property, innovation would grind to a halt. There are
plenty of understandable reasons why the law should make it easier for
innovative people or organizations to profit from their creations. We may very
well decide as a society that people simply deserve to profit from their good
ideas, and so we have to introduce a little artificial scarcity to ensure those
rewards. As someone who creates intellectual property for a living, I am more
than sympathetic toward that argument. But it is another matter altogether to
argue that those restrictions will themselves promote innovation in the long run.
As Lawrence Lessig has so persuasively argued over the years, there is
nothing “natural” about the artificial scarcity of intellectual property law. Those
laws are deliberate interventions crafted by human intelligence and are enforced
almost entirely by non-market powers. Jefferson’s point, in his letter to
McPherson, is that if you really want to get into a debate about which system is
more “natural,” then the free flow of ideas is always going to trump the artificial
scarcity of patents. Ideas are intrinsically copyable in the way that food and fuel
are not. You have to build dams to keep ideas from flowing.
To my mind, the great question for our time is whether large organizations—
public and private, governments and corporations alike—can better harness the
innovation turbine of fourth-quadrant systems. On the private-sector side, the
success of companies like Google and Twitter and Amazon—all of whom have,
in different ways, contributed to and benefited from fourth-quadrant innovation
—has made it clear that, in the software world, at least, a little openness goes a
long way. I suspect those lessons will grow increasingly inescapable in the
decades to come. But it is the public sector that I find more interesting, because
governments and other non-market institutions have long suffered from the
innovation malaise of top-heavy bureaucracies. Today, these institutions have an
opportunity to fundamentally alter the way they cultivate and promote good
ideas. The more the government thinks of itself as an open platform instead of a
centralized bureaucracy, the better it will be for all of us, citizens and activists
and entrepreneurs alike.
The wonderful irony is that this historic opportunity comes to governments in
part because of an innovation that they unleashed on the world: the Internet,
probably the clearest example of the way that public- and private-sector
innovation can complement each other. The generative platform of the Internet
(and the Web) has created a space where countless fortunes have been made
over the past thirty years, but the platform itself was created by the loose
affiliation of information scientists around the world, funded, in large part, by
the federal government of the United States. There are good ideas, and then there
are good ideas that make it easier to have other good ideas. YouTube was a good
idea that was made possible by the even better ideas of the Internet and the Web.
The fact that those idea-generating platforms were developed outside the private
sector is no accident. Proprietary platforms that reach critical mass are not
unheard of—Microsoft Windows has had a good run, for instance, and Apple’s
iPhone platform has been extraordinarily innovative in its first three years—but
they are rarities. Generative platforms require all the patterns of innovation we
have seen over the preceding pages; they need to create a space where hunches
and serendipitous collisions and exaptations and recycling can thrive. It is
possible to create such a space in a walled garden. But you are far better off
situating your platform in a commons.
But perhaps “commons” is the wrong word for the environment we’re trying to
imagine, though it has a long and sanctified history in intellectual property law.
The problem with the term is twofold. For starters, it has conventionally been
used in opposition to the competitive struggle of the marketplace. The original
“commons” of rural England disappeared when they were swallowed up by the
private enclosures of agrarian capitalism in the seventeenth and eighteenth
centuries. Yet the innovation environments we have explored are not necessarily
hostile to competition and profit. More important, however, the commons
metaphor doesn’t suggest the patterns of recycling and exaptation and
recombination that define so many innovation spaces. When you think of a
commons, you think of a cleared field dominated by a single resource for
grazing. You don’t think of an ecosystem. The commons is a monocrop
grassland, not a tangled bank.
I prefer another metaphor drawn from nature: the reef. You need only survey a
coral reef (or a rain forest) for a few minutes to see that competition for
resources abounds in this space, as Darwin rightly observed. But that is not the
source of its marvelous biodiversity. The struggle for existence is universal in
nature. The few residents of a desert ecosystem are every bit as competitive as
their equivalents on a coral reef. What makes the reef so inventive is not the
struggle between the organisms but the way they have learned to collaborate—
the coral and the zooxanthellae and the parrotfish borrowing and reinventing
each other’s work. This is the ultimate explanation of Darwin’s Paradox: the reef
has unlocked so many doors of the adjacent possible because of the way it
shares.
The reef helps us understand the other riddles we began with: the runaway
innovation of cities, and of the Web. They, too, are environments that
compulsively connect and remix that most valuable of resources: information.
Like the Web, the city is a platform that often makes private commerce possible
but which is itself outside the marketplace. You do business in the big city, but
the city itself belongs to everyone. (“City air is free air,” as the old saying goes.)
Ideas collide, emerge, recombine; new enterprises find homes in the shells
abandoned by earlier hosts; informal hubs allow different disciplines to borrow
from one another. These are the spaces that have long supported innovation,
from those first Mesopotamian settlements eight thousand years ago to the
invisible layers of software that support today’s Web.
Ideas rise in crowds, as Poincaré said. They rise in liquid networks where
connection is valued more than protection. So if we want to build environments
that generate good ideas—whether those environments are in schools or
corporations or governments or our own personal lives—we need to keep that
history in mind, and not fall back on the easy assumptions that competitive
markets are the only reliable source of good ideas. Yes, the market has been a
great engine of innovation. But so has the reef.
Most of us, I realize, don’t have a direct say in what macro forms of
information and economic organization prevail in the wider society, though we
do influence that outcome indirectly, in the basic act of choosing between
employment in the private or the public sector. But this is the beauty of the longzoom perspective: the patterns recur at other scales. You may not be able to turn
your government into a coral reef, but you can create comparable environments
on the scale of everyday life: in the workplaces you inhabit; in the way you
consume media; in the way you augment your memory. The patterns are simple,
but followed together, they make for a whole that is wiser than the sum of its
parts. Go for a walk; cultivate hunches; write everything down, but keep your
folders messy; embrace serendipity; make generative mistakes; take on multiple
hobbies; frequent coffeehouses and other liquid networks; follow the links; let
others build on your ideas; borrow, recycle, reinvent. Build a tangled bank.
Acknowledgments
Given that this is a book in part about the generative power of slow hunches, it
should come as no surprise that the topic has been lingering in my mind for
almost a decade now, ever since I designed an elaborate experiment, for my
book Mind Wide Open, to scan my brain as it was trying to come up with a good
idea in an FMRI machine. For the past four years, after I began work on the
project in earnest, I have consciously thought of this book as the closing volume
in an unofficial trilogy that began with The Ghost Map and The Invention of Air,
both books about world-changing ideas and the environments that made them
possible. (In a sense, you can think of this book as the latent theory lurking
behind those more focused narrative case-studies.) So I am grateful to the many
provocative and serendipitous responses I received from readers, critics, and
listeners regarding those two earlier books, many of which opened doors to new
rooms that I have greatly enjoyed exploring.
I want to extend particular thanks to several organizations that supported me
during the writing of the book, starting with my colleagues at outside.in, led by
Mark Josephson, who have tolerated the eccentric schedule of an
author/executive chairman with graciousness and true friendship. Thanks to
Columbia University’s Journalism School for appointing me the Hearst New
Media Scholar in Residence and for providing a forum where I could talk about
commonplace books, my grad school days, and the iPad in a single lecture.
Thanks to the wonderful SXSW festival for inviting me to talk about the
ecosystem of news in the spring of 2009, as the ideas for this book were starting
to come together. My editors at Time, Wired, The Wall Street Journal, and The
New York Times—particularly Rick Stengel, Alex Star, James Ryerson, Tim
O’Brien, Chris Anderson, and Larry Rout—allowed me to work through some of
these ideas (and sentences) in public, and provided insightful comments along
the way. (My former editors at Discover, Stephen Petranek and David Grogan,
helped me cultivate some of these themes several years ago during my tenure as
a columnist there.)
As usual, the team at Riverhead has been a great help to me, both in believing
in this idea in its embryonic state, and in allowing me to follow my serendipitous
discoveries of both outside.in and Joseph Priestley. Sean McDonald and Geoff
Kloske were incredibly patient when my hunch turned out to be even slower
than originally forecast, and when the book finally began to come together, they
did amazing work turning it into a finished product. I’m also grateful to Matthew
Venzon, Emily Bell, Hal Fessenden, Helen Conford, and my lecture agents at the
Leigh Bureau for their support. My research assistant, Chris Ross, was
enormously helpful in collaborating on our maps of innovation history. Once
again my agent, Lydia Wills, displayed her extraordinary knack for encouraging
my good ideas, and delicately shooting down the idiotic ones.
I am particularly grateful to the people who read parts or all of the manuscript
in draft form: Brent Constantz, Charlane Nemeth, Brian Eno, John Wilbanks,
and in particular Ray Ozzie, Carl Zimmer, and Scott Berkun, and my favorite
line editor, Alexa Robinson. They offered many improvements to the ideas
contained in this book. What errors remain are entirely my responsibility. It’s up
to you to decide whether they prove to be generative ones.
Brooklyn, NY
May 2010
Appendix: Chronology of Key Innovations, 1400-2000
DOUBLE-ENTRY ACCOUNTING (1300—1400)
First codified by the Franciscan friar and mathematician Luca Pacioli in 1494,
the double-entry method had been used for at least two centuries by Italian
bankers and merchants. Some evidence suggests that the technique was
developed by Islamic entrepreneurs who passed it on to the Italians through the
trade hubs of Venice and Genoa.
PRINTING PRESS (1440)
While elements of the printing press, including the concept of movable type,
date back to earlier Chinese and Korean inventors, the first true printing press
that combined the screw press and metallic movable type was created by
Johannes Gutenberg circa 1440.
CONCAVE LENS (1451)
Humans have used lenses to magnify images and to start fires for thousands of
years, but the first use of a concave lens to treat myopia is attributed to the
polymath German cardinal Nicholas of Cusa.
PARACHUTE (1483)
Leonardo da Vinci sketched the original design for a parachute in 1438 in the
margin of a notebook. The first physical test of the design occurred in 1783,
when Louis-Sébastien Lenormand leapt from the Montpelier Observatory in
France and, with the aid of his primitive parachute, landed without injury. In
2000, an exact replica of da Vinci’s parachute was constructed and tested, and
proved to function.
TERRESTRIAL GLOBE (1492)
The Nuremberg-based mapmaker Martin Behaim constructed the first terrestrial
globe in the early 1490s, after returning from extensive journeys in West Africa.
He called it the Erdapfel, which translates to “earth apple.”
BALL BEARINGS (1497)
Conceived and sketched by Leonardo da Vinci in 1497 as a method to reduce
friction; the first patent for ball bearings was awarded to Philip Vaughan in
1794.
PORTABLE WATCHES (1500)
One of the canonical examples of collective invention, portable watches evolved
out a group of clockmakers in Nuremberg in the early 1500s, led by one Peter
Henlein, who created the first lightweight watch. Heinlen’s watch was portable,
but not terribly accurate; subsequent improvements by his Nuremberg peers
allowed the device to keep better time.
EARTH ROTATES AROUND SUN (1514)
Nicolaus Copernicus first wrote out his “heliocentric” theory of the solar system
as a small pamphlet around 1514, but did not formally publish the idea for more
than twenty years, for fear of the controversy it would unleash. Word of his
radical theory leaked out and began spreading through the enlightened minds of
Europe during that period, but the first official publication came in his
posthumous text, On the Revolutions of the Heavenly Spheres, published in
1543.
SQUARE ROOT AND PLUS AND MINUS SYMBOLS
(1525)
German mathematician Christoph Rudolff invented the modern mathematical
symbols “+” and “−” and “√” in Coss, the first comprehensive guide to algebra
in German in 1525.
CUBIC EQUATIONS AND COMPLEX NUMBERS
(1530-1540)
The mathematicians of the Islamic Renaissance published several important
papers on the understanding of cubic equations—along with the notion of
complex numbers—which are essential to determining the area and volume of
objects. But the modern technique for solving them is most prominently
associated with the Italian mathematician and engineer Niccolò Tartaglia, who
won a famous contest in 1530 that showcased his approach. Two other Italians
from that period, Scipione del Ferro and his student Antonio Fiore, contributed
to the math as well.
PULMONARY RESPIRATION (1535)
The Spanish religious radical Michael Servetus made the first convincing case
that the aeration of blood took place in the lungs, after studying the size of the
pulmonary artery as a medical student at the University of Paris.
ETHER (1540)
German botanist Valerius Cordus discovered and described a radically new
method for the synthesis of ether in 1540, calling it “the sweet oil of vitriol.”
At around the same time, Swiss physician Paracelsus discovered the
anaesthetizing properties of ether.
STEAM TURBINE (1551)
The brilliant Turkish polymath Taqi al-Din described a functioning steam
turbine, designed to power a rotating spit, in his wonderfully titled 1551 opus,
The Sublime Methods in Spiritual Devices.
PENCIL (1560)
In the mid 1560s, the residents of a small village in England’s Cumbria region
stumbled across a massive deposit of graphite. The community first began using
the substance to mark their cattle and sheep, and ultimately hit upon wrapping a
wood casing around the graphite. It would take another two hundred years for
the device to be completed with the invention of the eraser.
MERCATOR MAP PROJECTION (1569)
Flemish mapmaker Gerard Mercator developed the Mercator projection, a
cartographical depiction of the world that allowed navigators to follow rhumb
lines between two locations, thus accounting for compass bearing.
SUPERNOVAS AND COMETS (1572—1577)
The Danish nobleman Tycho Brahe’s observation of a new star forming in 1572,
and his detailed proof that the supernova was not changing position relative to
other stars, undermined the prevailing orthodoxy that held that the heavens were
incapable of change. Several years later, Brahe’s equally precise observations of
a comet showed that the object was farther away from the moon, and thus not
part of earth’s atmosphere.
STOCKING FRAME (1589)
English clergyman William Lee created the first working version of a stocking
frame, a mechanical knitting machine used in the textile industry to mimic the
motions of hand-knitting. Following the inventor’s death, one of his assistants
made a number of improvements on the device that much improved its
functionality.
COMPOUND MICROSCOPE (1590)
Though a definite consensus does not exist on who invented the compound
microscope, most historians credit either the Dutch spectacle maker Zacharias
Janssen and his son Hans, or the German optician Hans Lippershey. In 1609
Galileo re-formed Janssen’s original design into a more efficient machine. In the
1670s, Antoni van Leeuwenhoek first applied the microscope to the field of
biology.
FLUSH TOILET (1596)
A water flushing device was invented in the late sixteenth century by Sir John
Harrington, who installed a functioning version for his godmother, Queen
Elizabeth, at Richmond Palace. But the device didn’t take off until the late
1700s, when a watchmaker named Alexander Cumings and a cabinet-maker
named Joseph Bramah filed for two separate patents on an improved version of
Harrington’s design.
PLANETARY MAGNETISM (1600)
English scientist William Gilbert realized that the earth itself was a magnet, a
discovery first published in his treatise “On the Magnet” in 1600. Gilbert
concluded that it was the earth’s magnetic nature that allowed the compass to aid
navigators. The nature of magnets had been studied by Aristotle and the ancient
Chinese among others throughout history.
TELESCOPE (1600--1610)
A classic example of collective invention, the first telescopes and spyglasses
began to appear in Europe in the first decade of the seventeenth century. Two
patent applications were filed on designs in the Netherlands in 1608, and by
1609 Galileo was using a device he built with 20x magnification to gaze at the
stars, discovering Jupiter’s moons in the process.
ELLIPTICAL ORBITS (1605--1609)
The German astronomer and mathematician Johannes Kepler was the first to
document the elliptical orbit that the planets took around the sun, though he built
his equations by analyzing data collected by Tycho Brahe, his friend and
occasional employer.
JUPITER’S MOONS (1610)
With the aid of a telescope, Galileo Galilei first observed the orbiting moons of
Jupiter, thus proving the fundamental principle of the Copernican system, that
the universe did not revolve around earth. Another scientist, Simon Marius,
claimed to have discovered the moons five weeks prior to Galileo, but he never
published his observations.
FLINTLOCK (1610)
French courtier Marin le Bourgeoys introduced the first fully developed flintlock
mechanism to King Louis XIII in 1610, a device that became standard in
firearms until the early nineteenth century. But Marin le Bourgeoy’s discovery
integrated many early innovations in firing mechanisms, from the matchlock to
the snaphance.
SUNSPOTS (1610)
SUNSPOTS (1610)
Sunspots, darkened, magnetic spots on the surface of the sun, were first observed
almost simultaneously by a number of astronomers with the use of telescopes.
Credit is alternately attributed to Galileo Galilei, Thomas Harriot, and Johannes
and David Fabricius.
LOGARITHMS (1614)
In an effort to simplify the process of multiplying large numbers, mathematician
John Napier conceived of logarithms as a way to express a number as a base
raised to a power, e.g., 100 as 10
2
, or 10 × 10. Logarithms have gone on to play
an essential role in science and engineering.
BLOOD CIRCULATION (1628)
English physician William Harvey correctly theorized the movement of blood
through the human body as pumped by the heart and cycled perpetually,
dispelling earlier arguments for the existence of two separate circulation
systems.
VERNIER SCALE (1631)
The Vernier scale, invented by French mathematician Pierre Vernier, can be
used in conjunction with a larger scale to precisely measure extremely small
units of space. It became widely employed in navigation systems.
OCEAN TIDES (1632)
Following in the steps of the ancients, Galileo Galilei ventured an explanation of
ocean tides in relation to the sun. Johannes Kepler correctly theorized that it was
the earth’s relation to the moon that created the phenomenon, and Isaac Newton
furnished the scientific community with a fully developed explanation in 1687.
SLIDE RULE (1632)
William Oughtred is commonly credited with inventing the earliest version of
the slide rule, two parallel logarithmic scales that one could slide in relation to
each other to conduct advanced calculations easily and quickly. Oughtred
improved upon the design of a more basic model developed by Edmund Gunter
as well as earliest conceptions by Galileo Galilei and John Napier.
LAW OF FALLING BODIES (1634)
For at least two thousand years, the Aristotelian consensus held that heavier
bodies fall faster than lighter ones, until Galileo devised several ingenious
experiments and formulated a mathematical equation to describe what we now
call uniform acceleration. While several observational accounts predate Galileo’s
work, his account was the first definitive proof.
ANALYTIC GEOMETRY (1637)
French philosopher and mathematician René Descartes invented the system now
known as analytic geometry as a way to express geometric shapes and properties
with a coordinate system. By translating geometric structures, both two and three
dimensional, into numerical representations, mathematicians could study and
investigate them algebraically. Analytic geometry would later form one of the
foundations of Isaac Newton’s development of calculus.
BAROMETER (1643)
The barometer, a device designed to measure air pressure, grew out of Italian
physicist Evangelista Torricelli’s efforts to aid his mentor, Galileo, in an attempt
to help miners pump water out of wells. While working with mercury, a heavier
liquid than water, Torricelli discovered that variations in the height of mercury
trapped in a tube from day to day were due to changes in the air’s atmosphere.
However, historians speculate that mathematician Gasparo Berti may have
unwittingly invented a barometer a few years earlier.
MECHANICAL CALCULATOR (1645)
French mathematician and philosopher Blaise Pascal invented what is now
called Pascal’s Calculator, one of the most important precursors to the modern
calculator, a device that could add and subtract through the use of spinning metal
wheels stamped with numbers 0 through 9. While Pascal was the first to present
his fully functioning invention to the public, a similar device had been conceived
and developed by German Wilhelm Schickard, based on work by John Napier.
VACUUM PUMP (1654)
Like the barometer, the vacuum pump grew out of scientists’ efforts to improve
upon the capabilities of a suction pump. Through a series of experiments, Otto
von Guericke discovered that it was possible to extract air or water from a sealed
container, creating a vacuum. He demonstrated this principle before Emperor
Ferdinand III by showing that two horses could not pull apart two bowls
between which a vacuum had been created. Von Guericke drew on the work of
Evangelista Torricelli, and his own work was improved upon by Robert Boyle
and Robert Hooke.
PENDULUM CLOCK (1656)
Once again building on the ideas of Galileo, Dutch scientist Christiaan Huygens
invented the most accurate clock to date by utilizing the regular oscillations of a
weighted pendulum, regulated slightly by a mechanical device.
BALANCE SPRING WATCHES (1660)
Vastly improving upon the accuracy of earlier timepieces, a balance spring
mechanism controlled the speed of the separate pieces of a watch with the help
of a regulator, which ensured that the whole mechanism remained as consistent
as possible. Robert Hooke and Christiaan Huygens are both credited with the
invention—Thomas Tompion engineered the most effective regulator of the time
around 1680.
BOYLE’S LAW (1662)
Boyle’s law, developed by scientist Robert Boyle, states that given a fixed
temperature and a closed system, the pressure and volume of gas will remain
inversely proportionate; i.e., as one decreases, the other increases, in
proportionate degrees. Boyle’s assistant Robert Hooke assisted in the discovery
of this law. French chemist Edme Mariotte discovered the same principle at
roughly the same time, but Boyle published it first.
LIGHT SPECTRUM (1665)
Correcting earlier views that prisms colored light, Sir Isaac Newton
demonstrated through a series of experiments that a ray of sunlight, shined
through a prism, contained colors and was not colored by the prism, which only
split the ray into its constituent parts. By isolating one color expressed by a
prism, and shining it through yet another prism, Newton showed that the color
remained consistent, and that the prism did not affect the shade.
MICROORGANISMS (1674--1680)
Thanks in part to his own improvements to the technology of the microscope, the
Dutch scientist Antoni Philips van Leeuwenhoek was the first person to directly
observe single-celled organisms, called “animalcules” at the time.
SPEED OF LIGHT (FIRST QUANTITATIVE
MEASURE) (1676)
While Galileo had been able to establish that light traveled faster than sound,
Danish astronomer Olaus Roemer, trying to account for disparities in his
observations of eclipses, realized that the culprit was the amount of time light
took to travel through space. By dint of advanced astronomical calculations,
Roemer was able to approximate a speed of light not far off from modern
estimates.
HOOKE’S LAW (1676)
Otherwise known as the law of elasticity, English scientist Robert Hooke
discovered that the displacement or deformation of an object was proportionate
to the amount of force exerted upon it—in other words, a spring stretches in
proportionate amount to the degree of stress placed on it, before resuming its
original shape.
PRESSURE COOKER (1679)
French physicist Denis Papin invented what he termed a steam digester—a
sealed device containing liquid, which, when heated, created pressure within the
closed unit, therefore raising the boiling point of the liquid, allowing for faster
cooking times.
CALCULUS (1684, 1693)
Though the principles of modern calculus had been noted through the centuries,
most historians credit Isaac Newton and Gottfried Wilhelm Leibniz with
systematizing the methods and principles on a larger scale than had ever before
been accomplished. Broadly described as a branch of mathematics that explains
the principles of physics, Newton and Leibniz both lay claim to its invention,
though history has since shown that both mathematicians arrived at many of the
same conclusions independently, though with different systems of notation.
LAW OF UNIVERSAL GRAVITATION (1686)
While the story of Newton’s apple may be the canonical example of private
inspiration, the actual origins of the law are much murkier, including a famous
battle between Robert Hooke and Newton over who first noted the inverse
square relationship that governed the gravitational attraction between two
objects.
THREE LAWS OF MOTION AND ORBITS OF
COMETS (1687, 1705)
Newton’s three laws of motion were first published in his groundbreaking
Philosophioe Naturalis Principia Mathematica in July 1687. Newton’s friend
and publisher, Edmund Halley, would then rely on those laws in producing the
first accurate prediction of a comet’s orbit around the earth.
PIANO (1700S)
Employed by the Medici court, Bartolomeo Cristofori sought to improve upon
the harpsichord and clavichord by creating a similar instrument that would allow
the player both expressive control and a larger spectrum of volume. He called it
a “pianoforte,” which has since been shortened to “piano.”
TUNING FORK (1711)
Designed by the British musician John Shore, the tuning fork, or “pitch-fork,”
produced a very pure tone by which instruments could be accurately tuned.
STEAM ENGINE (1712)
Expanding upon the earlier, more primitive inventions of Denis Papin and
Thomas Savery, Thomas Newcomen, an English blacksmith, utilized
atmospheric pressure to propel a piston upward and downward by condensing
steam, allowing an engine to pump water out of wells. It was the first
commercially successful device of its kind.
MERCURY THERMOMETER (1714)
While crude thermometers were conceived by both Galileo Galilei and Isaac
Newton, German physicist Daniel Gabriel Fahrenheit invented the first fully
functioning mercury thermometer: a glass tube containing mercury that
registered temperature according to the degree of heat applied to it, demarcating
both the boiling and freezing temperatures of water.
OCTANT (1730)
Invented at about the same time but independently by Thomas Godfrey and John
Hadley, the octant was a navigational device that spanned 45 degrees and, with
the help of attached mirrors and a small telescope, could allow sailors to orient
themselves at sea.
FLYING SHUTTLE (1733)
An invention that helped spur the Industrial Revolution, the flying shuttle was a
device that sped up the process of weaving with a loom and required less
manpower. The device did not become widely used until after inventor John
Kay’s death.
LINNEAN TAXONOMY (1735)
While the modern taxonomic scheme for organizing life still bears the name of
the Swedish botanist and zoologist Carl Linnaeus, his model built on
classificatory systems that had been evolving for hundreds of years. But
Linnaeus did make several essential additions, most importantly the practice of
naming each organism using a binomial structure, as in homo sapiens.
CHRONOMETER (1735)
Though countless versions of a chronometer had been developed since the early
sixteenth century, the most fully realized device was created by the carpenter
Thomas Harrison. The chronometer allowed navigators at sea to determine
longitude and latitude by providing an accurate representation of time at a
particular location.
LIGHTNING ROD (1750)
Ben Franklin first proposed the idea of a lightning rod in a letter written in 1750,
and his descriptions were ultimately translated into French. The first test of
Franklin’s theoretical design was actually implemented in France in 1752.
SPINNING JENNY (1764)
A longstanding debate questions whether James Hargreaves was the true
inventor of the spinning jenny, a machine that greatly improved the efficiency of
the cotton industry. Some evidence suggests that Hargreaves was merely
improving the design of an artisan named Thomas Highs. What is clear is that
the Hargreaves design was greatly improved upon in the years following the
production of his first model by weavers throughout Northern England.
CARBONATED WATER (1767)
Clergyman Joseph Priestley discovered that by charging water artificially with
carbon dioxide, he could create an effervescent beverage, known today as
seltzer. Though Priestley never capitalized on the business opportunities, many
inventors after him did.
PHOTOSYNTHESIS (1770--1800)
Most commonly associated with the Austrian physician Jan Ingenhousz, the
mechanism of photosynthesis was uncovered piece by piece over a thirty-year
period, starting in 1770 with a series of experiments and essays written by
Joseph Priestley. The cycle of carbon dioxide consumption and oxygen release,
triggered by sunlight, wasn’t fully formulated until the mid-1790s by the Swiss
naturalist Jean Senebier.
PLANT RESPIRATION (1772—1773)
While Joseph Priestley is conventionally associated with the isolation of oxygen,
he deserves more recognition for his discovery of plant respiration, circa 1773,
which he collaborated on via post with his good friend Benjamin Franklin.
OXYGEN (1772--1776)
One of the great stories of scientific collaboration and rivalry, oxygen was
isolated by three scientists in the mid-1770s: the Swedish chemist Carl Wilhelm
Scheele; the British polymath Joseph Priestley, who named it “dephlogisticated
air,” after the reigning, and inaccurate, theory of phlogiston; and Antoine
Lavoisier, who gave the element its name.
BIFOCALS (CIRCA 1780)
While the exact date of his invention in unclear, by the mid-1780s, Benjamin
Franklin was writing to friends about how happy he was with his “invention of
double spectacles, which serving for distant objects as well as near ones, make
my eyes as useful to me as they ever were.”
STEAMBOAT (1780--1810)
Robert Fulton is conventionally heralded as the inventor of the steamboat, but in
fact Fulton was merely the first to turn the steamboat into a commercial success.
A number of working steamboats had been built by engineers like John Fitch
and James Rumsey over the preceding two decades.
MANNED HOT AIR BALLOON (1783)
While hot air balloons date back to first-century A.D. Chinese culture, the first
manned flight was designed by the French entrepreneurs Joseph-Michel and
Jacques-Etienne Montgolfier.
MILKY WAY (1785)
Many astronomers and scientists, including AbūRayhān al-Bı̄rūnı̄and Galileo,
contributed to the notion of the Milky Way as a collection of stars, but the first
attempt to map the shape of the Milky Way was executed by William Herschel
and his sister Caroline in 1785.
POWER LOOM AND COTTON GIN (1785, 1793)
The English clergyman Edmund Cartwright patented a power loom design in
1785, but, like Eli Whitney’s cotton gin, the device relied on many subsequent
improvements by other engineers for it to revolutionize the textile industry.
SMALLPOX VACCINE (1796)
SMALLPOX VACCINE (1796)
The process of inoculating humans with small doses of the smallpox virus,
usually using scabs from the skin of a victim, was practiced widely through
China, Persia, and Africa after 1500. But the British scientist Edward Jenner was
the first to design a vaccine based on a related cowpox virus that produced
immunity to smallpox with much lower mortality.
LITHOGRAPHY (1796)
A playwright, Alois Senefelder struggled to find a way to distribute his writings
cheaply—he eventually discovered that he could etch on a copper plate with acid
and a needle. He improved this method, using the same fundamental idea, and
called it “stone printing,” which soon spread throughout Europe and the United
States.
ELECTRIC BATTERY (1800)
The Italian count Alessandro Volta created the first battery out of zinc and
copper discs, inspired by an argument with his peer Luigi Galvani, who believed
that electricity emerged out of animal tissue.
ATOMIC THEORY (1800--1810)
While it drew heavily from the revolution in chemistry spearheaded by Antoine
Lavoisier, the first rigorous argument that elements were made up of unique
atoms of a distinct character was put forth by the English chemist John Dalton
the first decade of the nineteenth century.
MOLECULES (1800--1810)
The notion that atoms form larger compound units, the most elemental of which
is a molecule, was first formulated in the decades around 1800, and drew upon
the related theories of the French chemist Joseph Proust, John Dalton, and the
Italian count Avogadro.
SUSPENSION BRIDGE (1800--1830)
While numerous crucial improvements were added over the first half of the
nineteenth century, the first functioning suspension bridge large enough to
transport humans and horses was the Jacob’s Creek Bridge, built in the early
1800s by the American judge and amateur engineer James Finley.
STEAM LOCOMOTIVE (1805)
A number of engineers built working steam-powered vehicles, some designed to
run on roads, some on rails, in the last decades of the eighteenth century, though
historians generally consider the train designed by Richard Trevithick in Wales
in 1805 to be the first fully functioning steam locomotive.
PUNCH CARDS (1805)
The idea of using punch cards for programming mechanical looms is generally
credited to Joseph Marie Jacquard, but weavers in the early 1700s, including
Basile Bouchon and Jean Falcon, experimented extensively with punch card
control of warp threads.
SPECTROSCOPE (1814)
German lenscrafter Joseph von Fraunhofer invented the spectroscope, a device
that measures properties of light, in order to study dark lines occurring in various
forms of spectra, which he later discovered were areas of the spectrum where
light is absorbed.
STETHOSCOPE (1816)
A French physician named René Laennec invented the stethoscope after
improvising one with a roll of paper while treating a woman suffering from heart
disease.
BICYCLE (1817--1863)
The first two-wheeled, steerable vehicle was designed by a German baron named
Karl von Drais, and mimicked by dozens of entrepreneurs throughout Europe in
the following decade. But it wasn’t until the 1860s that pedals and rotary cranks
were added to the device.
BRAILLE (1821)
Louis Braille, a blind, French fifteen-year-old, invented Braille—a tactile form
of reading—by improving upon a more rudimentary system of raised bump,
tactile text (night-writing) conceived by an army captain, Charles Barbier.
ELECTRIC MOTOR (1821--1850)
More than a dozen scientists and entrepreneurs contributed to the design of the
electric motor in the first half of the eighteenth century, beginning with the
English chemist and physicist Michael Faraday’s demonstration, in 1821, of a
system for converting electrical energy into mechanical energy.
SECOND LAW OF THERMODYNAMICS (1824)
The second law of thermodynamics, which evolved over the years in the hands
of various scientists, including Sadi Carnot and Rudolf Clausius, states a theory
of universal entropy that invalidates the possibility of perpetual motion
machines.
GEOLOGICAL UNIFORMITARIANISM (1830)
The idea that the geological state of the earth was based on consistent forces
acting over very long time scales is largely attributed to Charles Lyell’s
Principles of Geology, published in 1830, though the term itself comes from a
review of Lyell’s book written by William Whewell. Lyell’s ideas would
subsequently form the platform on which Darwin based his biological theory of
evolution.
CHLOROFORM (1831)
Chloroform, a colorless, organic compound, was discovered at about the same
time by three different scientists in three different countries—Eugene Soubeiran,
Samuel Guthrie, and Justus von Liebig. It was used as a treatment for asthma
and as a powerful alternative to ether as an anesthetic.
REFRIGERATOR (1834)
After acquiring a patent for a vapor-compression refrigeration system,
mechanical engineer Jacob Perkins invented the first practical refrigerator in
1834, though an earlier refrigeration machine had been invented by American
inventor Oliver Evans in 1805.
REVOLVER (1836)
Improving upon the flintlock firing mechanism, in 1836 American inventor
Samuel Colt designed and patented the revolver, a handgun that featured a
rotating cylinder with multiple chambers for bullets.
PROGRAMMABLE COMPUTER (1837)
Although a working version was never built, Charles Babbage outlined the basic
principles of the programmable computer—including the notions of what we
now call software, CPU, and memory—in his legendary Analytical Engine,
which he first published a description of in 1837. Lord Byron’s daughter Ada
Lovelace wrote the first computer algorithm for the device.
TELEGRAPH (1838)
In an effort to improve clumsier, five-wire models of the telegraph, inventor
Samuel Morse and his assistant Alfred Vail created a one-wire model that used
electric signals to shift an electromagnet in a patterned print across paper, known
as Morse code.
PHOTOGRAPHY (1839)
Most historians credit French chemist Louis Daguerre with developing the first
practical photographic process, which involved fixing images on copper places
covered in a chemical substance by exposing them to light. Daguerre’s’s
methods were deeply influenced by the innovations of Frenchman Joseph
Nicephore Niépce.
VULCANIZED RUBBER (1839)
After years of trial and error, Charles Goodyear discovered vulcanized rubber—
which unlike natural rubber, maintained its shape despite exposure to pressure
and heat—almost by accident, and fought for the rest of his life to claim
royalties on the product. Not long after Goodyear’s discovery, Thomas Hancock
beat him to the patent.
SEWING MACHINE (1845)
The invention of the modern, practical sewing machine was largely due to the
individual innovations of two men, American mechanic Elias Howe, who
developed the machine’s lockstitch mechanism, and American inventor Isaac
Singer, who pioneered the vertical motion mechanism for the needle. The two
men would clash over credit for the invention.
NITROGLYCERINE (1846)
Working as an assistant to professor J. T. Pelouze, Ascanio Sobrero first
discovered and synthesized nitroglycerine—aware of its explosive potential,
Sobrero warned against incautious use of the chemical and at times even seemed
to regret its discovery.
ABSOLUTE ZERO (1848)
Drawing on the work of earlier scientists studying temperature, Kelvin
developed absolute zero, which forms the lowest point of his Kelvin scale,
representing the point at which all matter ceases to move—roughly 273.15º C.
PIG IRON/STEELMAKING (1850--1860)
Though the process of making steel would continue to be improved for years
after Henry Bessemer’s innovations, the American inventor discovered the first
means to mass-producing steel. By oxidizing pig iron, Bessemer was able to
manufacture comparably high-quality steel in large quantities, eventually aiding
the construction of skyscrapers.
ELEVATOR (1853)
While rudimentary versions of “lifts” had existed since the Middle Ages,
American inventor Elisha Otis sparked wide public use of such machines in
1853 by developing a safety brake, following the introduction of steam and
hydraulic elevators around 1850.
ASPIRIN (1897)
While the pain-relieving properties of willow bark, whose medicinal quality
derived from the tree’s salicin, had been understood and prescribed since
Hippocrates, the drug’s use had been plagued by side effects, primarily stomach
pains. French chemist Charles Gerhardt discovered that adding sodium and
acetyl chloride assuaged the intestinal irritation, making for a better drug.
BUNSEN BURNER (1856)
German chemist Robert Bunsen developed the burner in order to carry out
experiments on spectral emissions of elements, for which the technology did not
yet exist. Stymied by the weak gas burners of the day, Bunsen produced a burner
with an incredibly hot and nearly invisible flame, and it became the standard
laboratory burner many still use today.
MASON JAR (1858)
Improving upon the inefficient jars commonly used at the time, tinsmith John L.
Mason invented a type of jar that would one day bear his name: a blocky glass
container with a screw top and rubber lining to create an airtight seal. The
Mason jar became essential in preserving perishable goods.
LEAD-ACID BATTERY (1859)
French physicist Gaston Plante invented the first rechargeable battery while
experimenting with the conductive power of rolled sheets of lead and sulfuric
acid.
NATURAL SELECTION (1859)
Natural selection was first formulated by Charles Darwin in the late 1830s,
though he did not publish his ideas until 1859 in his book The Origin of Species,
after being spurred on by the very similar theories that had been independently
developed by the British naturalist Alfred Russel Wallace.
GATLING GUN (1861)
Laboring under the belief that a revolving machine gun would create less
bloodshed on battlefields by reducing the number of soldiers needed, inventor
Richard Gatling created the Gatling gun, a hand-cranked continuously and
rapidly firing weapon drawn on two wheels.
VACUUM CLEANER (1861)
Though many inventors created versions of what we know today as a vacuum
cleaner in the late nineteenth century and early twentieth century, Ives W.
McGaffey patented the first manually powered vacuum cleaner—or “sweeping
machine”—in 1861, marketing the device’s ability to clean carpets.
PLASTIC (1862)
British metallurgist Alexander Parkes developed the first major commercial
man-made plastic—a synthetic material made from cellulose and treated with
nitric acid—and debuted it at the 1862 World’s Fair in London. Improvements
were made on the material through the nineteenth and twentieth centuries.
GERM THEORY (1862)
While the idea that germs carried contagious disease was not new and had been
proposed before, French chemist Louis Pasteur was one of the first to develop
experiments to prove the theory conclusively.
DYNAMITE (1863)
Seeking to develop new methods for blasting rock more effectively, Swedish
industrialist Alfred Nobel built on his experiments with nitroglycerin and
invented a detonator that used a strong shock to spark explosions, which he
patented in 1863.
PERIODIC TABLE (1864)
In 1864, Russian chemist Dmitri Mendeleev developed upon earlier notions of
British chemist John Newlands that chemical elements could be arranged in a
pattern according to their atomic masses, providing a more comprehensive chart
with a focus on recurring trends in properties.
DISCOVERY OF BENZENE STRUCTURE (1865)
Following the discovery of benzene in 1825, German chemists Joseph
Loschmidt and August Kekule von Stradonitz theorized a similar structure of the
organic chemical compound—a ring of six carbon atoms with alternating single
and double bonds. Kekule’s discovery was inspired by his legendary dream of
the tail-swallowing serpent.
HEREDITY (1865)
The idea that parents pass certain hereditary qualities to their offspring was
originated by Augustinian monk and scientist Gregor Mendel from his work on
plants, though his principles were synthesized into a wider theory of genetics by
Thomas Hunt Morgan in the early twentieth century.
TYPEWRITER (1868)
After the invention of an inefficient typographer machine in 1829, American
inventor Christopher Latham Sholes patented the first practical typewriter in
1868 with the help of his associates, pioneering a type-bar system and the
QWERTY arrangement of keys to avoid jamming.
TELEPHONE (1876)
The patent for the invention of the telephone was a hotly contested item, leading
to a last-minute race to the patent office between American engineer Alexander
Graham Bell and American electrical engineer Elisha Gray. Bell ultimately
received the patent for the device, which transmitted voice signals electrically.
ENZYMES (1878)
First named by German doctor Wilhelm Kühne in 1878, enzymes—proteins that
act as catalysts for chemical reactions by speeding up the process—were more
fully understood due to the studies of German chemist Eduard Buchner and
French chemist Louis Pasteur.
LIGHTBULB (1879)
By using electricity to heat a filament, causing it to glow and create light,
American inventor Thomas Alva Edison is often considered the inventor of the
lightbulb, replacing gas lighting as the main source of illumination. But Edison’s
work built on the designs of at least a half dozen other inventors who went
before him, including Joseph Swan and William Sawyer.
CELL DIVISION (1879)
The discovery of cell division, the process known as mitosis among eukaryotes
in which a parent cell divides into daughter cells, was the joint discovery of
German biologist Walther Flemming, Eduard Strasburger, and Edouard van
Beneden.
SEISMOGRAPH (1880)
Hired by the Japanese government to study tremors and earthquakes, three
British scientists worked on creating a device that could measure and classify the
strength of earthquakes, now known as a horizontal seismograph, characterized
by its use of a pendulum. Of the three, John Milne generally receives the lion’s
share of credit for the invention.
INFANT INCUBATORS (1881)
Inspired by the use of an incubator for baby birds, French obstetrician Étienne
Stéphane Tarnier began putting infant incubators—heated cribs for newborns—
into regular practice in hospitals. The original designs for the infant incubator
were created by French surgeon Jean-Louis-Paul Denucé and German
gynecologist Carl Credé.
WELDING MACHINE (1885)
Russian inventors Nikolai Bernardos and Stanislav Olszewski created the first
electric arc welder in 1885, though the principle underlying welding—that the
application of heat can be used to join metallic pieces—had been understood and
utilized for centuries.
MOTORCYCLE (1885)
German inventor Gottlieb Daimler expanded upon Nikolaus Otto’s internalcombustion engine by connecting it to a bicycle, thus powering the vehicle by
gas, not manpower. A steam-engine device resembling a modern motorcycle was
invented in 1867.
AUTOMOBILE (1885)
In roughly the same year, 1885, German engineer Gottlieb Daimler and Wilhelm
Maybach created a four-wheel, four-stroke engine automobile and German
engineer Karl Benz, who most historians credit as the ultimate inventor of the
modern automobile, designed a motor car powered by an internal combustion
engine and gasoline.
INDUCTION MOTOR (1885)
Both Italian physicist Galileo Ferraris and Austrian inventor Nikola Tesla filed
patents in the same year for the induction motor, an alternating current motor
that functions via electromagnetic power.
CALCULATOR (1885)
Following centuries of attempts to develop a reliable calculating machine,
American inventor William Seward Burroughs created a “calculating machine”
in 1885 that formed the basis for all further improvements in calculators.
CONTACT LENSES (1887)
Though Leonardo da Vinci is said to have sketched the first designs for
corrective vision lenses, German glassblower F. A. Muller first conceived of
lenses that would replicate the shape of the human eye and improve vision. With
the help of his assistants, German physicist Adolf Eugen Fick further improved
the design by creating lenses that conformed to the eye more comfortably than
any previous version.
EKG (1887)
The EKG evolved over a string of developments, though the most important
contribution may have been Augustus Waller’s in 1887; Waller was the first
scientist to publish an EKG by attaching an electrometer to a projector.
MOTION PICTURE CAMERA (1888)
American inventor Thomas Edison patented one of the early versions of a
motion picture camera—which he called a “kinetoscope”—but his device drew
heavily on similar work done by English photographer Eadweard Muybridge and
the discoveries of other experimenters with the photographic medium in the late
nineteenth century.
MITOCHONDRIA (1890)
German pathologist Richard Altmann is generally credited with first discovering
mitochondria—organelles that provide cells with the majority of their chemical
energy—for postulating that they were fundamental units of cell activity.
Numerous scientists continued to make large strides in their understanding of
mitochondria throughout the twentieth century.
TESLA COIL (1891)
The Czech inventor Nikola Tesla invented the Tesla coil, a high-frequency
transformer that creates extremely large amounts of voltage, and which was used
commercially for lighting and in radio transmission.
RADIO (1896)
While Italian engineer Guglielmo Marconi is traditionally credited with the
invention of modern radio by using radio waves to create a system of wireless
telegraphy (he received a patent for the creation in 1896), the contributions of
Nikola Tesla, Karl Ferdinand Braun, and Heinrich Hertz, among others, were
essential to the final design.
RADIOACTIVITY (1896)
Expounding on the closely preceding discoveries of German physicist Wilhelm
Roentgen and French physicist Henri Becquerel, Polish chemist Marie Curie and
her husband, Pierre Curie, formed a theory of radioactivity, which describes the
spontaneous disintegration of atomic nuclei.
ELECTRON (1897)
British physicist J. J. Thomson, aided by Irish physicist John Townsend and
British physicist H. A. Wilson, discovered the electron while experimenting with
cathode rays, proposing that they were composed of negatively charged particles
smaller than atoms, which he called corpuscles, later renamed electrons.
BLOOD TYPES (1901)
In 1901, Austrian physician Karl Landsteiner published results of studies in
which he argued that four distinct blood types existed—distinguished by the
presence of particular antibodies and antigens—and that blood transfusion
between two individuals could be successful only if they shared the same blood
type.
AIR CONDITIONING (1902)
In an attempt to solve a printing factory’s difficulties with the effects of
fluctuating temperatures and humidity on paper, Willis Haviland Carrier
conceived of a way to reverse the process of heating to create cold air, thus
controlling the amount of moisture in the air. Carrier would go on to start a
company dedicated to air conditioning, and it was not long before models were
made available for domestic use.
STRATOSPHERE (1902)
German meteorologist Richard Assmann and French meteorologist Léon
Teisserenc de Bort are both credited with the discovery of the stratosphere, the
second layer of the earth’s atmosphere, in 1902.
ENGINE-POWERED AIRPLANE (1903)
Inspired by the efforts of German aerial engineer Otto Lilienthal, the Wright
brothers experimented with the flying patterns of kites and eventually developed
the first engine-powered airplane, which succeeded in performing sustained
flight in 1903.
VITAMINS (1905)
While people knew for centuries that eating certain foods could prevent disease,
the English doctor William Fletcher discovered in 1905 that unpolished white
rice was instrumental in creating immunity to beriberi disease, while other kinds
of rice were not, leading him to believe there were nutrients in the unpolished
rice whose absence in a person’s diet would increase their susceptibility to
disease.
HORMONES (1905)
HORMONES (1905)
Confirming earlier scientific work on internal secretions in the human body,
English physiologists Ernest Henry Starling and William Maddock Bayliss
showed that a chemical agent released in one part of the body could affect the
functioning of another part of the body, via the bloodstream. The discovery of
hormones would later lead to the invention of both oral contraceptives and
insulin.
MASS-ENERGY EQUIVALENCE (1905)
Theoretical physicist Albert Einstein stated in a paper published in 1905 that the
mass of a body is equivalent to its energy content, expressed in the famous
equation E=mc
2
, or energy equals mass times the speed of light squared.
SPECIAL RELATIVITY (1905)
The theory of special relativity—developed by Einstein in 1905—concerns the
motion and behavior of particles moving at close to the speed of light, and is
based on two postulates: that the speed of light is the same, regardless of the
speed of the observer, and that the laws of physics are consistent when observed
from any inertial, or nonaccelerating, frame of reference.
EARTH’S CORE (1906)
Irish seismologist Richard Oldham deduced that the earth’s core was made of
less dense, more liquid material than the rock surrounding it by studying why
earthquake waves moved slower through the earth’s core than through the
mantle.
NEUROTRANSMITTERS (1906)
Spanish physician Santiago Ramón y Cajal revolutionized the theories of the
structure of the nervous system in the early twentieth century, aided by methods
developed by Italian physicist Camillo Golgi. Cajal’s theory that the nervous
system was composed of billions of tiny nerve centers—to become known as
neurons—led the discovery of neurotransmitters, chemicals that relay messages
across synapses.
WASHING MACHINE (1908)
American engineer Alva John Fisher pioneered the first electric washing
machine by attaching a motor to the traditional model of a hand-cranked washer.
The Chicago-based Hurley Machine Company introduced the product in 1908.
GENES ON CHROMOSOMES (1910)
American embryologist Thomas Hunt Morgan’s experiments with genetic
mutations and the fruit fly Drosophila melanogaster led him and his team of
students at Columbia University to discover how heredity was in part governed
by genes transported by chromosomes.
SUPERCONDUCTIVITY (1911)
In 1911, Dutch physicist Heike Kamerlingh Onnes tested the behavior and
properties of metals such as lead, tin, and mercury when placed at liquid helium
temperatures, and discovered that they lost all resistance when cooled to
cryogenic levels. This quality became known as superconductivity.
COSMIC RAYS (1913)
The discovery of cosmic rays—particles that bombard earth from beyond its
atmosphere—was the culmination of the work of a number of scientists in the
early twentieth century, although the German physicist Werner Kolhörster did
receive a Nobel Prize for his work and research in the nascent field. However,
Kolhörster’s experiments leaned heavily on earlier discoveries by Victor Hess
and Theodor Wulf.
ELECTRON’S ROLE IN CHEMICAL BONDING (1913)
Danish physicist Niels Bohr proposed his model of the electron (loosely based
on British chemist Ernest Rutherford’s model) in 1913, postulating that electrons
travel in patterned orbits around the nucleus of an atom, and further theorized
that the chemical makeup of an element is derived from the number of electrons
in the atom’s orbit. Bohr’s discovery revealed the electron’s fundamental role in
chemical bonding.
CONTINENTAL DRIFT (1915)
In 1915, German meteorologist and geologist Alfred Wegener published a book
in which he argued that all the continents of the earth had once been part of one
massive landmass called Pangea, which had slowly split apart over time.
Wegener’s ideas were initially rejected, but became universally accepted by the
1960s.
MOVING ASSEMBLY LINE (1913)
Heralding the era of mass production, the Ford Motor Company instituted a
moving assembly line to construct cars under Ford’s leadership in 1913,
lowering the price of cars and quickening their production. The inspiration for
the assembly line came from nineteenth-century midwestern meatpacking
factories.
THEORY OF GENERAL RELATIVITY (1915)
Theoretical physicist Albert Einstein argued in 1915 that matter warps time and
space, allowing large masses to bend light. One of the seminal aspects of this
theory was Einstein’s idea that the pull of gravity in one direction was equivalent
to the force of acceleration in the opposite direction. Einstein’s theory was
proved in 1919 in a study of solar eclipses.
HELICOPTER (1920)
Many failed, but promising models of primitive helicopters preceded the type
created by Argentinean inventor Raúl Pateras Pescara. Pescara’s helicopter was
the first to achieve cyclic pitch, or control of the rotor blades, and he set the
world record in 1924 for flying close to a half mile in a little over four minutes.
QUANTUM MECHANICS (1925)
The field of quantum mechanics, the physics of atomic and subatomic scales,
can be loosely dated back to 1925, when Werner Heisenberg published his first
paper on the topic, but was largely created as a result of efforts of a number of
innovative thinkers, including Einstein, Bohr, Planck, and others, working from
the 1900s to the 1930s.
LIQUID ENGINE ROCKET (1926)
American physicist Robert H. Goddard overcame criticism of his belief in the
future of rockets and helped pioneer the field in 1926, when he set off the first
liquid-fueled rocket in a New England cabbage field.
UNCERTAINTY PRINCIPLE (1927)
First presented in a letter in 1927, German physicist Werner Heisenberg’s
uncertainty principle stated that the more precisely the position of a subatomic
particle position was known, the less precisely one could know the particle’s
momentum. Interpreted in a number of ways, the most influential notion has
been the idea that the act of observation changes the very object being observed.
TELEVISION (1927)
American inventor Philo Farnsworth filed a patent for the first complete
electronic television in 1927, though technological developments (cathode ray
tube, Audion vacuum tube) leading to this final stage were contributed by many
engineers and inventors over the course of the previous century.
PENICILLIN (1928)
While healers dating back to ancient civilization realized that molds could be
used to help cure infection, it was a famous mistake in Scottish biologist
Alexander Fleming’s laboratory that eventually brought penicillin to public
attention as a miraculous antibiotic. The discovery occurred when a spore of
Penicillium notatum floated into a petri dish containing mold, sparking
Fleming’s observation that the spore was inhibiting the growth of the bacteria.
EXPANSION OF THE UNIVERSE (1929)
While working at an observatory in California, American astronomer Edwin
Hubble determined that the universe was expanding while measuring the
redshifts (shifts in the frequency of photons) of distant galaxies and discovering
that they were moving away from each other at a rate constant to the distance
between them.
JET ENGINE (1930)
The credit for the invention of the jet engine is shared between German engineer
Hans von Ohain and RAF officer Frank Whittle, who both independently
developed the engine model, propelled by ignited, compressed air and based on
the principles of Newton’s third law of physics.
NEUTRON (1932)
British physicist James Chadwick discovered the neutron—a subatomic particle
with no electrical charge—in 1932, putting in place some of the first steps
toward the development of the atomic bomb.
RADAR (1935)
Scottish meteorologist Robert Watson-Watt drew on previous research using
radio waves to sense inclement weather and successfully employed a shortwave
radar in 1935 to detect a bomber in the air, a discovery that would prove
instrumental in Britain’s defense during the Battle of Britain.
TAPE RECORDER (1935)
Tape recorders began appearing in the early 1930s, led by German technology
companies. German-born engineer Semi Joseph Begun developed the first
consumer tape recorder, a “Sound Mirror,” in 1935 by employing his research on
magnetic recording, using a specially coated paper and plastic.
NYLON (1937)
While heading the research department at DuPont, American chemist Wallace
Carothers developed nylon—“the miracle fiber,” a man-made synthetic rubber—
in part to create an alternative to silk, which at that time was difficult to obtain
because of shaky trade relations with Japan.
ECOSYSTEM (1935)
First coined in 1935, “ecosystem” came to be fully defined in 1935 by British
chemist Arthur Tansley as a natural system in which all physical and organic
elements coexist and function as a more or less complete unit.
KREBS CYCLE (1937)
The Krebs cycle, the chemical mechanism by which a cell’s respiratory system
functions, was formulated by German biochemist Hans Krebs in 1937, building
on extensive advances by multiple scientists over the preceding decade in
understanding the way cells convert nutrients into energy.
ATOMIC REACTOR (1938)
Italian physicist Enrico Fermi and Hungarian physicist Leo Szilard created the
first nuclear reactor in the 1930s, based on studies conducted by Fermi and his
colleagues on beta decay and the theory of neutrons.
COMPUTER (1944)
German engineer Konrad Zuse is credited by many with inventing the first fully
functioning modern computer, based on a binary system, in 1944. However,
Charles Babbage, Alan Turing, and John Vincent Ansoff can all also be credited
with inventing various forms of computers.
DNA AS GENETIC MATERIAL (1944)
The idea that DNA carries genetic material was initially established by the
famous Avery-MacLeod-McCarthy experiment in 1944, which demonstrated
that, since DNA could cause the transformation of bacteria, it could be seen to
play a major role in hereditary transfer and the passing of genes from one
generation to the next.
MICROWAVE OVEN (1946)
Percy Spencer, an American engineer, discovered the possibility of creating a
microwave oven somewhat by accident when he noticed a candy bar melting
while building a magnetron for Raytheon during an experiment with
electromagnetic radiation.
TRANSISTOR (1947)
Enlisted to improve upon the vacuum tube, experimental theoretician Bill
Shockley and physicists Walter Brattain and John Bardeen experimented with
semiconductors at Bell Labs, eventually producing a reliable transistor that could
amplify and switch electronic signals.
RADIOCARBON DATING (1949)
While at the University of Chicago, American physicist Willard Libby worked
with his colleagues to develop radiocarbon dating, a method of determining the
age of organic substances by how much carbon-14 is present in the material,
revolutionizing the field of archeology.
ARTIFICIAL PACEMAKER (1950)
Although a few primitive versions of artificial heart pacemakers had been
designed before 1950, Canadian engineer John Hopps is generally credited with
the invention of the device, which uses electrical impulses to regulate and
simulate the normal beating patterns of the heart. Internal pacemakers would not
be developed until 1958.
ORAL CONTRACEPTIVE (1951)
A group of loosely connected scientists, most prominently Harvard professor
John Rock, developed the birth control pill in the early 1950s, funded in part by
the American birth control advocate Margaret Sanger. Leading a research group
at the pharmaceutical company Syntex, American chemist Carl Djerassi worked
on developing a steroid hormone, cortisone, which eventually led to the
synthesis of norethindrone, a progestogen, which became a fundamental part of
the first successful oral contraceptive.
EARLY LIFE SIMULATED (1953)
In an effort to understand the conditions governing early life on earth, American
chemist Stanley Miller and American physical chemist Harold Urey created a
closed system, including the elements they believed were present in earth’s early
atmosphere such as hydrogen, methane, and water. Miller and Urey discovered
that amino acids could be easily produced under such conditions.
DOUBLE HELIX (1953)
Drawing on previous studies of nucleotides in DNA, American molecular
biologist James D. Watson and British molecular biologist Francis Crick
experimented with models of different combinations of nucleotides using paper
and wire, and eventually settled upon the intertwined, dual, nucleotide strands
that we now recognize as the double helix.
VCR (1956)
The invention of the VCR, or video cassette recorder, is generally attributed to
the American engineer Charles Paulson Ginsburg, who developed the device
while at the Ampex Corporation by applying high-frequency signals onto
magnetic tape.
LASER (1958)
While at Bell Labs, American physicists Arthur L. Schawlow and Charles H.
Townes began intensive investigation of infrared or visible radiations, initially
developing what they called a maser, which would later evolve into “light
amplification by stimulated emission of radiation,” or a laser.
GPS (1958)
GPS, or Global Positioning System, a navigational system that uses satellites as
reference points to calculate geographical positions, was developed by the
American engineer Ivan Getting and his team at the Raytheon Corporation, at
the behest of the U.S. Department of Defense, after the initial foundational work
of Guier and Weiffenbach tracking the orbit of Sputnik in 1957.
COSMIC MICROWAVE BACKGROUND RADIATION
(1965)
While working with receiver systems at Bell Labs, American astronomers Arno
Penzias and Robert Woodrow Wilson were confounded with a sound they could
not identify, which they ultimately realized was cosmic microwave background
radiation, a remaining radio trace of the Big Bang.
PULSARS (1967)
Pulsars—pulsating neutron stars that appear to blinking—were observed and
discovered in 1967 by Jocelyn Bell Burnell, a graduate student working under
the British astronomer Antony Hewish, who would later receive a Nobel.
RNAALSO GENETIC (1967)
Mirroring previous discoveries that DNA carried genetic material, American
microbiologist Carl Woese theorized in 1967 that RNA, ribonucleic acid, could
store information such as genes, and may have played a role in the development
of early and precellular life.
RESTRICTION ENZYMES (1968)
First isolated in 1968 by geneticists H. O. Smith, K. W. Wilcox, and T. J. Kelly
at Johns Hopkins University, restriction enzymes are found in bacteria and can
cut DNA at specific sequences, thus paving the way for the future of
recombinant DNA molecules.
GRAPHICAL USER INTERFACE (1968—1974)
The use of visual metaphors to represent data on a computer screen, along with
the concept of a mouse as pointing device, dates back to a legendary demo by
the Stanford professor Douglas Engelbart. Elements of the GUI were also
evident in Ivan Sutherland’s 1963 program Sketchpad. The idea was refined and
expanded by the Xerox PARC lab in the early 1970s.
INTERNET (1970--1975)
Assisted by many other computer scientists, the American Vinton Cerf designed
and created the original model of the Internet, building on his early research and
experiments with packet-switching networks, supported by the U.S. Department
of Defense Advanced Research Projects Agency.
CT SCAN (1971)
Using a grant provided by the British Department of Health and Social Services,
British electrical engineer Godfrey Hounsfield conceived and designed the first
CT scan (computerized axial tomography), which sent multiple X-ray beams
through the human body, providing a near three-dimensional image.
MRI (1974)
Building on the discoveries of early MRI inventors, Raymond Damadian
discovered that magnetic resonance imaging would command different
responses from cancerous and noncancerous animal tissue.
ENDORPHINS (1975)
Discovered at about the same time by two research teams working
independently, endorphins were first described when American scientist John
Hughes and German-born British biologist Hans Kosterlitz published their
results of a study in which they removed an amino-acid molecule from the brain
of a pig, which they believed would bolster investigations of the brain’s
receptors for morphine.
PERSONAL COMPUTER (1976)
Legendarily working out of a garage, entrepreneurs and college dropouts Steve
Wozniak and Steve Jobs designed one of the first personal computers, or
microcomputer—Apple I—in 1976, creating the first single-circuit board
computer, though many important models, including the Altair, preceded it.
ONCOGENES (1976)
Bolstering the understanding of cancer and how malignant tumors are created,
American immunobiologist J. Michael Bishop and cellular biologist Harold
Varmus discovered the first human oncogene in 1970.
RNA SPLICING (1977)
British biochemist Richard J. Roberts and American Phillip A. Sharp share both
the credit and the Nobel Prize for their independent discoveries of gene-splicing
—the removal of introns—though some controversy arose over lack of
acknowledgment of Roberts’s colleagues.
ARCHAEA (1977)
Realizing that a number of organisms did not fit into the traditional
categorization of plant or animal, American microbiologist Carl Woese and his
colleagues created a new classification of life, archaebacteria, shortened to
archaea, to accompany bacteria and fungi.
GLOBAL WARMING (1970—1980)
While theories had been proposed throughout the twentieth century suggesting
that carbon dioxide buildup could lead to a warmer planet, the science of global
warming did not reach critical mass until the 1970s and 1980s, as a broad
network of scientists, working in multiple fields, began to track and model
changes in the earth’s atmosphere.
ASTEROID EXTINCTION (1980)
On the basis of substantial geological evidence, scientific father-son team Luis
and Walter Alvarez theorized in 1980 that 65 million years ago, a giant asteroid
had struck earth, killing the dinosaur population.
DNA FORENSICS (1984)
British geneticist Alec Jeffreys discovered DNA forensic fingerprinting by
accident while looking at an X-ray from a DNA experiment that appeared to
show the variations in the DNA of his technician’s family. Jeffreys soon after
realized that DNA fingerprinting could be used to identify individuals by their
genetic code. Dozens of other scientists refined Jeffreys’s approach before it
could be used in criminal cases.
UNIVERSE ACCELERATING (1988)
Based on observations of the stars created by white dwarf star explosions, HighZ Supernova team scientists led by astronomers Adam Riess and Brian Schmidt
determined that the universe was expanding at an accelerating rate.
WORLD WIDE WEB (1989—1992)
British software engineer Tim Berners-Lee designed the program for the World
Wide Web almost completely independently while working at CERN (European
Laboratory for Particle Physics), in an attempt to create a “hypertext notebook,”
which was inspired by the memory of a childhood encyclopedia.
GAMMA RAY BURSTS (1997)
Gamma ray bursts—flashes of gamma rays coming from deep outer space—
were first observed in 1967 by unclassified military satellites. The bursts
befuddled scientists, uncertain of their nature or origin, until 1997, when the
Italian-Dutch satellite BeppoSAX was able to target the burst position, leading
scientists to understand that the rays were caused by residual X-ray emissions.
Notes and Further Reading
ON INNOVATION
An extensive literature exists on the question of innovation, particularly with
reference to scientific and technological fields. I have tried to include a broad
survey of these works in the bibliography, but several works have been
disproportionately influential on my argument and method in this book. Dean
Keith Simonton’s Origins of Genius and Howard Gruber’s Darwin on Man both
explicitly take a Darwinian approach to innovation, and use that approach to
make sense of Darwin’s own distinct genius. Arthur Koestler’s Act of Creation
and Thomas Kuhn’s Structure of Scientific Revolutions remain essential
platforms for the understanding of new ideas. Richard Florida’s Rise of the
Creative Class looks at creativity in an urban context. Richard Ogle’s Smart
World explores the intellectual and physical context of idea formation, as does
Howard Gardner’s Creating Minds. Everett M. Rogers’s Diffusion of
Innovations is the canonical study of the way good ideas spread through
organizations and society. Mihaly Csikszentmihalyi’s Flow and Creativity
explore the psychological states of intense creativity. The power of group and
“end-user” innovation has been persuasively documented by Eric von Hippel in
Democratizing Innovation and by Amar Bhidé’s Venturesome Economy. And
many of our clichés about the origins of good ideas are delightfully debunked in
Scott Berkun’s Myths of Innovation.
INTRODUCTION: REEF, CITY, WEB
The account of Darwin’s voyage to the Keeling Islands is drawn from Darwin’s
own narrative in Voyage of the Beagle, as well as from some of the
correspondence included in The Autobiography of Charles Darwin, and R. D.
Keynes’s Charles Darwin’s Beagle Diary. The connection between Darwin’s
theories about coral reef formation and his later insights into the mechanism of
natural selection is addressed in Howard Gruber’s Darwin on Man. The original
study on superlinear scaling in urban environments is available in “Growth,
Innovation, Scaling, and the Pace of Life in Cities,” by Bettencourt, et al. A
thoughtful layperson’s introduction to Kleiber’s law and its application to urban
culture can be found in George Johnson’s “Of Mice and Elephants: A Matter of
Scale.” For a thorough history of HDTV’s development, see Joel Brinkley’s
Defining Vision. An informative chart of twentieth-century technology adoption
rates in the United States can be found at
http://www.nytimes.com/imagepages/2008/02/10/opinion/10op.graphic.ready.html
John Cloud’s “The Gurus of YouTube” offers a history of the company’s
founding. For a compelling overview of the Web’s “generative” powers, see
Jonathan Zittrain’s The Future of the Internet—And How to Stop It. For more on
the evolution of software interfaces, see Howard Rheingold’s Tools for Thought
and my Interface Culture. The notion of “patterns” of innovation is loosely
based on the concept of patterns and metapatterns developed by Gregory
Bateson in Mind and Nature. The “long zoom” approach is discussed in more
detail in the appendices of my earlier books Everything Bad Is Good for You and
The Invention of Air. The idea has roots in Edward O. Wilson’s notion of
“consilience,” and was partially inspired by a “pace-layered” drawing of
civilization that I first encountered in Stewart Brand’s How Buildings Learn.
CHAPTER 1: THE ADJACENT POSSIBLE
For a history of the incubator, see Jeffrey Baker’s “The Incubator and the
Medical Discovery of the Premature Infant.” The site Neonatology on the Web
(http://www.neonatology.org/) maintains an excellent archive on the history of
incubators and other neonatal technologies. For more on Design That Matters’s
approach to innovation, see Timothy Prestero’s “Better by Design.” Additional
information on the NeoNurture device can be found at designthatmatters.org.
Kauffman’s theory of the adjacent possible is outlined in his book Investigations.
The social causes of multiple simultaneous discovery are outlined in Ogburn and
Thomas’s “Are Inventions Inevitable?” The phenomenon is also discussed at
length in Dean Keith Simonton’s Creativity in Science. For more on the
discovery of oxygen, see Kuhn’s Structure of Scientific Revolutions, Joe
Jackson’s World on Fire, and my own Invention of Air. Charles Babbage’s
attempt to build the first computer is chronicled in Doron Swade’s The
Difference Engine. The story of Apollo 13 is told in Jim Lovell and Jeffrey
Kluger’s Lost Moon.
CHAPTER 2: LIQUID NETWORKS
On the importance of carbon and liquid water to the origins of life, I recommend
several sources: a collection of essays, edited by J. William Schopf, entitled
Life’s Origin; Philip Ball’s imaginative “biography” of water, Life’s Matrix; and
Carl Zimmer’s Science essay “Evolutionary Roots: On the Origin of Life on
Earth.” The original Miller-Urey experiment was published in Science in the
essay “A Production of Amino Acids Under Possible Primitive Earth
Conditions.” Silicon-based life appears in multiple science fictions, including
Stanley Weinbaum’s A Martian Odyssey and in the form of the Horta, a siliconbased creature discovered in episode 26 of the original Star Trek series. Chris
Langton’s theories about the generative power of liquid networks are developed
in his essay “Life at the Edge of Chaos.” Illuminating accounts of his work
appear in both James Gleick’s Chaos and Kevin Kelly’s Out of Control.
Wikipedia maintains an excellent “timeline of innovations,” which provided a
useful starting point for the charts of historical innovation that are included in
this book. On the emergence and innovations of early Renaissance towns,
Braudel’s Wheels of Commerce remains the canonical text. The history of
double-entry accounting is told in John Richard Edwards’s History of Financial
Accounting. For more on the power of collective decision-making, see James
Surowiecki’s Wisdom of Crowds, Howard Rheingold’s Smart Mobs, Clay
Shirky’s Here Comes Everybody, and Kevin Kelly’s Out of Control. Jaron
Lanier’s critique of the “hive mind” appears in his book You Are Not a Gadget,
and in shorter form in the essay “Digital Maoism.” For more on Kevin Dunbar’s
research, see “What Scientific Thinking Reveals About the Nature of
Cognition.” Malcolm Gladwell’s take on the Jane Jacobsian future of workspace
design appeared in the New Yorker in the essay “Designs for Working.” Stewart
Brand devotes a chapter of How Buildings Learn to the “low road” approach of
Building 20. MIT also maintains a website that includes reminiscences about the
building at http://libraries.mit.edu/archives/mithistory/building20/quotes.html.
CHAPTER 3: THE SLOW HUNCH
The intelligence failures surrounding the Phoenix Memo and the Moussaoui
investigation are addressed in the 9/11 Commission Report and in Bill Gertz’s
Breakdown. A transcript of Minneapolis field agent Coleen Rowley’s letter to
FBI director Mueller, detailing the failed connections leading up to the 9/11
attacks, is available at
http://www.time.com/time/covers/1101020603/memo.html. The website
http://www.historycommons.org/ contains an exhaustive archive of documents
and press reports related to the 9/11 attacks, including the most comprehensive
timeline of the late summer months preceding the attack that I have encountered.
António Damásio’s research into emotional brain flash assessments can be found
in his artful work Descartes’ Error. Snap judgments are also investigated in
Gladwell’s Blink and Jonah Lehrer’s How We Decide. For more on Priestley’s
slow hunch, see my book The Invention of Air. Microsoft’s principal scientist
Bill Buxton writes about the slow hunch model in technology in his
BusinessWeek essay “The Long Nose of Innovation.” Howard Gruber’s Darwin
on Man is both the canonical study of Darwin’s intellectual journey toward the
idea of natural selection and one of the most insightful books on scientific
creativity ever written. Images from Erasmus Darwin’s commonplace book can
be found online at http://www.revolutionaryplayers.org.uk/. John Mason’s selfhelp guide to commonplace books appeared in his Treatise on Knowledge.
Robert Darnton’s essay “Extraordinary Commonplaces,” from the New York
Review of Books, provides an erudite account of the impact that commonplace
books had on the Enlightenment-era literary imagination. Tim Berners-Lee’s
Weaving the Web tells the story of his invention of the Web, along with his ideas
for improving the current platform. Myths of Innovation author Scott Berkun has
an interesting analysis of Google’s “innovation time off” program on his blog at
http://www.scottberkun.com/blog/2008/thoughts-on-googles-20-time/.
CHAPTER 4: SERENDIPITY
For more on the battle between the chemical and electrical interpretations of
brain activity, as well as additional material on Loewi’s dream, see Eliot
Valenstein’s The War of the Soups and the Sparks. Edward O. Wilson’s
Consilience discusses the intellectual revelations of dreamwork, with specific
reference to Kekulé’s vision of Ouroboros. Ullrich Wagner’s experiment is
documented in the Nature essay “Sleep Inspires Insight.” Robert Thatcher’s
study of different phase states can be found in “Intelligence and EEG Phase
Reset” from the journal NeuroImage. For more on neurological serendipity, see
David Robson’s New Scientist essay “Disorderly Genius.” William James’s
quote on the chaotic nature of “higher mind” appears in Great Men, Great
Thoughts, and the Environment. For an entertaining and provocative overview of
the evolution of sexual reproduction, see Matt Ridley’s Red Queen and Jared
Diamond’s Why Is Sex Fun? John Barth’s discussion of serendipity comes from
his novel The Last Voyage of Somebody the Sailor. Henri Poincairé’s pedestrian
epiphanies are recounted in his Foundations of Science. A surprisingly long list
of essays have argued that the Web is diminishing our opportunities for
serendipitous discovery, including William McKeen’s “The Endangered Joy of
Serendipity” and Damon Darlin’s “Serendipity, Lost in the Digital Deluge.”
Cass Sunstein has discussed his notion of an architecture of serendipity in Going
to Extremes, and, with Richard Thaler, in Nudge. Alex Osborn’s brainstorming
technique was introduced in his book Applied Imagination. For a discussion of
the problems with brainstorming and group creativity in general, see B. A.
Nistad’s “Illusion of Group Productivity,” from the European Journal of Social
Psychology. For more on open R&D labs, see Don Tapscott’s Wikinomics.
CHAPTER 5: ERROR
For more on Lee de Forest’s extraordinary career as an inventor (and, in later
life, a Hollywood denizen) see his autobiography, Father of Radio. W. Rupert
Maclaurin’s essay “The Process of Technological Innovation” also contains
revealing analysis of de Forest’s error-prone invention of the triode. Additional
information on Wilson Greatbatch’s invention of the pacemaker can be found in
John Adam’s “Making Hearts Beat.” Will Stanley Jevons’s reference to the
“errors of the great mind” appears in his “Principles of Science.” For more on
the generative power of error, see Kathryn Schulz’s superb Being Wrong. I have
discussed the connection between Kuhn’s scientific paradigms and the long
zoom approach in my Invention of Air. For an excellent discussion of Dunbar’s
research and the accidental discovery of cosmic background radiation, see Jonah
Lehrer’s Wired essay “Accept Defeat.” A good introduction to Charlan
Nemeth’s research can be found in her essays “Differential Contributions of
Majority and Minority Influence” and “Dissent as Driving Cognition, Attitudes,
and Judgments.” For a taste of the statistics of free association, see Palermo’s
Word Association Norms. A discussion of Darwin’s failed theory of pangenesis
can be found in Kirschner and Gerhart’s Plausibility of Life. An overview of
how the human genetic mutation rate was calculated can be found in Elie
Dolgin’s Nature News article “Human Mutation Rate Revealed.” For more on
Susan Rosenberg’s research on stress and mutation rates, see her essay
“Microbiology and Evolution: Modulating Mutation Rates in the Wild” in
Science. For more on the “fail fast” movement, see Doug Hall’s BusinessWeek
essay “Fail Fast, Fail Cheap” and Timothy Prestero’s “Better by Design.”
CHAPTER 6: EXAPTATION
Gutenberg’s invention of the printing press is recounted in John Man’s
Gutenberg. I have also drawn upon the insights on Gutenberg’s revolution that
appear in Richard Ogle’s Smart World, and Elizabeth Eisenstein’s Printing
Press as an Agent of Change. Gould and Vrba’s concept of exaptation originally
appeared in Paleobiology in the essay “Exaptation—A Missing Term in the
Science of Form.” For more on the concept, see Buss et al’s Adaptations,
Exaptations, and Spandrels. For more on the history of Google, see John
Battelle’s The Search. Franco Moretti discusses cultural exaptation in his essay
“On Literary Evolution,” included in his Signs Taken for Wonders. Koestler’s
Act of Creation contains many examples of exaptative thought, though he does
not explicitly use the term, since the book predates Gould and Vrba’s essay. For
more on urban subcultures, see Claude Fischer’s essays “Toward a Subcultural
Theory of Urbanism” and “The Subcultural Theory of Urbanism: A TwentiethYear Assessment.” Jane Jacobs’s Death and Life of Great American Cities and
The Economy of Cities contain many similar insights about the capacity of big
cities to cultivate small clusters of interests. (Chris Anderson discusses this in
the context of his “long tail” theory in The Long Tail.) For more on the concept
of the “Third Place,” see Ray Oldenburg’s The Great Good Place. For more on
the innovations of the British coffeehouse, see Brian Cowan’s Social Life of
Coffee, Tom Standage’s History of the World in Six Glasses, and my Invention
of Air. Freud’s Vienna salon is described in the context of innovation in Howard
Gardner’s Creating Minds. Martin Ruef’s research appears in his essay “Strong
Ties, Weak Ties and Islands,” originally published in Industrial and Corporate
Change. For more on Ronald Burt’s analysis of social networks and
organizational innovation, see his “Social Contagion and Innovation” and Social
Origins of Good Ideas. Richard Ogle gives a riveting account of the exaptative
creativity of Watson and Crick in Smart World. For more on Apple’s design and
development processes, see Lev Grossman’s “How Apple Does It.” Howard
Gruber describes his “networks of enterprise” in his essay “The Evolving
Systems Approach to Creative Work.” For more on John Snow’s diverse
intellectual interests, see Peter Vinten-Johansen’s Cholera, Chloroform, and the
Science of Medicine and my Ghost Map.
CHAPTER 7: PLATFORMS
Charles Lyell’s uniformitarian theory is outlined in his Principles of Geology.
For more on Lyell’s reaction to Darwin’s idea, see the correspondence included
in Darwin’s Autobiography. For more on the concept of a keystone species, see
R. T. Paine’s “Conversation on Refining the Concept of Keystone Species,”
published in Conservation Biology. The concept of an ecosystem engineer is
introduced in Clive Jones’s “Organisms as Ecosystem Engineers.” For a
delightful history of the origins of GPS, see William Guier and George
Weiffenbach’s first-person account, “Genesis of Satellite Navigation.” Franco
Moretti has published a number of important works that look at the literary
history of genres and devices, including The Way of the World and Graphs,
Maps, Trees. For more on the innovation track record of the Twitter platform,
see my essay “How Twitter Will Change the Way We Live” from Time
magazine. Like many Web successes, Twitter’s platform innovation relies on
two key contributions from its users: end-user innovation and “venturesome”
consumption. For more on these concepts, see Eric von Hippel’s Democratizing
Innovation and Amar Bhidé’s Venturesome Economy. For more on the politics
of collaborative platforms, see Clay Shirky’s Here Comes Everybody. Tim
O’Reilly discusses the idea of government-as-platform in a Forbes column titled
“Gov 2.0: The Promise of Innovation.” An account of the Redbird artificial reef
can be found in Ian Urbina’s New York Times article “Growing Pains for a DeepSea Home Built of Subway Cars.” For more on the Jacobs vision of
neighborhoods as emergent platforms, see my Emergence. Claudio Richter’s
coral research is described in John Roach’s National Geographic article “Rich
Coral Reefs in Nutrient-Poor Water: Paradox Explained?” For more on Calera’s
technology, see David Biello’s Scientific American article “Cement from CO2
: A
Concrete Cure for Global Warming?” I address the notion of the Web as rain
forest in a Discover column, “Why the Web Is Like a Rainforest,” and in a
speech from the SXSW conference titled “Old Growth Media and the Future of
News,” a transcript of which is available at
http://www.stevenberlinjohnson.com.
CONCLUSION: THE FOURTH QUADRANT
CONCLUSION: THE FOURTH QUADRANT
Willis Carrier’s life story is told in his Father of Air Conditioning. Moretti’s
concept of “distant reading” is outlined in his Graphs, Maps, Trees. The
innovation survey also draws on the histriometric approach to innovation
developed by Dean Keith Simonton in Genius, Creativity, and Leadership and
Creativity in Science. Yochai Benkler includes a more complex chart of potential
innovation frameworks in Wealth of Networks. For more on the notion of
collective invention, see Peter B. Meyer’s “Episodes of Collective Invention.”
Marx and Engels’s reaction to Darwin’s work is discussed in Gruber’s Darwin
on Man. For more on the metaphor of the tangled bank and its importance to the
theory of evolution, see Carl Zimmer’s Tangled Bank. McPherson’s dispute with
Evans and his correspondence with Jefferson are discussed in Joseph Scott
Miller’s essay “Nonobviousness: Looking Back and Looking Ahead,” included
in the collection Intellectual Property and Information Wealth, edited by Peter
K. Yu. I first encountered Jefferson’s quote in Lawrence Lessig’s Future of
Ideas. That book, along with his books Code and Remix, is essential reading for
anyone interested in the notion of an information commons.
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Index
Absolute zero
Accounting, double-entry
Adjacent possible
for Analytical Engine
of biosphere
Darwin’s Paradox and
error and
of evolution
exploring
of jazz
networks and
in origins of life
serendipity and
of Web
Adobe Flash
AdSense
Afghanistan, war in
Air conditioning
Airplanes
Algorithms
computer
evolutionary
Al Qaeda
Altmann, Richard
Alvarez, Luis and Walter
Amazon
American Airlines Flight
Analytical Engine
Analytic geometry
Ansoff, Vincent
Apollo 13 mission
Apple
iPhone
iPod
Macintosh
Application programming interfaces (APIs)
Apps for America
Apps for Democracy
Archaea
Archaeopteryx
Archimedes
Aristotle
ARPANET
Ashbery, John
Aspirin
Asteroid impact, extinction caused by
Assembly lines
Assmann, Richard
Atomic reactors
Atomic theory
Atta, Mohamed
Audion
Automobiles
Avery-MacLeod-McCarthy experiment
Avogadro, Count Amedeo
Babbage, Charles
Bacon, Francis
Balance-spring watches
Ball bearings
Barbier, Charles
Bardeen, John
Barometers
Barth, John
Battelle, John
Bayliss, William Maddock
Beagle (ship)
Beatles, the
Becquerel, Henri
Begun, Semi Joseph
Behaim, Martin
Bell, Alexander Graham
Bell, John
Bell Labs
Beneden, Edouard van
Benkler, Yochai
Benz, Karl
Benzene, structure of
Bernardos, Nikolai
Berners-Lee, Tim
Berti, Gasparo
Bessemer, Henry
Bharat, Krishna
Bible, Gutenberg
Bicycles
Bifocals
Big Bang
Bin Laden, Osama
Biosphere, adjacent possible of
Bı̄rūnı̄, AbūRayhān alBishop, J. Michael
BlackBerry
Blood circulation
Blood types
Bohr, Niels
Bose Acoustics
Bouchon, Basile
Boyle, Robert
Brahe, Tycho
Braille
Bramah, Joseph
Brand, Stewart
Brattain, Walter
Braun, Karl Ferdinand
Brazil, social networking sites in
Brin, Sergey
Britain, Battle of
Brunelleschi, Filippo
Buchner, Eduard
Buffalo Forge Company
Bunsen burner
Burnell, Jocelyn Bell
Burroughs, William Seward
Burt, Ronald
Bush, George W.
Büyükkökten, Orkut
Byrne, David
Byron, Lord
Calculators
Calculus
Calera
California, University of
Berkeley
Davis
Santa Barbara
Santa Cruz
Capitalism
agrarian
industrial
merchant
Carbonated water
Carnot, Sadi
Carothers, Wallace
Carrier, Willis Haviland
Carrier Engineering Corporation
Cartwright, Edmund
Cary Institute for Ecosystem Studies
Cell division
Cerf, Vincent
CERN (European Laboratory for Particle Physics)
Chadwick, James
Chaos
Chardack, William
Chen, Steve
Chicago, University of
China
ancient
Great Wall of
movable type invented in
smallpox inoculation in
Chloroform
Chomsky, Noam
Chromosomes
Chronic Disease Institute (Buffalo, New York)
Chronometer
Cities
adjacent possible in
ancient
discarded spaces in
Italian Renaissance
subcultures of
superlinear scaling of
Clarkson, Martha
Clausius, Rudolf
Clinton, Bill
Clocks, pendulum
Collins, Wilkie
Colt, Samuel
Columbia University
Comets
Complex numbers
Computers
Analytical Engine as
early
graphical interface with
interconnection of, see Internet; World Wide Web
mainframe
multitasking of
personal
vacuum tubes and
Concave lenses
Constantz, Brent
Contact lenses
Continental drift
Copernicus, Nicolaus
Cordus, Valerius
Cosmic rays
Cotton gin
CPU (central processing unit)
Creative Commons
Credé, Carl
Cretaceous period
Crick, Francis
Cristofori, Bartolomeo
Csikszentmihalyi, Mihaly
CT scan (computerized tomography)
Cubic equations
Cumings, Alexander
Cuneus of Leyden
Curie, Marie and Pierre
Daguerre, Louis
Daimler, Gottlieb
Dalton, John
Damadian, Raymond
Damásio, António
Daphnia (water flea)
Dark Ages
Darlin, Damon
Darnton, Robert
Darwin, Charles
biblical argument against
on Keeling Islands
Marx on
natural selection theory of
notebooks of
on origins of life
side interests of
tangled bank metaphor of
Darwin, Erasmus
Darwin’s Paradox
Data.gov
Davis, Miles
Dean, Howard
Defense Department, U.S.
De Forest, Lee
Delaware Department of Natural Resources and Environmental Control
Dennett, Daniel
Denucé, Jean-Louis-Paul
Deoxyribonucleic acid, see DNA
Descartes, René
Design that Matters
DEVONthink
Dickens, Charles
Difference Engine
Digital Equipment Corporation
Din, Taqi alDinosaurs, extinction of
Diodorus Siculus
Djerassi, Carl
DNA
complementary replication system of
double-helix structure of
forensic use of
natural selection and
repair system in
Doppler effect
Dorian scale
Dorsey, Jack
Double-entry accounting
Drais, Karl von
Dreams
Drosophila melanogaster
Duchamp, Marcel
Dujardin, Edouard
Dunbar, Kevin
DuPont Corporation
DVD players
Dylan, Bob
Earthquakes
Eccles, John Carew
Edison, Thomas Alva
Einstein, Albert
EKG
Electrical batteries
Electric motors
Electroencephalogram (EEG)
Electromagnetic spectrum
Electrons
Elevators
Elizabeth I, Queen of England
Elliptical orbits
Encyclopaedia Britannica
Endorphins
Engelbart, Doug
Engels, Friedrich
England
commons of
Enlightenment in
Industrial Revolution in
Victorian
ENIAC
Enlightenment
Eno, Brian
Enquire software application
Enzymes
Erdapfel
Error
inventions generated by
noise and
paradigm shifts and
Ether
Evans, Oliver
Evolution
adjacent possible in
Darwin’s theory of
of facial expressions
mutation in
see also Natural selection
Exaptation
in coffeehouse model
in evolutionary theory
in subcultures
in shared media
Exposition Universelle (Paris)
Eyeglasses
bifocal
concave lens
Fabricius, Johannes and David
Facebook
Fahrenheit, Daniel Gabriel
Falcon, Jean
Falling bodies, law of
Faraday, Michael
Farnsworth, Philo
Federal Bureau of Investigation (FBI)
Automated Case Support system
Counterterrorism
Radical Fundamentalist Unit (RFU)
Federal Communications Commission (FCC)
Ferdinand III, Holy Roman Emperor
Fermi, Enrico
Ferraris, Galileo
Ferro, Scipione del
Fick, Adolf Eugen
Finley, James
Fiore, Antonio
Firearms
Fischer, Claude
Fisher, Alva John
Fitch, John
FitzRoy, Vice Admiral James
Flaubert, Gustave
Fleming, Alexander
Flemming, Walther
Fletcher, William
Flintlock firing mechanism
Flush toilets
Flying shuttle
Ford Motor Company
Forensics, DNA
Foursquare
France, medical establishment in
Franklin, Benjamin
Franklin, Rosalind
Frasca, David
Fraunhofer, Joseph von
Freud, Sigmund
Fuchsian functions
Fulton, Robert
GABA
Galápagos Islands
Galileo Galilei
Galvani, Luigi
Gamma ray bursts
Gates, Bill
Gatling gun
General Electric
Geological uniformitarianism
Geometry
Gerhardt, Charles
Germany
technology companies in
viticulture in
in World War I,
Germ theory
Getting, Ivan
Gilbert, William
Ginsburg, Charles Paulson
Gladwell, Malcolm
Global Positioning System (GPS)
Global warming
Goddard, Robert H.
Godfrey, Thomas
Goethe, Johann Wolfgang von
Golgi, Camillo
Goodyear, Charles
Google
Alerts
Books
Gmail
Innovation Time Off
Maps
News
Search Products and User Experience
Gore, Al
Gould, Steven Jay
Grand Alliance
Granovetter, Mark
Graphical user interface (GUI)
Gravity
Gray, Elisha
Great Barrier Reef
Greatbatch, Wilson
Greeks, ancient
mythology of
GreenXchange
Gruber, Howard
Guericke, Otto von
Guier, William
Gunter, Edmund
Gutenberg, Johannes
Guthrie, Samuel
Hadley, John
Halley, Edmund
Hancock, Thomas
Hanjour, Hani
Hargreaves, James
Harrington, John
Harriot, Thomas
Harrison, Thomas
Harvard University
Harvey, William
Hayek, Friedrich von
Hegelian dialectics
Heisenberg, Werner
Helicopters
Heliocentric theory
Henlein, Peter
Heredity
Herschel, William and Caroline
Hertz, Heinrich Rudolf
Hess, Victor
Hewish, Antony
Hewlett-Packard
High-definition television (HDTV)
Highs, Thomas
High-Z Supernova team
Hippocrates
Homebrew Computer Club
H1N1 virus
Hooke, Robert
Hooker, Joseph
Hopps, John
Hormones
Hot air balloons
Hounsfield, Godfrey
Howard, Ron
Howe, Elias
HTML
HTTP
Hubble, Edwin
Hughes, John
Hunches
APL platform and
Darwin’s
instant
about 9/11 terrorists
serendipity and
slow
Hurley, Chad
Huxley, T. H.
Huygens, Christian
IBM
Incubators
India
social networking sites in
Indian Ocean tsunami (2004)
Induction motors
Industrial Revolution
Infant mortality
Information spillover
Ingenhousz, Jan
“Intelligent design,”
Internet
commercial value of
filters on
generative platform of
open architecture of
start-ups on
transmission costs of idea sharing on
see also World Wide Web
IQ scores
Iran, political protests in
IRC messaging platform
Islam
fundamentalist
Italy, Renaissance
Ive, Jonathan
Jacob, François
Jacobs, Jane
Jacquard, Joseph-Marie
James, William
Janssen, Zacharias and Hans
Japan
earthquakes in
HDTV in
Javascript
Jefferson, Thomas
Jeffreys, Alex
Jenner, Edward
Jet engines
Jevons, William Stanley
Jobs, Steve
Johns Hopkins University
Applied Physics Laboratory
Johnson, George
Jones, Clive
Jones, Greg
Joyce, James
Jupiter, moons of
Karim, Jawed
Kauffman, Stuart
Kay, Alan
Kay, John
Keeling Islands
Kekulé von Stradonitz, Friedrich August
Kelly, T. J.
Kelvin scale
Kepler, Johannes
Khosla, Vinod
Kindle
Kleiber, Max
Koestler, Arthur
Kolhörster, Werner
Korean Air Lines Flight
Korschinsky, S.
Kosterlitz, Hans
Krebs cycle
Kuhn, Thomas
Kühne, Wilhelm
Kundra, Vivek
Laennec, René
Lamarckianism
Landsteiner, Karl
Langton, Christopher
Lanier, Jaron
Lasers
Lavoisier, Antoine
Lee, William
Leeuwenhoek, Antonie Philips van
Lehrer, Jonah
Leibniz, Gottfried Wilhelm
Lenormand, Louis-Sébastien
Leonardo da Vinci
Lessig, Lawrence
Libby, Willard
Liebig, Justus von
Life, origins of
simulation of
Light
spectrum of
speed of
Lightbulbs
Lightning rods
Lilienthal, Otto
Linnaeus, Carl
Lion, Alexandre
Lippershey, Hans
Liquid networks
Lithography
Lloyd, Edward
Locke, John
Locomotives
Loewi, Otto
Logarithms
London
cholera in
Science Museum
University
World’s Fair (1862)
Long-zoom perspective
Looms, mechanization of
Loschmidt, Joseph
Louis XIII, King of France
Lovelace, Ada
Lyell, Charles
Magnetism
Malthus, Thomas
Maps
Google
Mercator projection
Marconi, Guglielmo
Marin le Bourgeoys
Mariotte, Edme
Marius, Simon
Martin, Odile
Marx, Karl
Mason, John
Mason jars
Massachusetts Institute of Technology (MIT)
Building
Mass production
Mathematical symbols
Mauna Kea
Mauritius
Maybach, Wilhelm
Mayer, Marissa
McClure, Frank T.
McGaffey, Ives W.
McKeen, William
McPherson, Isaac
Mellotron synthesizer
Mendel, Gregor
Mendeleev, Dmitri
Mendelian genetics
Mercator projection
Mesopotamia
Metabolism, relationship of size to
Michelangelo
Microorganisms
Microscopes
Microsoft
Building
Windows
Windows Media Player
Microwave ovens
Microwaves
Milky Way
Miller, Stanley L.
Milne, John
Milton, John
Mitochondria
Molecules, formulation of theory of
Montgolfier, Joseph-Michel and Jacques-Étienne
Moretti, Franco
Morgan, Thomas Hunt
Morse code
Motion, laws of
Motion picture camera
Motorcycle
Mount Vesuvius
Moussaoui, Zacarias
MRI (magnetic resonance imaging)
Mueller, Robert
Muller, F. A.
Multiples
Mutation
Muybridge, Eadweard
Nairobi (Kenya)
Napier, John
National Science Foundation (NSF)
Natural selection
Navigational devices
see also GPS
Navy, U.S.
Nemeth, Charlan
NeoNurture
Neurotransmitters
Neutrons
Newcomen, Thomas
Newlands, John
Newton, Isaac
New York City
Police Department (NYPD)
Nicholas of Cusa
Niepce, Joseph Nicephore
Nike Corporation
Nitroglycerine
Nobel, Alfred
Nobel Prize
Noise
error and
Non-Euclidian geometry
Nucleotides
Nylon
Obama, Barack
Ocean tides
Octants
Ogburn, William
Ogle, Richard
Ohain, Hans von
Oldenburg, Ray
Oldham, Richard
Olszewski, Stanislav
Oncogenes
Onnes, Heike Kamerlingh
Oral contraceptives
Orbits
of comets
of electron around nucleus of atom
of man-made satellites
planetary
O’Reilly, Tim
Orkut
Osborn, Alex
Otis, Elisha
Otto, Nikolaus
Oughtred, William
Outside.in
Oxford University
Oxygen, isolation of
Ozzie, Ray
Pacemakers
Pacioli, Luca
Page, Larry
PageRanks
Pan Am International Flight Academy
Pangea
Papin, Denis
Paracelsus
Parachutes
Paradigm shifts
Paris
Maternité de
University of
Parkes, Alexander
Pascal, Blaise
Pasteur, Louis
Pelouze, J. T.
Pencils
Pendulums
Penicillin
Penzias, Arno
Periodic table
Perkins, Jacob
Pescara, Raúl Pateras
Phoenix memo
Photography
Photosynthesis
Piano
Pi Sheng
Planck, Max
Plante, Gaston
Plant respiration
Plastic
Platforms
city as
emergent
generative
open
stacked
Pliny the Elder
Poe, Edgar Allan
Poincaré, Henri
Poindexter, Admiral John
Polaris nuclear missiles
Portland cement
Pressure cookers
Prestero, Timothy
Priestley, Joseph
Princeton University
Printing press
Procter & Gamble
Proust, Joseph
Ptolemaic astronomy
Public Broadcasting System (PBS)
Public Enemy
Pulmonary respiration
Pulsars
Punch cards
Pyramids
Quantum mechanics
Quarter-power laws
Quick-Time
Radioactivity
Radiocarbon dating
Radios
RAM (random access memory)
Ramón y Cajal, Santiago
Rangiroa atoll
Raytheon Corporation
Reagan, Ronald
Red Sea
Refrigerators
Relativity theory
REM sleep
Renaissance
Reproductive strategies
Research and development (R&D) labs
Respiration
plant
pulmonary
Restriction enzymes
Revolvers
Richter, Claudio
Riess, Adam
RNA
Roberts, Richard J.
Rock, John
Rockets
Roemer, Olaus
Roentgen, Wilhelm
Romans, ancient
Rosen, Jonathan
Rosenberg, Susan
Royal Air Force (RAF)
Royal Society
Rubber
vulcanized
Rudolff, Christoph
Ruef, Martin
Rumsey, James
Rutherford, Ernest
Sackett-Wilhelm Lithography Company
Salesforce.com
Samit, Harry
Sanger, Margaret
Santa Fe Institute
Sarcopterygii
Saudi Arabia
Savery, Thomas
Sawyer, William
Schawlow, Arthur L.
Scheele, Carl Wilhelm
Scheutz, Per Georg
Schickard, Wilhelm
Schmidt, Brian
Schumpeter, Joseph
Scleractinia
Seismographs
Senebier, Jean
Senefelder, Alois
September 11, 2001, terrorist attacks (9/11)
Serendipity
chaos and
in dreams
hunches and
Web and
Servetus, Michael
Sewing machines
Sexual reproduction
SGML
Sharp, Philip A.
Shibh, Ramzi bin alShocklee, Hank
Shockley, Bill
Sholes, Christopher Latham
Shore, John
Singer, Isaac
Six Sigma
Sketchpad
Slide rules
Smallpox vaccine
Smith, H. O.
SMS mobile communications platform
Snow, John
Sobrero, Ascanio
Solar system, heliocentric theory of
Sony Corporation
Soubeiran, Eugène
Soubra, Zakaria Mustapha
Soviet Union
space program of
Spectroscopes
Speed of light
Spencer, Percy
Spillover, information
Spinning jenny
Sputnik
Stanford University
Business School
Woods Institute for the Environment
Staphylococcus
Starling, Ernest Henry
Steamboats
Steam engines
Steam locomotives
Steelmaking
Stocking frames
Stone, Biz
Strasburger, Eduard
Stratosphere
StumbleSafely
Subcultures
Sunlight Foundation
Sunspots
Sunstein, Cass
Superconductivity
Superlinear scaling
Supernovas
Suspension bridges
Sutherland, Ivan
Swan, Joseph
Switzerland
Syntex
Szilard, Leo
Talking Heads
Tangled bank, metaphor of
Darwin’s use of
Tansley, Arthur
Tape recorders
Tarnier, Stéphane
Tartaglia, Niccolò
Taxonomy
TBWA/Chiat/Day advertising agency
TCP/IP
Teisserenc de Bort, Léon
Telegraphy
Telephone
Telescopes
Television
10/10 rule
Terrestrial globes
Tesla, Nikola
Textile industry
Thatcher, Robert
Thermodynamics
Thermometers
Thomas, Dorothy
Thomson, J. J.
3M Corporation
Tompion, Thomas
Torricelli, Evangelista
Total Information Awareness project
Total Quality Management
Townes, Charles H.
Townsend, John
Transistors
Transit system project
Trevithick, Richard
Tuning forks
Turing, Alan
Twitter
Uncertainty principle
Uniform acceleration
United Technologies
UNIVAC
Universal gravitation, law of
Universe, expansion of
Unwin, William
Urey, Harold C.
Vacuum cleaners
Vacuum pumps
Vacuum tubes
Vail, Alfred
Varmus, Harold
Vaughan, Philip
VCRs (video cassette recorders)
Venice
Vernier scale
Victorian era
sewage systems of
technology of
Vitamins
Volta, Alessandro
Von Kleist, Dean
Vrba, Elisabeth
Vries, Hugo de
Vulcanized rubber
Wagner, Ullrich
Wallace, Alfred Russel
Waller, Augustus
Walpole, Horace
Washing machines
Washington, D.C.
Watches
Watson, James
Watson-Watt, Robert
Watt, James
Wegener, Alfred
Weiffenbach, George
Welding
Welsbach burner
Wendt, William F.
West, Geoffrey
Whewell, William
Whitney, Eli
Whittle, Frank
Wikipedia
Wilcox, K. W.
Williams, Evan
Williams, Ken
Wilson, H. A.
Wilson, Robert Woodrow
Wirth, Louis
Woese, Carl
World War I,
World War II,
World Wide Web
adjacent possible of
development of
ecosystem of
generative platform of
geographic mashups on
hunch database on
marketplaces on
news on
politics and
queries of(see also Google)
serendipity and
superlinear scaling in
video and sound on(see also YouTube)
work environments and
see also Internet
Wozniak, Steve
Wright brothers
Wulf, Theodor
Xerox
X-ray crystallography
X-rays
YouTube
Zuckerman, Ethan
Zuse, Konrad
1
The convoluted history of HDTV’s origins could be the subject of an entire
book, but the condensed version goes something like this: in the early 1980s the
Japanese public broadcasting company NHK gave a series of demonstrations of
a prototype high-definition television platform to members of the U.S. Congress
and other government officials. This was at the height of American fears about
Japan’s economic ascendancy, a time when Sony televisions were already
outselling venerable American brands like RCA and Zenith. The idea that the
Japanese might introduce a higher-quality image to the U.S. market posed a
threat both to American consumer electronics companies and, as then-senator Al
Gore pointed out after watching the NHK demo, to the semiconductor
companies that would make the chips for all those new television boxes. Within
a matter of months, the Federal Communications Commission formally decided
to investigate the possibility of improving the picture quality of broadcast and
cable TV. All the forces were aligned for the next major step forward in the
television medium. Ronald Reagan, always one to grasp the transformative
possibilities of television, even called the development of a U.S. HDTV standard
a matter of “national interest.”
But what followed in the subsequent years was less of a Great Leap Forward
and more of an endless, serpentine crawl. First, the FCC appointed a committee
—the Advisory Committee on Advanced Television Service (ACATS)—that
solicited and reviewed twenty-three different proposals over the next year,
eventually winnowing them down to six different systems, each using a unique
scheme to convey higher-definition sound and image. Some were analog, others
digital. Some were backward compatible with the current systems; others would
require the consumer to upgrade to new equipment. For five years, the sponsor
organizations enhanced and tested their various platforms, at a cost of hundreds
of millions in research-and-development dollars. The whole process was
supposed to come to a conclusion in 1993, when ACATS was scheduled to run a
series of final tests and pick a winner, but the final tests turned out to be a
preamble: the only thing the committee agreed on was that digital was preferable
to analog, which reduced the field slightly. The remaining contenders all had
enough flaws individually to keep the committee from anointing a new heir
apparent, and so the ACATS group proposed that the remaining candidates
collaborate on a single standard. This group—called the Grand Alliance—
reached agreement on specifications for digital high-definition video and audio
in 1995, which the FCC embraced the following year.
2
This fact, ironically, may be related to some of their blemishes. It may be that
the criminals and spammers thrive in these spaces because they, too, are able to
be more innovative at their trades.
3
Sections of the argument that follows will be familiar to anyone who has spent
the last decade or two exploring the new possibility spaces of the Web. I last
wrote about the Web in book form ten years ago; since that time, a marvelous
community of entrepreneur theorists has materialized, capable of pushing the
boundaries of the medium, and at the same time reflecting on what those
advances might mean. We have, all of us, seen firsthand how innovative a space
the Web can be, and we have assembled a great deal of local knowledge about
the forces that make that innovation possible. In assembling the seven patterns of
innovation, I have tried to organize that knowledge into productive categories,
and I hope I have provided a few insights into how the Web works that will
surprise the natives. But even the most devoted crowd-sourcing, microblogging
Wikipedia-head has doubts about how portable the Web experience is to realworld innovation environments. Just because the patterns work for Google
doesn’t mean that they are relevant for an understaffed nonprofit, or auto-parts
manufacturer, or city government. And so one way to think about the pages that
follow is as an argument that the particular magic that we have seen on the Web
has a long history that predates the Web and can be reproduced in other
environments.
4
Patents actually have a complicated historical relationship to the idea of open
information networks. While most patent law is exclusive in nature—forbidding
non-patent-holders from using a patented “method” without permission for a
finite time period—patent law also conventionally involves an element of
disclosure, where the inventor is forced to reveal the nature of his or her creation
in technical detail. The disclosure is obviously partly designed to help enforce
the restrictions in cases of patent infringement, but it was also intended to
encourage good ideas to spread more freely, by making them part of the public
record. Unfortunately, the modern emergence of patent trolls and squatters,
supported by overzealous intellectual property lawyers, means that the protective
side of patent law has dominated the connective side.
5
The same pattern appears in rain forests, precisely because there are so many
organisms exploiting every tiny niche of the nutrient cycle. That efficiency is
one of the reasons that clearing the rain forests is such a shortsighted move: the
nutrient cycles in rain-forest ecosystems are so tight that the soil is usually very
poor for farming—all the available energy has been captured on the way down
to the earth.
6
Innovation, of course, is not the sole reason so much of the world defied the
predictions of The Communist Manifesto and embraced the capitalist way of life.
Economists and social historians have documented multiple factors that drove
the march of the market: capitalist economies had a better track record of longterm increases in GDP; economic actors had more liberty to make individual
choices; economic self-interest is an undeniable motivating force for human
beings. But few defenses of capitalism’s economic virtues failed to mention its
protean force. Even its critics acknowledged the market’s drive for novelty and
innovation, as in Joseph Schumpeter’s famous theory of “creative destruction.”
7
This framework is adapted from Yochai Benkler’s book The Wealth of
Networks. Benkler’s point is that we have extensive experience with three of the
four possible combinations. Private corporations are centralized and marketbased. The marketplace itself is decentralized and, obviously, market-based.
Planned economies are centralized and non-market-based. But the magic square
is the fourth one: that of decentralized, non-market environments. This is a
combination that does not easily fit into the standard boxes of capitalism and
socialism. Yet in recent years, this quadrant has been a hothouse of innovation,
thanks in large part to the open architecture of the Internet.