Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries

by Safi Bahcall

Like Vannevar Bush, who insisted, as described in chapter 1, that he “made no technical contribution whatever to the war effort,” Catmull saw his job as minding the system rather than managing the projects.

That message got through to Jobs. Jobs had a role in the system—he was a brilliant deal-maker and financier. It was Jobs, for example, who insisted on timing the Pixar IPO with the Toy Story release, and Jobs who negotiated the Pixar deals with Disney. But he was asked to stay out of the early feedback loop on films. The gravity of his presence could crush the delicate candor needed to nurture early-stage, fragile projects. On those occasions he was invited to help near-finished films, Jobs would preface his remarks: “I’m not a filmmaker. You can ignore everything I say.” Jobs had learned to mind the system, not manage the project.

Also gone were the blinders to S-type loonshots. For example, by 2001 music piracy on the internet was rampant. The idea of an online store selling what could easily be downloaded for free seemed absurd. And no one sold music online that customers could keep on their own computers (online music, at the time, was available only through subscription: monthly fees for streaming songs). Plus one more nutty thing: no one sold individual songs, at 99 cents each, rather than whole albums. “You’re crazy,” anyone could have told Jobs. “There’s no way that could make any money.” The idea didn’t seem so crazy after one million songs were downloaded from the iTunes store in the first six days. There were no new technologies. Just a change in strategy that no one thought could work. Apple’s P-type loonshots, of course, transformed their industries: the iPod, the iPhone, and the iPad. But what ultimately made them so successful, aside from excellence in design and marketing (most, although not all, of the technologies inside had been invented by others), was an underlying S-type loonshot. It was a strategy that had been rejected by nearly all others in the industry: a closed ecosystem.

During the 1970s, engineers at PARC invented the first graphics-enabled personal computer (the Alto), the first visual-based word processor, the first laser printer, the first local networking system (ethernet), the first object-oriented programming language, and a half dozen other firsts. It was an incredible run. But none of those breakthroughs was commercialized by Xerox.

The stories in part one illustrate the first three Bush-Vail rules: 1. Separate the phases • Separate your artists and soldiers • Tailor the tools to the phase • Watch your blind side: nurture both types of loonshots (product and strategy) 2. Create dynamic equilibrium • Love your artists and soldiers equally • Manage the transfer, not the technology: be a gardener, not a Moses • Appoint, and train, project champions to bridge the divide 3. Spread a system mindset • Keep asking why the organization made the choices that it did • Keep asking how the decision-making process can be improved • Identify teams with outcome mindsets, and help them adopt system mindsets

Greenspan’s view, however, that efficient markets and invisible hands are fundamental laws that are rarely, if ever, violated is widespread. But it’s a fallacy. That fallacy is a common cause of policy disasters (or investment opportunities, if you are a trader). Neither efficient markets nor invisible hands are fundamental laws. They are both emergent properties.

Emergent properties are collective behaviors: dynamics of the whole that don’t depend on the details of the parts, the macro that rises above the micro. Molecules will flow at high temperatures and freeze at low temperatures regardless of the differences in their details. A water molecule has three atoms and is shaped like a triangle. Ammonia has four atoms and is shaped like a pyramid. Molecules of buckminsterfullerene have sixty atoms and are shaped like soccer balls (they’re called buckyballs for short). Yet they all exhibit the same fluid dynamics at high temperatures and solid dynamics at low temperatures.

One of the things that distinguishes an emergent property like the flow of liquids from a fundamental law—like quantum mechanics or gravity, for example—is that an emergent property can suddenly change. With a small shift in temperature, liquids suddenly change into solids. That sudden shift from ...

Although all people are different, and all teams are different, what makes emergent properties and the phase transitions between them so interesting is that they are so predictable. We will see why organizations will always transform above a certain size, just like water will always freeze below a certain temperature, traffic will always jam above a critical density of cars, and one burning tree in...

Adam Smith, who knew how to make an exit, has grown into a misty icon, a hero to libertarians and free-marketeers who like their economics neat, hold the morals. (The real Adam Smith argued for restraints on markets and prized his work on ethics more than his work on economics.) The manuscript Smith asked his friends to spare had nothing to do with either ethics or economics. It was his History of Astronomy, written shortly after he finished graduate school.

Adam Smith’s work, however, was much closer to the field’s quieter, and less well-known, Protestant offshoot: the study of emergent phenomena. The high priest of that offshoot was Newton’s widely admired contemporary, Robert Boyle.

6 Phase Transitions, I: Marriage, Forest Fires, and Terrorists When gradual shifts cause sudden transformations You’re driving home from work, on the highway, you’re anxious, maybe speeding a bit, but the traffic is flowing well. Suddenly, the highway turns into a parking lot of stopped cars. There’s no visible cause. There are no on-ramps or accidents in sight. You set aside thoughts of a cold dinner and angry spouse and wonder: where did this traffic jam come from? Answer: You have just experienced a phase transition—a sudden change between two emergent behaviors. Those two behaviors are smooth flow and jammed flow.

To understand what phase transitions tell us about nurturing loonshots more effectively, we need to know just two things about them: 1. At the heart of every phase transition is a tug-of-war between two competing forces. 2. Phase transitions are triggered when small shifts in system properties—for example, density or temperature—cause the balance between those two forces to change. That’s it.

JANE AUSTEN, PHYSICIST It is a truth universally acknowledged that a single man in possession of a good fortune must be in want of a wife. —Jane Austen, Pride and Prejudice Miss Austen suggests that two competing forces tug at single men. Those of modest fortunes, in their younger and more aggressive years, may travel widely in the pursuit of fame, wealth, and glory. Let’s call that force “entropy.” Those of greater fortune, in their older and gentler years, want to settle down with a partner. They seek family, stability, and cable TV. Let’s call that force “binding energy.”

In physics language, we triggered a marble-solid to marble-liquid phase transition. The marble-solid to marble-liquid phase transition: when shaking energy crosses a threshold, marbles suddenly break free The system property that we gradually change to trigger a phase transition is called a control parameter. In the traffic flow example, the density of cars on the highway is the control parameter. In this marble-solid to marble-liquid transition, the vigor of our shaking is the control parameter. Shaking vigor can be measured on a scale. We can call that scale “temperature.” The hotter the temperature, the more the entropy term dominates (the urge to roam all over). The colder the temperature, the more the binding energy dominates (the attraction to the bottom of the egg well). When the temperature crosses a threshold—a breakeven point between entropy and binding energy—the system suddenly changes behavior. That’s a phase transition. In real solids, the binding energy arises from the forces between molecules rather than a fixed landscape of small wells. But otherwise the model gets it right. This microscopic tug-of-war between entropy and binding energy is behind every liquid-to-solid phase transition.

In the next chapter, I will show you that team size plays the same role in organizations that temperature does for liquids and solids. As team size crosses a “magic number,” the balance of incentives shifts from encouraging a focus on loonshots to a focus on careers.

The magic number is not universal, however. Teams transform at different sizes, just like solids melt at different temperatures. The reason is the key idea behind our fourth rule. It’s why we can change the magic number. Systems have more than one control parameter. In our egg carton example, imagine making the egg wells a hundred times deeper. You need to shake a hundred times harder to knock the marbles out of their wells. The deeper well is how we can think about a solid with a stronger binding energy. For example, the binding energy in iron is nearly a hundred times stronger than the binding energy in water. That’s why iron melts at close to 2,800 degrees Fahrenheit, while ice melts at 32 degrees Fahrenheit. Binding energy is another control...

PHASE DIAGRAMS Let’s come back to our traffic flow example, and to a useful technique that scientists use to help think about these questions. The two competing forces for drivers on a highway are speed and safety. A driver accelerates to reach cruising speed, but he brakes to avoid hitting the bumper in front of him. The spacing between cars—average car density—is one control parameter, as we saw above. But it’s not the only one. Your decision whether to slam on your brakes when you see brake lights flash on the car ahead depends not only on the distance to that car but on how fast you’re going. At 30 miles per hour, your stopping distance is roughly six car lengths. At 80 miles per hour, it’s closer to 30 car lengths. In deciding whether to brake, your brain intuitively estimates your stopping distance and compares it with the distance to the bumper in front of you. Both the average speed of cars and the average density of cars contribute to triggering the transition. A phase diagram captures these two control parameters in one graphic. In the diagram below, the average distance between cars is measured on the vertical axis, and the average car speed is measured on the horizontal axis. At low speeds or when there are few cars on the highway, above and to the left of the dashed line (#1 in the diagram below), traffic flows smoothly. When the traffic flow crosses the transition line at either higher car densities (#2) or faster car speeds (#3), small disruptions grow expon...

Some of the most creative ideas for adjusting control parameters, as we will see, come from the connections between systems that appear to be unrelated but turn out to share the same category of phase transition. The solid-to-liquid transitions described above—both the marbles and real solids—fall within a category called symmetry-breaking transitions. A liquid has symmetry in the sense that, averaged over time, it looks the same from any angle. That’s called rotation symmetry. A solid does not: it “breaks” rotation symmetry. That’s because the view of a molecule looking directly down the x-axis will be very different than the view looking five or ten degrees off that axis. Over a dozen Nobel Prizes have been awarded for discoveries that were ultimately explained by this same principle of a symmetry-breaking transition. The sudden change in traffic flow falls within a second category of phase transition called a dynamic instability. A change in control parameters transforms one kind of motion (smoothly flowing cars) into a different kind of motion (jammed flow) by making the smooth flow very sensitive to small disruptions (driver tapping on his brakes). Fluids and gases also experience dynamic instabilities. They flow smoothly, but only at speeds below a critical threshold. Above that threshold, the flow suddenly becomes turbulent.

Broadbent and Hammersley discovered that the answer to both the gas-mask puzzle and the forest-fire puzzle was described by a phase transition. Below a threshold density of pores in a gas mask, no air could get through. Above that critical density, a channel would always appear connecting one side to the other. For forests, below a threshold density of trees, the fire would die out. Above that critical density, the fire would engulf the whole forest.

When will a small disease outbreak grow into an epidemic? Go back to the model of fire spreading from tree to tree. A high wind speed in the forest, blowing sparks quickly from tree to tree, is like a virus that is highly contagious. A high density of trees is like people living close together (in cities, for example). When the infectability and density cross a critical threshold, small outbreaks erupt into epidemics. When they fall below that threshold, small outbreaks die out quickly. That’s the epidemic phase transition.

The percolation models predict something you would never guess through intuition, or experience, or microsimulations with different tree types and vegetation. It is a unique prediction of the science of emergence and of phase transitions. According to these models, as a forest gets dangerously close to a phase transition, to erupting, the frequency of fires should take a specific form. The frequency should vary in inverse proportion to size: Twenty-acre fires should occur half as often as ten-acre fires. Forty-acre fires should occur one-quarter as often as ten-acre fires. Hundred-acre fires should occur one-tenth as often, and so on. That pattern, called a power law, is a surprising prediction—a mathematical clue that a forest is on the verge of erupting. The pattern has been seen elsewhere. As we will discuss below, the power-law pattern is seen not only in forest-fire models, but in financial markets and terrorist attacks.

The pattern of many connections within one tight community, punctuated by occasional ties to distant communities, describes a vast range of systems. Neurons in the brain mostly connect within one cluster, but occasionally their axons extend far outside, to an entirely different cluster. Proteins in a cell mostly interact within one functional group, but occasionally they connect with receptors far removed. Sites on the internet mostly connect within one tight group (celebrity news sites link to other celebrity news sites; biology sites link to other biology sites), but occasionally a site will connect far outside its cluster (TMZ will link to a study on neuroscience). The Kevin Bacon game had shown that there are surprisingly few steps between any two nodes (actors) in these kinds of networks. So Watts and Strogatz called a system with mostly local connections but occasional distant ties a “small-world network.”

Whether a computer virus spreads widely across the internet or disappears quickly; whether a tiny neuronal misfiring is harmless or erupts into a seizure engulfing your brain; whether an idea spreads explosively throughout a population or fades away quickly—all are governed by similar dynamics: percolation on a small-world network.

By building a model that was simple, but not simplistic—that is, it captured the essence of trading, without getting lost in the details—Johnson showed that his trading cliques model seemed to explain the fat tail distribution in financial markets pretty well. That fat tail took on a characteristic shape: a power law. There were 32 times fewer cliques of 40 people than cliques of 10. There were 32 times fewer cliques of 160 than cliques of 40. And so on. The number of cliques decreased with the size of the clique by an unusual power: 2.5.

Systems snap—liquids suddenly freeze, traffic suddenly jams, forests or terror networks suddenly erupt—when the tide turns in a microscopic battle. Two forces compete, and the victory flag changes sides. The marble is drawn to the bottom of its well in the egg carton. But shaking the carton hard enough rocks the marble out of the well. That’s binding energy vs. entropy. A driver wants to cruise fast. But the driver brakes to avoid hitting the bumper in front of him. That’s speed vs. safety. Fires propagate from tree to tree, but they can exhaust their fuel, or rain can wet the trees. Violent causes can spread, but the ideas can get stale, or online agents can shut down virtual terror cells. Both are examples of increasing vs. decreasing virality. Acting on a lone atom or individual, the forces cause only gradual change. But multiplied a thousandfold or millionfold, the change becomes the sudden snap of a system: a phase transition.

In his 1992 article, Dunbar listed measures of brain volume and average social group size for 38 species of lemurs, monkeys, and apes. He showed that if you plotted one measure of brain volume (size of neocortex) vs. social group size, the plot seemed to lie along a straight line—the bigger the brain, the larger the group. So Dunbar proposed a novel idea: the size of a species’ brain determines the optimal size of their social groups. Maintaining relationships, argued Dunbar, requires brain power. More relationships require more neurons. Extrapolating his straight line from primate brains to human brains, he found that the optimal human group size, if this hypothesis were true, would be an interesting number: 150.

As the small startup’s size gradually increases, it will eventually reach a breakeven point where the two incentives, pulling in opposing directions, are equal. Above that size, a behavior appears across the organization that favors killing loonshots and supporting franchises. Let’s call that behavior the Invisible Axe. The sudden emergence of that Invisible Axe is a phase transition.

The number of direct reports is called “management span.” In US companies, the average management span has been between five and seven for many years, although recent studies have suggested the span has grown as high as ten.

It’s also easy to see how span affects your choice. Imagine an organization with an enormous span, more than one hundred direct reports (we will discuss one such example in the next chapter). Promotions happen so rarely that it’s not worth spending any time politicking. With a span of two, however, you are constantly in competition with your peer. The career ladder may be always on your mind.

The greater your skill on the projects to which you have been assigned, which we can call project–skill fit, the more likely you are to choose project work. The lower your project–skill fit, the more likely you are to choose politics.

What matters for our purposes, in our simple-model organization, is the ratio between project–skill fit and return-on-politics. We will write that ratio, which is a measure of overall organizational “fitness,” as F. In high-fitness organizations, reward systems discourage politics and employees are well matched to their roles. As a result, they are eager to spend time on their projects—building the best coffee machine. In low-fitness organizations, politics strongly influence promotion decisions and employees are poorly matched to their assignments. As a result, they are inclined to spend time on politics.

For management span, let’s use the middle of the range mentioned earlier, six. And let’s plug in a typical step-up in salary from promotions of around 12 percent. We will come back to typical values of E and F later, but for now let’s consider an evenly balanced company, where equity and salary are equal fractions of pay (each 50 percent) and the skill and politics ratios are equal (F = 1). Plugging in these numbers we find That’s interesting. I mentioned recent studies have reported larger management spans. In 2014, a survey of 248 companies conducted by Deloitte (one of the Big Four global accounting firms) reported an average span between nine and eleven. Along with that increased span and responsibility, of course, comes an increased growth in pay. Let’s plug in a management span of ten and an aggressive, but not outrageous, average salary step-up of one-third (33 percent). We get Also very interesting. Brigham Young, Bill Gore, Malcolm Gladwell, and Robin Dunbar may have been onto something. For typical real-world values of the control parameters there is, in fact, a sudden change in incentives around the magic number 150. At that size, the balance of forces in the tug-of-war changes, and the system suddenly snaps from favoring a focus on loonshots to a focus on careers.

By 1950, the conflicts had been resolved and Congress established the backbone of today’s extended system of national research labs. The National Science Foundation, the National Institutes of Health, and similar agencies support public-interest science: research on the spread of disease, water purification, earthquake prediction, and so on. The agencies also support research in areas that might be called “market failures”: fields whose commercial futures are so uncertain or distant that no one company can afford to invest in them. Genetic engineering, for example, 50 years ago. Nuclear fusion, today.

McElroy had helped to develop the nation’s most successful consumer-product research lab. He understood that technologies buried in the lab that fail to transfer to the field, or labs that fail to respond quickly to feedback from the field, are useless. In other words, McElroy understood, and drafted into the charter for his new agency, the essence of the first two Bush-Vail rules: phase separation and dynamic equilibrium.

DARPA funded the creation of the first major computer graphics center. It chose the University of Utah. The group at Utah, described in chapter 5, was co-led by a former DARPA program manager, Ivan Sutherland. Sutherland supervised the computer graphics PhD thesis of Ed Catmull, the Pixar founder, who has said he was “profoundly influenced” by the DARPA model of nurturing creativity. DARPA funded another engineer, named Douglas Engelbart, who built the first computer mouse, the first bitmapped screens (early graphical interfaces), the first hypertext links, and demonstrated them in 1968 at what computer scientists now refer to as the “Mother of All Demos.” In 1970, much of Engelbart’s team left and joined a newly created research group, led by another former DARPA program manager, Bob Taylor. That was Xerox PARC—the birth center of much of the early personal computer industry. Taylor said he modeled that legendary research group on “the management principles developed at DARPA.” Former DARPA program managers or directors currently lead or have recently led research groups at Facebook, Google, Microsoft, IBM, Draper Laboratory, and MIT Lincoln Labs. This small group’s management principles have spread throughout both private industry and public research in the US, forming an extended web of DARPA.

The team’s solution had been to launch a network with a creative reward system. A total pot of $4,000 would be assigned to each balloon. If Susan spotted a red balloon and reported it on the MIT team website, she would win half the pot: $2,000. If Greg was the one who told Susan about the game, he would win half of the remaining pot: $1,000. If Karen was the one who told Greg about it, she would win half of the remainder: $500. And so on, so that everyone in the reporting chain would get a cut (any money left over from the $4,000 when the chain ended would be donated to charity). The brilliance of the system was that even people who never left their home and couldn’t possibly spot a balloon had a reason to pitch in and help MIT win. The links were all tracked through the team’s website (built in two days). Mostly, the balloon hunters reached out to local friends. But occasionally they reached out to connections far away. In other words, it was a small-world network:

Use soft equity DARPA replaces that traditional career-ladder carrot with another. Project managers are publicly identified and broadly known in their community. They are granted authority to choose their projects, negotiate contracts, manage timelines, and assign goals. The combination of visibility and autonomy creates a powerful motivating force: peer pressure.

That intangible can quickly become tangible. If your job is to partner with external peers to develop new ideas, and you are recognized by those peers as a strong manager, scientists or inventors or other creatives will want to work with you rather than your competitors. They will be more inclined to bring their next great loonshot to you rather than your competitor. That could make a very big difference to your career. And, of course, being well known and well regarded among peers creates the obvious benefit of future job offers. Partnerships reduce the return-on-politics in still another way. External peers are more likely to be impartial judges, not susceptible to politicking. That impartial view, of both successes and failures, is crucial for a strong system mindset, as discussed in chapter 5. Did a program fail because the underlying technology just didn’t work (hypothesis failure), or because the person running the project botched it up (operational failure)? Did a program succeed because the person did a terrific job, or did he make a bunch of critical mistakes and get lucky despite those mistakes? Fans watching a baseball game recognize the difference between a player who scores off a beautiful hit and a player who scores off a sure out that a fielder happened to let dribble through his legs. DARPA’s principles—elevated autonomy and visibility; a focus on the best external rather than internal ideas—won’t apply the same way to every company (most companies are not ...

One example is the growing practice of open innovation. In open innovation, companies jointly develop new ideas, technologies, or markets with customers (usually early adopters or superfans) or business partners (for example, suppliers and comarketers). The Red Balloon Challenge is an example of one organization recruiting the best minds across the nation to help it think through an important problem in network theory: how to rapidly mobilize groups. The practice is common in the tech world. Software companies routinely share unfinished products with tight-knit developer communities to quickly generate feedback. Biotech companies frequently work closely with university scientists (and, in a growing trend, patient groups) to develop products. Recently, the idea has spread beyond tech. Open innovation helped the Coors Brewing Company develop a cold-activated beer can—the mountain logo printed on the can changes from white to blue when the beer reaches the ideal drinking temperature. (According to Coors, that’s 43 to 50 degrees Fahrenheit.) The practice helped Kraft Foods develop melt-resistant chocolate. Parents can thank open innovation for summers free of sticky chocolate goo.

As research has become increasingly fast-paced, many companies have decided that the double bonus of open innovation, especially the long-term gain of a more nimble organization, now outweighs those of the closed, more secretive model.

The DARPA model is extreme: reduce career politics by eliminating careers. McKinsey approaches the same goal in a less extreme way. It retains careers but invests heavily in reducing the subjectivity of promotion decisions.

The importance of project–skill fit also changes how we should think about training. Managers usually invest in training employees with the end goal of better products or higher sales. Send a coffee machine designer to a workshop on product design and you will get better coffee machines. Send a sales manager to a marketing workshop and your sales may improve. But training employees has another benefit. A designer who has learned new techniques wants to practice them. A marketer with new skills wants to try them out. Training encourages spending time on projects, which reduces time spent on lobbying and networking. In other words, it improves organizational fitness.

Tilting the rewards more toward projects and away from promotion means celebrating results, not rank. Examples of celebrating rank include not just big increases in base salary, but any kind of special privilege: parking spots, a special cafeteria, trips to Hawaii for “executive workshops,” and so on.

But the most difficult job in redesigning incentives may be the business-world equivalent of the Hippocratic Oath: first do no harm. It is surprisingly easy to unintentionally create perverse incentives. Here’s an example from a slightly different context. When the Dead Sea Scrolls were first discovered by Bedouin shepherds in a desert cave near the Dead Sea in modern-day Israel, archaeologists offered to pay the shepherds for each new scrap they found. That encouraged the shepherds to rip any scrolls they found into tiny scraps. The archaeologists had the right idea in theory but didn’t think through the perverse incentive in practice.

I’d rather be worth 100 million euros, have fun now, and enjoy people’s respect when I am the senile chairman of my firm than be worth a billion and get paid fat dividends by a little **** with a Harvard MBA, who runs my firm and lectures me at board meetings.

The engineering group, which eventually included over five thousand people, was responsible for data storage. When Coughran joined, Google was backing up the internet daily. Shortly after, it added billions of emails (Gmail, 2004) and videos (YouTube, 2006). Traditional models for storing data wouldn’t work; Coughran needed radical solutions. He organized his team so that over a hundred engineers reported directly to him—at one point, the total reached 180. Each of the engineering directors in his group managed 30 or so people. The spans were wide and the controls were loose. He was encouraging experiments and nurturing loonshots. His teams succeeded: they developed the radical storage solutions Google uses to store billions of emails and videos. Some groups had competed, testing different solutions, but eventually the engineers had come together to support each other when those loonshots inevitably stumbled.

Taylor and Coughran understood about engineers what Catmull understood about film directors: creative talent responds best to feedback from other creative talent. Peers, rather than authority. Catmull designed a system for a group of peer film directors to regularly coalesce around a project and give its director advice—honest feedback from colleagues rather than marching orders from marketers or producers. Creatives are suspicious of those outside their faith. It’s similar in drug discovery: biologists and chemists respond best to criticism from their own kind, much less well to suggestions from MBAs.

For a thousand years, from the middle of the first millennium AD to the middle of the second, China and India dominated the world’s economy. Together, during this period, they averaged just over half of the world’s GDP. The five largest nation-states of Western Europe, by comparison, averaged somewhere between 1 and 2 percent. Paper and printing appeared in China centuries before they appeared in Europe. The magnetic compass, gunpowder, cannons, crankshafts, deep-well drilling, cast iron, paper currency, sophisticated astronomical observatories: China. The imperial civil service exams—over a million tested annually, less than 1 percent passing—had been creating a class of scholar-elites in China for nearly a thousand years before the first universities opened their doors in Europe. Estimates of literacy rates in China around that time range as high as 45 percent. In England, it was close to 6 percent. In the early part of the fifteenth century, the Chinese navy sailed to North Africa and back with the largest fleet and ships ever seen—28,000 men and 300 ships, the largest weighing about 3,100 tons. A few decades later, Christopher Columbus sailed with three small ships, the largest weighing about 100 tons.

But something odd happened over the course of that long period. The Chinese giant turned inward, to big projects requiring massive resources. A new capital (Beijing). The Great Wall. The Grand Canal. Franchise projects. The Chinese leaders outgrew their interest in easily dismissed crazy ideas. The motion of planets, for example, or the properties of gases. Loonshots. When the British approached China to expand trade in the eighteenth century, the Qianlong emperor wrote to King George III, “There is nothing we lack. We have never set much store on strange or ingenious objects, nor do we need any more of your country’s manufactures.” Not long after, one of those strange and ingenious ideas arrived off the coast of China, powering the British ship Nemesis. Within weeks, the British fleet destroyed the old and outdated wooden sailing junks of the Chinese navy. The Chinese empire never recovered. David’s slingshot was the steam engine.