Smarter Faster Better: The Secrets of Being Productive

by Charles Duhigg

“This can’t be true,” he said seconds before the plane hit the water. Then there are his last words, which make all the sense in the world once you realize Bonin was still grasping for useful mental models as the plane hurtled toward the waves: “But what’s happening?”

A decade after Beth Crandall interviewed the NICU nurses, two economists and a sociologist from MIT decided to study how, exactly, the most productive people build mental models.

The first was they tended to work on only five projects at once—a healthy load, but not extraordinary. There were other employees who handled ten or twelve projects at a time. But those employees had a lower profit rate than the superstars, who were more careful about how they invested their time.

The superstars weren’t choosing tasks that leveraged existing skills.30 Instead, they were signing up for projects that required them to seek out new colleagues and demanded new abilities. That’s why the superstars worked on only five projects at a time: Meeting new people and learning new skills takes a lot of additional hours.

The superstars were constantly telling stories about what they had seen and heard. They were, in other words, much more prone to generate mental models. They were more likely to throw out ideas during meetings, or ask colleagues to help them imagine how future conversations might unfold, or envision how a pitch should go. They came up with concepts for new products and practiced how they would sell them. They told anecdotes about past conversations and dreamed up far-fetched expansion plans. They were building mental models at a near constant rate.

Researchers have found similar results in dozens of other studies. People who know how to manage their attention and who habitually build robust mental models tend to earn more money and get better grades.

If you want to make yourself more sensitive to the small details in your work, cultivate a habit of imagining, as specifically as possible, what you expect to see and do when you get to your desk. Then you’ll be prone to notice the tiny ways in which real life deviates from the narrative inside your head.

Companies say such tactics are important in all kinds of settings, including if you’re applying for a job or deciding whom to hire. The candidates who tell stories are the ones every firm wants. “We look for people who describe their experiences as some kind of a narrative,” Andy Billings, a vice president at the video game giant Electronic Arts, told me. “It’s a tip-off that someone has an instinct for connecting the dots and understanding how the world works at a deeper level. That’s who everyone tries to get.”

After the crew’s visualization session, de Crespigny laid down some rules. “Everyone has a responsibility to tell me if you disagree with my decisions or think I’m missing anything.”

What if, de Crespigny thought to himself, I imagine this plane as a Cessna? What would I do then? “That moment is really the turning point,” Barbara Burian, a research psychologist at NASA who has studied Qantas Flight 32, told me. “When de Crespigny decided to take control of the mental model he was applying to the situation, rather than react to the computer, it shifted his mindset. Now, he’s deciding where to direct his focus instead of relying on instructions.

Today, Qantas Flight 32 is taught in flight schools and psychology classrooms as a case study of how to maintain focus during an emergency.

But this questionnaire is not intended to test personal organization. Rather, it’s designed to measure a personality trait known as “the need for cognitive closure,”5 which psychologists define as “the desire for a confident judgment on an issue, any confident judgment, as compared to confusion and ambiguity.”

Most people respond to this exam—which is called “the need for closure scale”—by demonstrating a preference for a mix of order and chaos in their lives.

Put differently, an instinct for decisiveness is great—until it’s not. When people rush toward decisions simply because it makes them feel like they are getting something done, missteps are more likely to occur.

Both Zeira and his chief lieutenant “lacked the patience for long and open discussions and regarded them as ‘bullshit,’” the historians Uri Bar-Joseph and Abraham Rabinovich wrote. Zeira would “humiliate officers who, in his opinion, came unprepared for meetings.

In the 1940s, GE had formalized a corporate goal-setting system that would eventually become a model around the world. By the 1960s, every GE employee was required to write out their objectives for the year in a letter to their manager. “Simply put,” historians at Harvard Business School wrote in 2011, “the manager’s letter required a job holder to write a letter to his or her superior indicating what the goals for the next time frame were, how the goals would be met, and what standards were to be expected. When the superior accepted this letter—usually after editing and discussion—it became the work ‘contract.’”18

“Some 400 laboratory and field studies [show] that specific, high goals lead to a higher level of task performance than do easy goals or vague, abstract goals such as the exhortation to ‘do one’s best,’” Locke and Latham wrote in 2006 in a review of goal-setting studies.

Many of the SMART goals the consultants found inside the factories were just as detailed—and just as trivial. Workers spent hours making sure their objectives satisfied every SMART criterion, but spent much less time making sure the goals were worth pursuing in the first place.

Aims such as SMART goals “can cause [a] person to have tunnel vision, to focus more on expanding effort to get immediate results,”

The only way to improve performance at the nuclear factory, Kerr thought, was to find a way to shake people out of their focus on short-term objectives. GE had recently started a series of meetings among top executives called “Work-Outs” that were designed to encourage people to think about bigger ambitions and more long-term plans.25 Kerr helped expand those meetings to factories’ rank-and-file.

Six months after Welch’s trip to Japan, every division at GE had a stretch goal.

Numerous academic studies have examined the impact of stretch goals, and have consistently found that forcing people to commit to ambitious, seemingly out-of-reach objectives can spark outsized jumps in innovation and productivity. A 1997 study of Motorola, for instance, found that the time it took engineers to develop new products fell tenfold after the company mandated stretch goals throughout the firm.36 A study of 3M said stretch goals helped spur such inventions as Scotch tape and Thinsulate.37 Stretch goals transformed Union Pacific, Texas Instruments, and public schools in Washington, D.C., and Los Angeles. Surveys of people who have lost large amounts of weight or have become marathon runners later in life have found that stretch goals are often integral to their success.

There is an important caveat to the power of stretch goals, however. Studies show that if a stretch goal is audacious, it can spark innovation. It can also cause panic and convince people that success is impossible because the goal is too big. There is a fine line between an ambition that helps people achieve something amazing and one that crushes morale. For a stretch goal to inspire, it often needs to be paired with something like the SMART system.

This lesson can extend to even the most mundane aspects of life. Take, for instance, to-do lists. “To-do lists are great if you use them correctly,” Timothy Pychyl, a psychologist at Carleton University, told me. “But when people say things like ‘I sometimes write down easy items I can cross off right away, because it makes me feel good,’ that’s exactly the wrong way to create a to-do list. That signals you’re using it for mood repair, rather than to become productive.”

So one solution is writing to-do lists that pair stretch goals and SMART goals. Come up with a menu of your biggest ambitions. Dream big and stretch. Describe the goals that, at first glance, seem impossible, such as starting a company or running a marathon.

Then choose one aim and start breaking it into short-term, concrete steps. Ask yourself: What realistic progress can you make in the next day, week, month? How many miles can you realistically run tomorrow and over the next three weeks? What are the specific, short-term steps along the path to bigger success? What timeline makes sense? Will you open your store in six months or a year? How will you measure your progress? Within psychology, these smaller ambitions are known as “proximal goals,” and repeated studies have shown that breaking a big ambition into proximal goals makes the large objective more likely to occur.

Yet Zeira’s craving for closure and his intolerance for revisiting questions once they were answered are among the biggest reasons why Israel failed to anticipate the attacks.

The software’s development had been overseen by a young man from Wall Street who had convinced the FBI to hire him by arguing that the bureau needed to draw on lessons from companies such as Toyota, and methods such as “lean manufacturing” and “agile programming.”6 He had promised he could get Sentinel working in less than two years with a handful of software engineers—and then he had delivered.

In 1994, two business school professors at Stanford began studying how, exactly, one creates an atmosphere of trust within a company.

Eventually, they collected enough data to conclude that most companies had cultures that fell into one of five categories.14, 15 One was a culture they referred to as the “star” model. At these firms, executives hired from elite universities or other successful companies and gave employees huge amounts of autonomy. Offices had fancy cafeterias and lavish perks. Venture capitalists loved star model companies because giving money to the A-Team, conventional wisdom held, was always the safest bet.

In fact, when Baron and Hannan looked at their data, they found the only culture that was a consistent winner were the commitment firms. Hands down, a commitment culture outperformed every other type of management style in almost every meaningful way. “Not one of the commitment firms we studied failed,” said Baron. “None of them, which is amazing in its own right. But they were also the fastest companies to go public, had the highest profitability ratios, and tended to be leaner, with fewer middle managers, because when you choose employees slowly, you have time to find people who excel at self-direction.”

In 1985, Car and Driver magazine printed an issue with the cover line “Hell Freezes Over,” announcing NUMMI’s accomplishments. The worst auto factory on earth had become one of the most productive plants in existence, using the same workers as before.

Even now NUMMI is cited inside business schools and by corporate chieftains as an example of what organizations can achieve when a commitment culture takes hold. Since NUMMI was founded, the “lean manufacturing” principles have infiltrated nearly every corner of American commerce, from Silicon Valley to Hollywood to healthcare. “I’m really glad I ended up my years as an autoworker at NUMMI,” Madrid said. “I went from being depressed, bored, people didn’t even know I existed, to seeing J. D. Power name NUMMI as a top quality plant.”

The Agile methodology, as it

came to be known, emphasized collaboration, frequent testing, rapid iteration, and pushing decision making to whoever was closest to a problem. It quickly revolutionized software development and now is the standard methodology among many tech firms.30

These approaches emerged in different industries, but they and other adaptations of lean manufacturing shared key attributes. Each was dedicated to devolving decision making to the person closest to a problem. They all encouraged collaboration by allowing teams to self-manage and self-organize. They emphatically insisted on a culture of commitment and trust.

The only rules, Fulgham told them, were that everyone had to make suggestions, anyone could declare a time-out if they thought a project was moving in the wrong direction, and the person closest to a problem had primary responsibility for figuring out how to solve it.

In recent years, however, as the success of Sentinel had attracted more notice within the bureau, officials had become increasingly committed to using lean and agile techniques throughout the agency. Commanders and field agents had embraced the philosophy that the person

closest to a question should be empowered to answer it.

This, ultimately, is one of the most important lessons of places such as NUMMI and the lean and agile philosophies: Employees work smarter and better when they believe they have more decision-making authority and when they believe their colleagues are committed to their success. A sense of control can fuel motivation, but for that drive to produce insights and innovations, people need to know their suggestions won’t be ignored, that their mistakes won’t be held against them. And they need to know that everyone else has their back.

Psychology, at that moment, was undergoing a transformation brought on by discoveries in cognitive sciences that were bringing a scientific rigor to understanding behaviors that had long seemed immune to methodical analysis. Psychologists and economists were working together to understand the codes that explain why people do what they do. Some of the most exciting research—work that would eventually win a Nobel Prize—was focused on studying how people make decisions.4

Two of the universities, however, took a different tack. A group of psychologists, statisticians, and political scientists from the University of Pennsylvania and the University of California–Berkeley, working together, decided to use the government’s money as an opportunity to see if they could train regular people to become better forecasters.9 This group called themselves “the Good Judgment Project,” and rather than recruit specialists, the GJP solicited thousands of people—lawyers, housewives, master’s students, voracious newspaper readers, and enrolled them in online forecasting classes that taught them different ways of thinking about the future. Then, after the training, those participants were asked to answer the same foreign affairs questions as the experts.10

Eventually, the GJP published their findings: Giving participants even brief training sessions in research and statistical techniques—teaching them various ways of thinking about the future—boosted the accuracy of their predictions. And most surprising, a particular kind of lesson—training in how to think probabilistically—significantly increased people’s abilities to forecast the future.11

So there were three potential futures to consider: Sarkozy could earn 67 percent, 25 percent, or 45 percent of the votes cast. In one scenario, he would win easily, in another he would lose by a wide margin, and the third scenario was a relatively close call. How do you combine those contradictory outcomes into one prediction? “You simply average your estimates based on incumbency, approval ratings, and economic growth rates,” the training explained. “If you have no basis for treating one variable as more important than another, use equal weighting. This approach leads you to predict [(67% + 25% + 45%)/3] = approximately a 46% chance of reelection.”

This is the most basic kind of probabilistic thinking, a simplistic example that teaches an underlying idea: Contradictory futures can be combined into a single prediction. As this kind of logic gets more sophisticated, experts usually begin speaking about various outcomes as probability curves—graphs that show the distribution of potential futures. For instance, if someone was asked to guess how many seats Sarkozy’s party was going to win in the French parliament, an expert might describe the possible outcomes as a curve that shows how the likelihood of winning parliamentary seats is linked to Sarkozy’s odds of remaining president:

Throughout, a central idea was repeated again and again. The future isn’t one thing. Rather, it is a multitude of possibilities that often contradict one another until one of them comes true. And those futures can be combined in order for someone to predict which one is more likely to occur.

Simply exposing participants to probabilistic training was associated with as much as a 50 percent increase in the accuracy of their predictions, the GJP researchers wrote.

In the late 1990s, a professor of cognitive science at the Massachusetts Institute of Technology named Joshua Tenenbaum began a large-scale examination of the casual ways that people make everyday predictions.

“How do our minds get so much from so little?” Tenenbaum wrote in a paper published in the journal Science in 2011. “Any parent knows, and scientists have confirmed, that typical 2-year-olds can learn how to use a new word such as ‘horse’ or ‘hairbrush’ from seeing just a few examples.”22 To a two-year-old, horses and hairbrushes have a great deal in common. The words sound similar. In pictures, they both have long bodies with a series of straight lines—in one case legs, in the other bristles—extruding outward. They come in a range of colors. And yet, though a child might have seen only one picture of a horse and used only one hairbrush, she can quickly learn the difference between those words.

Most people intuitively understand that they need to apply different kinds of reasoning to predicting different kinds of events. We know that box office totals and life spans require different types of estimations, even if we don’t know anything about medical statistics or entertainment industry trends. Tenenbaum and Griffiths were curious to find out how people intuitively learn to make such estimations. So they found events with distinct patterns, from box office grosses to life spans, as well as the average length of poems, the careers of congressmen (which adhere to an Erlang distribution), and the length of time a cake needs to bake (which has no strong pattern).24