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

by Safi Bahcall

Steve Jobs 1.0

Most depictions of Jobs portray him as pretty much unchanged from the time he convinced Woz to start a company to the time he died. This is much more nuanced and, I suspect, more accurate.

We can think of analyzing why a move is bad—why pawn-takes-bishop, for example, lost the game—as level 1 strategy, or outcome mindset. After a bad move costs him a game, however, Kasparov analyzes not just why the move was bad, but how he should change the decision process behind the move. In other words, how he decided on that move, in that moment, in the context of that opponent, and what that means for how he should change his decision-making and game-preparation routine in the future. Analyzing the decision process behind a move I’ll call level 2 strategy, or system mindset.

The principle applies broadly. You can analyze why an investment went south. The company’s balance sheet was too weak, for example. That’s outcome mindset. But you will gain much more from analyzing the process by which you arrived at the decision to invest. What’s on your diligence list? How do you go through that list? Did something distract you or cause you to overlook or ignore that item on the list? What should you change about what’s on your list or how you conduct your analyses or how you draw your conclusions—the process behind the decision to invest—to ensure that mistake won’t happen again? That’s system mindset.

System mindset means carefully examining the quality of decisions, not just the quality of outcomes. A failed outcome, for example, does not necessarily mean the decision or decision process behind it was bad. There are good decisions with bad outcomes. Those are intelligent risks, well taken, that didn’t play out. For example, if a lottery is paying out at 100 to 1, but only three tickets are sold, one of which will win, then yes, purchasing one of those three tickets is a good decision. Even if you end up holding one of the two that did not win. Under those same conditions, you should always make that same decision.

This could be one of the big insights in the book.

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.

“The whole notion of how you build a company is fascinating,” Jobs told his biographer, Walter Isaacson. “I discovered that the best innovation is sometimes the company, the way you organize.” Jobs arrived at the same conclusion that the military historian James Phinney Baxter did half a century earlier, reflecting on the success of Bush’s system in turning the course of World War II: “If a miracle had been accomplished anywhere along the line,” Baxter wrote, “it was in the field of organization, where conditions had been created under which success was more likely to be achieved in time.”

The example of Xerox PARC has appeared several times above. Before pulling together what we’ve learned from the five stories in the five chapters of part one, it’s worth briefly touching on what happened at PARC. It highlights another side—the inverse—of the Moses Trap.

This story is well told in booklength form in Dealers of Lightning: Xerox PARC and the Dawn of the Computer Age by Michael A. Hiltzik (https://amzn.to/2Wp0guk)

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 one emergent behavior to another is exactly what we mean by a phase transition.

The terrific thing about the science of emergence is that once we understand a phase transition, we can begin to manage it. We can design stronger materials, build better highways, create safer forests—and engineer more innovative teams and companies.

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.

The physicist Richard Feynman once said, “Learn by trying to understand simple things in terms of other ideas—always honestly and directly.”

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.

Identifying those other control parameters is the key to changing when systems will snap: when solids will melt, when traffic will jam, or when teams will begin rejecting loonshots.

To understand macro patterns of forest fires, it turns out, you need just two key parameters. I labeled the horizontal axis in the forest fire phase diagram a few pages before “wind speed.” But a better term, which captures what really matters for the spread of fires, might be “virality.” High wind speeds, dry ground, and low humidity increase virality: they make fires more likely to spread. Low wind speeds, moist ground, and high humidity decrease virality: they make fires less likely to spread.

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.

Textbooks on the behavior of stock markets often begin, like the Bible, with a declaration of faith. In the beginning, there were efficient markets. Markets capture all information into their prices; deviations from efficient prices are random (often called “random walks”). Bad actors can spoil the show (insider trading; manipulation), but with good behavior and proper enforcement, markets will revert to pure, perfectly efficient form. Much of modern finance theory, including estimates of risk and the pricing of stock options, is based on this belief.

When extreme outcomes happen much more frequently than you expect, the probability distribution develops what statisticians call a “fat tail.” Physicists love fat tails. Random systems with no hidden connections, like coin tosses, have thin tails. They’re kind of boring. Fat tails signal interesting dynamics in a network. That might be a network of trees through which a fire spreads. Or it might be a network of people trading stocks, through which an idea spreads—in other words, a financial market. Physicists, including Johnson, had been studying the fat tails in financial markets for years, trying to make sense of them. Market crashes (Greenspan’s “notably rare exceptions”), hedge fund collapses, and sudden bank defaults are often caused by, or at least associated with, fat tails.

Most researchers tried to solve the fat tail problem by studying the behavior of individual traders. Johnson, instead, looked at clusters. He asked what would happen if we assumed traders acted in cliques: small groups whose members all behave the same way, that is, they make the same buy or sell decisions. (The evidence for groupthink in markets, from tulip mania to the internet bubble, is strong.) The clusters need not be permanent. Just like cliques in high school, members come and go, trading cliques form and dissolve, they merge with other cliques or split into two. Imagine bringing a pot of water to a boil. Just before the boiling point, bubbles of gas appear. Those bubbles grow or collapse, merge with other bubbles or fragment, all while new bubbles are forming. Johnson proposed that trading cliques act like those percolating bubbles.

First, the results suggest that rather than having to closely monitor millions of individual online behaviors, focusing on the behavior of a small number of cells, which may number in the tens or hundreds, is a better use of time and resources.

Second, recently developed mathematical techniques can identify “superspreaders”: clusters with the greatest influence. (Those are not always the ones with the greatest number of links.)

A third strategy is to increase the fragmentation rate—the rate at which clusters dissolve. The goal is to back a terror network away from the contagion transition, just as prescribed burns back a forest away from its contagion 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.

of Gore Associates, the maker of Gore-Tex fabric, which limits how many people work together in one building. “We put a hundred and fifty parking spaces in the lot,” the president, Bill Gore, said. “When people start parking on the grass, we know it’s time to build a new plant.”

Company name is W. L. Gore and Associates

Just because a theory might be a bit wacky, however, doesn’t mean there isn’t something to the observation. In physics language, you might have the right observation but the wrong theory. Dozens of theories of superconductivity, for example, came and went; the observation never changed. Well before Dunbar’s theory and other social models, Bill Gore and Brigham Young limited groups to 150 people. We intuitively understand that something changes inside teams and companies as they cross a certain threshold in size. But the volume of our neocortex might have nothing to do with it. Let’s see how the science of emergence offers an alternative explanation—and more.

In the previous chapter, I described an important step in developing new insights into forest fires: creating a model that was simple, but not simplistic. Hemingway wrote that “the dignity of movement of an iceberg is due to only one-eighth of it being above water.” He called it his Theory of Omission. The power of beautiful prose comes from what you leave out. In science, it’s the same. The power of a beautiful model comes from what you choose to omit.

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.

Are these the only possible control parameters? What could we add? Or take away?

Below the critical threshold, the magic number in the equation above, incentives encourage individuals to unite around loonshots. When group size crosses that magic number, incentives shift toward favoring a focus on careers: the politics of promotion (#1 → #2 in the diagram below). For typical group structures, that number may be roughly 150. But by adjusting the parameters of structure—equity fraction, fitness, management span, compensation growth rate—we can raise that magic number (which is why the dashed line is angled; if we couldn’t adjust the number then the dashed line would be purely vertical). Another way to say the same thing: groups much larger than 150 people, which are stuck in the career politics phase (#2 in the diagram), can restore the focus on loonshots by adjusting their structure (#2 → #3).

As mentioned earlier, the ideas apply not only to small teams that want to remain entrepreneurial as they grow but also to large organizations that wish to create powerful teams for nurturing loonshots internally.

It seems to me like W. L. Gore would be a good model. Essentially, they are a large organization made up of 150 person units.

Before we dive in, it’s worth keeping in mind that revving the creative engine to fire at higher speeds—nurturing more loonshots, with greater productivity and efficiency—means more ideas and more experiments, which also means, inevitably, more failed experiments. That’s not the right choice for every team.

On February 7, 1958, McElroy’s new organization officially began operations. He called it the Advanced Research Projects Agency. Bush’s OSRD had been reborn.

This is an excellent summary of how OSRD was reincarnated as ARPA.

In traditional structures, the career ladder is the ultimate carrot. Meet this goal, get a bigger office, a higher salary, more staff, and so on. That same career-ladder carrot, of course, encourages the weed of politics to spread.

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 faced with problems that might be solved by a giant nuclear suppository). But every organization can find opportunities to increase autonomy, visibility, and soft equity.

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.

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.

Examining the unintended effects of well-intentioned goals has not received much study. One exception is a recent article, “Goals Gone Wild,” which traces a handful of famous business disasters to poorly constructed goals.

Rewarding one person for designing one coffee machine is a simple example. Somewhere between simple one-person rewards and wasteful free-rider rewards given to everyone lies a valuable and critical sweet spot: rewarding teams for collective outcomes. Designing a team reward is tricky. It requires carefully thinking through both the benefits and possible perverse incentives of many different choices. The analysis goes beyond the normal experience of a rubber-stamper payroll person. In other words, it requires a strategic chief incentives officer.

SUMMARY: RAISE THE MAGIC NUMBER • Reduce the return on politics: Make lobbying for compensation and promotion decisions difficult. Find ways to make those decisions less dependent on an employee’s manager and more independently assessed. • Use soft equity: Identify and apply the nonfinancial rewards that make a big difference. For example: peer recognition, intrinsic motivators. • Increase project–skill fit: Invest in the people and processes that will scan for a mismatch between employees’ skills and their assigned projects. Adjust roles or transfer employees between groups when mismatches are found. The goal is employees stretched neither too much nor too little by their roles. • Fix the middle: Identify and fix perverse incentives, the unintended consequences of well-intentioned rewards. Pay special attention to the dangerous middle-manager levels, the weakest point in the battle between loonshots and politics. Shift away from incentives that encourage battles for promotion and toward incentives centered on outcomes. Celebrate results, not rank. • Bring a gun to a knife fight: Competitors in the battle for talent and loonshots may be using outmoded incentive systems. Bring in a specialist in the subtleties of the art—a chief incentives officer. • Fine-tune the spans: Widen management spans in loonshot groups (but not in franchise groups) to encourage looser controls, more experiments, and peer-to-peer problem solving.

Physicians and hospitals are often paid more for C-sections than for vaginal births. The greater the difference, one study found, the higher the C-section rate. The results have led to a change in policies in some hospitals, requiring equal pay for both types of deliveries.

What we have done is explore the rational reasons why teams arrive at those decisions. In other words, why teams and companies, not just individuals, are “predictably irrational.”

The aim of this book is not to replace the idea that certain patterns of behavior are helpful (celebrating victories, for example) and others are less so (screaming), but to complement it. In part one we saw how the lessons on structure from Bush and Vail, and an idea borrowed from a chess champion, can help us defeat chaos, stagnation, and the Moses Trap. In part two we saw how the science of phase transitions creates fresh insights into building more innovative groups. And we have seen how these ideas have come together, in a handful of examples, to win wars, cure diseases, and transform industries.

When you have learned to explain simpler things, so you have learned what an explanation really is, you can then go on to more subtle questions. —Richard Feynman

So Kepler decided to reject “that which exists only in the mind, and which Nature entirely refuses to accept”: the assumption of circular motion.

Reminds me of the Feynman quote that "Nature will not be fooled."

“Kepler’s lifework was possible,” Einstein wrote, “only once he succeeded in freeing himself to a great extent of the intellectual traditions into which he was born.”

Most drugs we use come from nature—plants, animals, or microbes. The active ingredients in these natural-product drugs are relatively small molecules: aspirin, from willow tree bark, has just 21 atoms; morphine, from the opium poppy, has 40; Akira Endo’s statin, from a mold, has 62. They fight disease by acting on proteins, the much larger molecules that do much of the work in a cell. When proteins malfunction, cells can spin out of control, causing disease. Natural-product drugs work by jamming into tiny crevices in overactive proteins, stopping them like a small wrench inserted into the guts of a giant, out-of-control robot. Aspirin blocks proteins involved in inflammation.

Loonshots vs. Disruption

I think this section presents a misreading of Christensen. He talks about disruptive strategies, not disruptive products. The strategy is specific. A company goes after the non-consumption part of the market with an "inferior" product.

APPENDIX A SUMMARY: THE

This is an excellent summary.

Analyzing why a team arrived at a decision can be both unfamiliar and uncomfortable. It requires self-awareness from team members; the self-confidence to acknowledge mistakes, especially interpersonal ones; and the candor and trust to give and receive delicate feedback. The process is likely to be more efficient, and less painful, when it is mediated by a neutral expert from outside the team.

Many companies are adopting the "After-Action Review." What's here is weak. Seek out some material on after action reviews.

Bring in a specialist in the subtleties of the art—a chief incentives officer.

Dear god, another Chief Officer. Soon they will outnumber the stars in the heavens.

At the edge of obsession, relationships are often the first to go. But they are usually our most important need. When I catch myself making that mistake, I think back to those end-of-life conversations.