بخش 1-1

PART 1

The Idea

Chapter One

Deep Work Is Valuable

As Election Day loomed in 2012, traffic at the New York Times website spiked, as is normal during moments of national importance. But this time, something was different. A wildly disproportionate fraction of this traffic—more than 70 percent by some reports—was visiting a single location in the sprawling domain. It wasn’t a front-page breaking news story, and it wasn’t commentary from one of the paper’s Pulitzer Prize–winning columnists; it was instead a blog run by a baseball stats geek turned election forecaster named Nate Silver. Less than a year later, ESPN and ABC News lured Silver away from the Times (which tried to retain him by promising a staff of up to a dozen writers) in a major deal that would give Silver’s operation a role in everything from sports to weather to network news segments to, improbably enough, Academy Awards telecasts. Though there’s debate about the methodological rigor of Silver’s hand-tuned models, there are few who deny that in 2012 this thirty-five-year-old data whiz was a winner in our economy.

Another winner is David Heinemeier Hansson, a computer programming star who created the Ruby on Rails website development framework, which currently provides the foundation for some of the Web’s most popular destinations, including Twitter and Hulu. Hansson is a partner in the influential development firm Basecamp (called 37signals until 2014). Hansson doesn’t talk publicly about the magnitude of his profit share from Basecamp or his other revenue sources, but we can assume they’re lucrative given that Hansson splits his time between Chicago, Malibu, and Marbella, Spain, where he dabbles in high-performance race-car driving.

Our third and final example of a clear winner in our economy is John Doerr, a general partner in the famed Silicon Valley venture capital fund Kleiner Perkins Caufield & Byers. Doerr helped fund many of the key companies fueling the current technological revolution, including Twitter, Google, Amazon, Netscape, and Sun Microsystems. The return on these investments has been astronomical: Doerr’s net worth, as of this writing, is more than $3 billion.

Why have Silver, Hansson, and Doerr done so well? There are two types of answers to this question. The first are micro in scope and focus on the personality traits and tactics that helped drive this trio’s rise. The second type of answers are more macro in that they focus less on the individuals and more on the type of work they represent. Though both approaches to this core question are important, the macro answers will prove most relevant to our discussion, as they better illuminate what our current economy rewards.

To explore this macro perspective we turn to a pair of MIT economists, Erik Brynjolfsson and Andrew McAfee, who in their influential 2011 book, Race Against the Machine, provide a compelling case that among various forces at play, it’s the rise of digital technology in particular that’s transforming our labor markets in unexpected ways. “We are in the early throes of a Great Restructuring,” Brynjolfsson and McAfee explain early in their book. “Our technologies are racing ahead but many of our skills and organizations are lagging behind.” For many workers, this lag predicts bad news. As intelligent machines improve, and the gap between machine and human abilities shrinks, employers are becoming increasingly likely to hire “new machines” instead of “new people.” And when only a human will do, improvements in communications and collaboration technology are making remote work easier than ever before, motivating companies to outsource key roles to stars—leaving the local talent pool underemployed.

This reality is not, however, universally grim. As Brynjolfsson and McAfee emphasize, this Great Restructuring is not driving down all jobs but is instead dividing them. Though an increasing number of people will lose in this new economy as their skill becomes automatable or easily outsourced, there are others who will not only survive, but thrive—becoming more valued (and therefore more rewarded) than before. Brynjolfsson and McAfee aren’t alone in proposing this bimodal trajectory for the economy. In 2013, for example, the George Mason economist Tyler Cowen published Average Is Over, a book that echoes this thesis of a digital division. But what makes Brynjolfsson and McAfee’s analysis particularly useful is that they proceed to identify three specific groups that will fall on the lucrative side of this divide and reap a disproportionate amount of the benefits of the Intelligent Machine Age. Not surprisingly, it’s to these three groups that Silver, Hansson, and Doerr happen to belong. Let’s touch on each of these groups in turn to better understand why they’re suddenly so valuable.

The High-Skilled Workers

Brynjolfsson and McAfee call the group personified by Nate Silver the “high-skilled” workers. Advances such as robotics and voice recognition are automating many low-skilled positions, but as these economists emphasize, “other technologies like data visualization, analytics, high speed communications, and rapid prototyping have augmented the contributions of more abstract and data-driven reasoning, increasing the values of these jobs.” In other words, those with the oracular ability to work with and tease valuable results out of increasingly complex machines will thrive. Tyler Cowen summarizes this reality more bluntly: “The key question will be: are you good at working with intelligent machines or not?” Nate Silver, of course, with his comfort in feeding data into large databases, then siphoning it out into his mysterious Monte Carlo simulations, is the epitome of the high-skilled worker. Intelligent machines are not an obstacle to Silver’s success, but instead provide its precondition.

The Superstars

The ace programmer David Heinemeier Hansson provides an example of the second group that Brynjolfsson and McAfee predict will thrive in our new economy: “superstars.” High-speed data networks and collaboration tools like e-mail and virtual meeting software have destroyed regionalism in many sectors of knowledge work. It no longer makes sense, for example, to hire a full-time programmer, put aside office space, and pay benefits, when you can instead pay one of the world’s best programmers, like Hansson, for just enough time to complete the project at hand. In this scenario, you’ll probably get a better result for less money, while Hansson can service many more clients per year, and will therefore also end up better off.

The fact that Hansson might be working remotely from Marbella, Spain, while your office is in Des Moines, Iowa, doesn’t matter to your company, as advances in communication and collaboration technology make the process near seamless. (This reality does matter, however, to the less-skilled local programmers living in Des Moines and in need of a steady paycheck.) This same trend holds for the growing number of fields where technology makes productive remote work possible—consulting, marketing, writing, design, and so on. Once the talent market is made universally accessible, those at the peak of the market thrive while the rest suffer.

In a seminal 1981 paper, the economist Sherwin Rosen worked out the mathematics behind these “winner-take-all” markets. One of his key insights was to explicitly model talent—labeled, innocuously, with the variable q in his formulas—as a factor with “imperfect substitution,” which Rosen explains as follows: “Hearing a succession of mediocre singers does not add up to a single outstanding performance.” In other words, talent is not a commodity you can buy in bulk and combine to reach the needed levels: There’s a premium to being the best. Therefore, if you’re in a marketplace where the consumer has access to all performers, and everyone’s q value is clear, the consumer will choose the very best. Even if the talent advantage of the best is small compared to the next rung down on the skill ladder, the superstars still win the bulk of the market.

In the 1980s, when Rosen studied this effect, he focused on examples like movie stars and musicians, where there existed clear markets, such as music stores and movie theaters, where an audience has access to different performers and can accurately approximate their talent before making a purchasing decision. The rapid rise of communication and collaboration technologies has transformed many other formerly local markets into a similarly universal bazaar. The small company looking for a computer programmer or public relations consultant now has access to an international marketplace of talent in the same way that the advent of the record store allowed the small-town music fan to bypass local musicians to buy albums from the world’s best bands. The superstar effect, in other words, has a broader application today than Rosen could have predicted thirty years ago. An increasing number of individuals in our economy are now competing with the rock stars of their sectors.

The Owners

The final group that will thrive in our new economy—the group epitomized by John Doerr—consists of those with capital to invest in the new technologies that are driving the Great Restructuring. As we’ve understood since Marx, access to capital provides massive advantages. It’s also true, however, that some periods offer more advantages than others. As Brynjolfsson and McAfee point out, postwar Europe was an example of a bad time to be sitting on a pile of cash, as the combination of rapid inflation and aggressive taxation wiped out old fortunes with surprising speed (what we might call the “Downton Abbey Effect”).

The Great Restructuring, unlike the postwar period, is a particularly good time to have access to capital. To understand why, first recall that bargaining theory, a key component in standard economic thinking, argues that when money is made through the combination of capital investment and labor, the rewards are returned, roughly speaking, proportional to the input. As digital technology reduces the need for labor in many industries, the proportion of the rewards returned to those who own the intelligent machines is growing. A venture capitalist in today’s economy can fund a company like Instagram, which was eventually sold for a billion dollars, while employing only thirteen people. When else in history could such a small amount of labor be involved in such a large amount of value? With so little input from labor, the proportion of this wealth that flows back to the machine owners—in this case, the venture investors—is without precedent. It’s no wonder that a venture capitalist I interviewed for my last book admitted to me with some concern, “Everyone wants my job.” Let’s pull together the threads spun so far: Current economic thinking, as I’ve surveyed, argues that the unprecedented growth and impact of technology are creating a massive restructuring of our economy. In this new economy, three groups will have a particular advantage: those who can work well and creatively with intelligent machines, those who are the best at what they do, and those with access to capital.

To be clear, this Great Restructuring identified by economists like Brynjolfsson, McAfee, and Cowen is not the only economic trend of importance at the moment, and the three groups mentioned previously are not the only groups who will do well, but what’s important for this book’s argument is that these trends, even if not alone, are important, and these groups, even if they are not the only such groups, will thrive. If you can join any of these groups, therefore, you’ll do well. If you cannot, you might still do well, but your position is more precarious.

The question we must now face is the obvious one: How does one join these winners? At the risk of quelling your rising enthusiasm, I should first confess that I have no secret for quickly amassing capital and becoming the next John Doerr. (If I had such secrets, it’s unlikely I’d share them in a book.) The other two winning groups, however, are accessible. How to access them is the goal we tackle next.

How to Become a Winner in the New Economy

I just identified two groups that are poised to thrive and that I claim are accessible: those who can work creatively with intelligent machines and those who are stars in their field. What’s the secret to landing in these lucrative sectors of the widening digital divide? I argue that the following two core abilities are crucial.

Two Core Abilities for Thriving in the New Economy

1. The ability to quickly master hard things.

2. The ability to produce at an elite level, in terms of both quality and speed.

Let’s begin with the first ability. To start, we must remember that we’ve been spoiled by the intuitive and drop-dead-simple user experience of many consumer-facing technologies, like Twitter and the iPhone. These examples, however, are consumer products, not serious tools: Most of the intelligent machines driving the Great Restructuring are significantly more complex to understand and master.

Consider Nate Silver, our earlier example of someone who thrives by working well with complicated technology. If we dive deeper into his methodology, we discover that generating data-driven election forecasts is not as easy as typing “Who will win more votes?” into a search box. He instead maintains a large database of poll results (thousands of polls from more than 250 pollsters) that he feeds into Stata, a popular statistical analysis system produced by a company called StataCorp. These are not easy tools to master. Here, for example, is the type of command you need to understand to work with a modern database like Silver uses: CREATE VIEW cities AS SELECT name, population, altitude FROM capitals UNION SELECT name, population, altitude FROM non_capitals;

Databases of this type are interrogated in a language called SQL. You send them commands like the one shown here to interact with their stored information. Understanding how to manipulate these databases is subtle. The example command, for example, creates a “view”: a virtual database table that pulls together data from multiple existing tables, and that can then be addressed by the SQL commands like a standard table. When to create views and how to do so well is a tricky question, one of many that you must understand and master to tease reasonable results out of real-world databases.

Sticking with our Nate Silver case study, consider the other technology he relies on: Stata. This is a powerful tool, and definitely not something you can learn intuitively after some modest tinkering. Here, for example, is a description of the features added to the most recent version of this software: “Stata 13 adds many new features such as treatment effects, multilevel GLM, power and sample size, generalized SEM, forecasting, effect sizes, Project Manager, long strings and BLOBs, and much more.” Silver uses this complex software—with its generalized SEM and BLOBs—to build intricate models with interlocking parts: multiple regressions, conducted on custom parameters, which are then referenced as custom weights used in probabilistic expressions, and so on.

The point of providing these details is to emphasize that intelligent machines are complicated and hard to master.* To join the group of those who can work well with these machines, therefore, requires that you hone your ability to master hard things. And because these technologies change rapidly, this process of mastering hard things never ends: You must be able to do it quickly, again and again.

This ability to learn hard things quickly, of course, isn’t just necessary for working well with intelligent machines; it also plays a key role in the attempt to become a superstar in just about any field—even those that have little to do with technology. To become a world-class yoga instructor, for example, requires that you master an increasingly complex set of physical skills. To excel in a particular area of medicine, to give another example, requires that you be able to quickly master the latest research on relevant procedures. To summarize these observations more succinctly: If you can’t learn, you can’t thrive.

Now consider the second core ability from the list shown earlier: producing at an elite level. If you want to become a superstar, mastering the relevant skills is necessary, but not sufficient. You must then transform that latent potential into tangible results that people value. Many developers, for example, can program computers well, but David Hansson, our example superstar from earlier, leveraged this ability to produce Ruby on Rails, the project that made his reputation. Ruby on Rails required Hansson to push his current skills to their limit and produce unambiguously valuable and concrete results.

This ability to produce also applies to those looking to master intelligent machines. It wasn’t enough for Nate Silver to learn how to manipulate large data sets and run statistical analyses; he needed to then show that he could use this skill to tease information from these machines that a large audience cared about. Silver worked with many stats geeks during his days at Baseball Prospectus, but it was Silver alone who put in the effort to adapt these skills to the new and more lucrative territory of election forecasting. This provides another general observation for joining the ranks of winners in our economy: If you don’t produce, you won’t thrive—no matter how skilled or talented you are.

Having established two abilities that are fundamental to getting ahead in our new, technology-disrupted world, we can now ask the obvious follow-up question: How does one cultivate these core abilities? It’s here that we arrive at a central thesis of this book: The two core abilities just described depend on your ability to perform deep work. If you haven’t mastered this foundational skill, you’ll struggle to learn hard things or produce at an elite level.

The dependence of these abilities on deep work isn’t immediately obvious; it requires a closer look at the science of learning, concentration, and productivity. The sections ahead provide this closer look, and by doing so will help this connection between deep work and economic success shift for you from unexpected to unimpeachable.

Deep Work Helps You Quickly Learn Hard Things

“Let your mind become a lens, thanks to the converging rays of attention; let your soul be all intent on whatever it is that is established in your mind as a dominant, wholly absorbing idea.”

This advice comes from Antonin-Dalmace Sertillanges, a Dominican friar and professor of moral philosophy, who during the early part of the twentieth century penned a slim but influential volume titled The Intellectual Life. Sertillanges wrote the book as a guide to “the development and deepening of the mind” for those called to make a living in the world of ideas. Throughout The Intellectual Life, Sertillanges recognizes the necessity of mastering complicated material and helps prepare the reader for this challenge. For this reason, his book proves useful in our quest to better understand how people quickly master hard (cognitive) skills.

To understand Sertillanges’s advice, let’s return to the quote from earlier. In these words, which are echoed in many forms in The Intellectual Life, Sertillanges argues that to advance your understanding of your field you must tackle the relevant topics systematically, allowing your “converging rays of attention” to uncover the truth latent in each. In other words, he teaches: To learn requires intense concentration. This idea turns out to be ahead of its time. In reflecting on the life of the mind in the 1920s, Sertillanges uncovered a fact about mastering cognitively demanding tasks that would take academia another seven decades to formalize.

This task of formalization began in earnest in the 1970s, when a branch of psychology, sometimes called performance psychology, began to systematically explore what separates experts (in many different fields) from everyone else. In the early 1990s, K. Anders Ericsson, a professor at Florida State University, pulled together these strands into a single coherent answer, consistent with the growing research literature, that he gave a punchy name: deliberate practice.

Ericsson opens his seminal paper on the topic with a powerful claim: “We deny that these differences [between expert performers and normal adults] are immutable… Instead, we argue that the differences between expert performers and normal adults reflect a life-long period of deliberate effort to improve performance in a specific domain.” American culture, in particular, loves the storyline of the prodigy (“Do you know how easy this is for me!?” Matt Damon’s character famously cries in the movie Good Will Hunting as he makes quick work of proofs that stymie the world’s top mathematicians). The line of research promoted by Ericsson, and now widely accepted (with caveats*), de-stabilizes these myths. To master a cognitively demanding task requires this specific form of practice—there are few exceptions made for natural talent. (On this point too, Sertillanges seems to have been ahead of his time, arguing in The Intellectual Life, “Men of genius themselves were great only by bringing all their power to bear on the point on which they had decided to show their full measure.” Ericsson couldn’t have said it better.) This brings us to the question of what deliberate practice actually requires. Its core components are usually identified as follows: (1) your attention is focused tightly on a specific skill you’re trying to improve or an idea you’re trying to master; (2) you receive feedback so you can correct your approach to keep your attention exactly where it’s most productive. The first component is of particular importance to our discussion, as it emphasizes that deliberate practice cannot exist alongside distraction, and that it instead requires uninterrupted concentration. As Ericsson emphasizes, “Diffused attention is almost antithetical to the focused attention required by deliberate practice” (emphasis mine).

As psychologists, Ericsson and the other researchers in his field are not interested in why deliberate practice works; they’re just identifying it as an effective behavior. In the intervening decades since Ericsson’s first major papers on the topic, however, neuroscientists have been exploring the physical mechanisms that drive people’s improvements on hard tasks. As the journalist Daniel Coyle surveys in his 2009 book, The Talent Code, these scientists increasingly believe the answer includes myelin—a layer of fatty tissue that grows around neurons, acting like an insulator that allows the cells to fire faster and cleaner. To understand the role of myelin in improvement, keep in mind that skills, be they intellectual or physical, eventually reduce down to brain circuits. This new science of performance argues that you get better at a skill as you develop more myelin around the relevant neurons, allowing the corresponding circuit to fire more effortlessly and effectively. To be great at something is to be well myelinated.

This understanding is important because it provides a neurological foundation for why deliberate practice works. By focusing intensely on a specific skill, you’re forcing the specific relevant circuit to fire, again and again, in isolation. This repetitive use of a specific circuit triggers cells called oligodendrocytes to begin wrapping layers of myelin around the neurons in the circuits—effectively cementing the skill. The reason, therefore, why it’s important to focus intensely on the task at hand while avoiding distraction is because this is the only way to isolate the relevant neural circuit enough to trigger useful myelination. By contrast, if you’re trying to learn a complex new skill (say, SQL database management) in a state of low concentration (perhaps you also have your Facebook feed open), you’re firing too many circuits simultaneously and haphazardly to isolate the group of neurons you actually want to strengthen.

In the century that has passed since Antonin-Dalmace Sertillanges first wrote about using the mind like a lens to focus rays of attention, we have advanced from this elevated metaphor to a decidedly less poetic explanation expressed in terms of oligodendrocyte cells. But this sequence of thinking about thinking points to an inescapable conclusion: To learn hard things quickly, you must focus intensely without distraction. To learn, in other words, is an act of deep work. If you’re comfortable going deep, you’ll be comfortable mastering the increasingly complex systems and skills needed to thrive in our economy. If you instead remain one of the many for whom depth is uncomfortable and distraction ubiquitous, you shouldn’t expect these systems and skills to come easily to you.

Deep Work Helps You Produce at an Elite Level

Adam Grant produces at an elite level. When I met Grant in 2013, he was the youngest professor to be awarded tenure at the Wharton School of Business at Penn. A year later, when I started writing this chapter (and was just beginning to think about my own tenure process), the claim was updated: He’s now the youngest full professor* at Wharton.

The reason Grant advanced so quickly in his corner of academia is simple: He produces. In 2012, Grant published seven articles—all of them in major journals. This is an absurdly high rate for his field (in which professors tend to work alone or in small professional collaborations and do not have large teams of students and postdocs to support their research). In 2013, this count fell to five. This is still absurdly high, but below his recent standards. He can be excused for this dip, however, because this same year he published a book titled Give and Take, which popularized some of his research on relationships in business. To say that this book was successful is an understatement. It ended up featured on the cover of the New York Times Magazine and went on to become a massive bestseller. When Grant was awarded full professorship in 2014, he had already written more than sixty peer-reviewed publications in addition to his bestselling book.

Soon after meeting Grant, my own academic career on my mind, I couldn’t help but ask him about his productivity. Fortunately for me, he was happy to share his thoughts on the subject. It turns out that Grant thinks a lot about the mechanics of producing at an elite level. He sent me, for example, a collection of PowerPoint slides from a workshop he attended with several other professors in his field. The event was focused on data-driven observations about how to produce academic work at an optimum rate. These slides included detailed pie charts of time allocation per season, a flowchart capturing relationship development with co-authors, and a suggested reading list with more than twenty titles. These business professors do not live the cliché of the absentminded academic lost in books and occasionally stumbling on a big idea. They see productivity as a scientific problem to systematically solve—a goal Adam Grant seems to have achieved.

Though Grant’s productivity depends on many factors, there’s one idea in particular that seems central to his method: the batching of hard but important intellectual work into long, uninterrupted stretches. Grant performs this batching at multiple levels. Within the year, he stacks his teaching into the fall semester, during which he can turn all of his attention to teaching well and being available to his students. (This method seems to work, as Grant is currently the highest-rated teacher at Wharton and the winner of multiple teaching awards.) By batching his teaching in the fall, Grant can then turn his attention fully to research in the spring and summer, and tackle this work with less distraction.

Grant also batches his attention on a smaller time scale. Within a semester dedicated to research, he alternates between periods where his door is open to students and colleagues, and periods where he isolates himself to focus completely and without distraction on a single research task. (He typically divides the writing of a scholarly paper into three discrete tasks: analyzing the data, writing a full draft, and editing the draft into something publishable.) During these periods, which can last up to three or four days, he’ll often put an out-of-office auto-responder on his e-mail so correspondents will know not to expect a response. “It sometimes confuses my colleagues,” he told me. “They say, ‘You’re not out of office, I see you in your office right now!’” But to Grant, it’s important to enforce strict isolation until he completes the task at hand.

My guess is that Adam Grant doesn’t work substantially more hours than the average professor at an elite research institution (generally speaking, this is a group prone to workaholism), but he still manages to produce more than just about anyone else in his field. I argue that his approach to batching helps explain this paradox. In particular, by consolidating his work into intense and uninterrupted pulses, he’s leveraging the following law of productivity: High-Quality Work Produced = (Time Spent) x (Intensity of Focus)

If you believe this formula, then Grant’s habits make sense: By maximizing his intensity when he works, he maximizes the results he produces per unit of time spent working.

This is not the first time I’ve encountered this formulaic conception of productivity. It first came to my attention when I was researching my second book, How to Become a Straight-A Student, many years earlier. During that research process, I interviewed around fifty ultra-high-scoring college undergraduates from some of the country’s most competitive schools. Something I noticed in these interviews is that the very best students often studied less than the group of students right below them on the GPA rankings. One of the explanations for this phenomenon turned out to be the formula detailed earlier: The best students understood the role intensity plays in productivity and therefore went out of their way to maximize their concentration—radically reducing the time required to prepare for tests or write papers, without diminishing the quality of their results.

The example of Adam Grant implies that this intensity formula applies beyond just undergraduate GPA and is also relevant to other cognitively demanding tasks. But why would this be? An interesting explanation comes from Sophie Leroy, a business professor at the University of Minnesota. In a 2009 paper, titled, intriguingly, “Why Is It So Hard to Do My Work?,” Leroy introduced an effect she called attention residue. In the introduction to this paper, she noted that other researchers have studied the effect of multitasking—trying to accomplish multiple tasks simultaneously—on performance, but that in the modern knowledge work office, once you got to a high enough level, it was more common to find people working on multiple projects sequentially: “Going from one meeting to the next, starting to work on one project and soon after having to transition to another is just part of life in organizations,” Leroy explains.

The problem this research identifies with this work strategy is that when you switch from some Task A to another Task B, your attention doesn’t immediately follow—a residue of your attention remains stuck thinking about the original task. This residue gets especially thick if your work on Task A was unbounded and of low intensity before you switched, but even if you finish Task A before moving on, your attention remains divided for a while.

Leroy studied the effect of this attention residue on performance by forcing task switches in the laboratory. In one such experiment, for example, she started her subjects working on a set of word puzzles. In one of the trials, she would interrupt them and tell them that they needed to move on to a new and challenging task, in this case, reading résumés and making hypothetical hiring decisions. In other trials, she let the subjects finish the puzzles before giving them the next task. In between puzzling and hiring, she would deploy a quick lexical decision game to quantify the amount of residue left from the first task.* The results from this and her similar experiments were clear: “People experiencing attention residue after switching tasks are likely to demonstrate poor performance on that next task,” and the more intense the residue, the worse the performance.

The concept of attention residue helps explain why the intensity formula is true and therefore helps explain Grant’s productivity. By working on a single hard task for a long time without switching, Grant minimizes the negative impact of attention residue from his other obligations, allowing him to maximize performance on this one task. When Grant is working for days in isolation on a paper, in other words, he’s doing so at a higher level of effectiveness than the standard professor following a more distracted strategy in which the work is repeatedly interrupted by residue-slathering interruptions.

Even if you’re unable to fully replicate Grant’s extreme isolation (we’ll tackle different strategies for scheduling depth in Part 2), the attention residue concept is still telling because it implies that the common habit of working in a state of semi-distraction is potentially devastating to your performance. It might seem harmless to take a quick glance at your inbox every ten minutes or so. Indeed, many justify this behavior as better than the old practice of leaving an inbox open on the screen at all times (a straw-man habit that few follow anymore). But Leroy teaches us that this is not in fact much of an improvement. That quick check introduces a new target for your attention. Even worse, by seeing messages that you cannot deal with at the moment (which is almost always the case), you’ll be forced to turn back to the primary task with a secondary task left unfinished. The attention residue left by such unresolved switches dampens your performance.

When we step back from these individual observations, we see a clear argument form: To produce at your peak level you need to work for extended periods with full concentration on a single task free from distraction. Put another way, the type of work that optimizes your performance is deep work. If you’re not comfortable going deep for extended periods of time, it’ll be difficult to get your performance to the peak levels of quality and quantity increasingly necessary to thrive professionally. Unless your talent and skills absolutely dwarf those of your competition, the deep workers among them will outproduce you.

What About Jack Dorsey?

I’ve now made my argument for why deep work supports abilities that are becoming increasingly important in our economy. Before we accept this conclusion, however, we must face a type of question that often arises when I discuss this topic: What about Jack Dorsey?

Jack Dorsey helped found Twitter. After stepping down as CEO, he then launched the payment-processing company Square. To quote a Forbes profile: “He is a disrupter on a massive scale and a repeat offender.” He is also someone who does not spend a lot of time in a state of deep work. Dorsey doesn’t have the luxury of long periods of uninterrupted thinking because, at the time when the Forbes profile was written, he maintained management duties at both Twitter (where he remained chairman) and Square, leading to a tightly calibrated schedule that ensures that the companies have a predictable “weekly cadence” (and that also ensures that Dorsey’s time and attention are severely fractured).

Dorsey reports, for example, that he ends the average day with thirty to forty sets of meeting notes that he reviews and filters at night. In the small spaces between all these meetings, he believes in serendipitous availability. “I do a lot of my work at stand-up tables, which anyone can come up to,” Dorsey said. “I get to hear all these conversations around the company.” This style of work is not deep. To use a term from our previous section, Dorsey’s attention residue is likely slathered on thick as he darts from one meeting to another, letting people interrupt him freely in the brief interludes in between. And yet, we cannot say that Dorsey’s work is shallow, because shallow work, as defined in the introduction, is low value and easily replicable, while what Jack Dorsey does is incredibly valuable and highly rewarded in our economy (as of this writing he was among the top one thousand richest people in the world, with a net worth over $1.1 billion).

Jack Dorsey is important to our discussion because he’s an exemplar of a group we cannot ignore: individuals who thrive without depth. When I titled the motivating question of this section “What About Jack Dorsey?,” I was providing a specific example of a more general query: If deep work is so important, why are there distracted people who do well? To conclude this chapter, I want to address this question so it doesn’t nag at your attention as we dive deeper into the topic of depth in the pages ahead.

To start, we must first note that Jack Dorsey is a high-level executive of a large company (two companies, in fact). Individuals with such positions play a major role in the category of those who thrive without depth, because the lifestyle of such executives is famously and unavoidably distracted. Here’s Kerry Trainor, CEO of Vimeo, trying to answer the question of how long he can go without e-mail: “I can go a good solid Saturday without, without… well, most of the daytime without it… I mean, I’ll check it, but I won’t necessarily respond.” At the same time, of course, these executives are better compensated and more important in the American economy today than in any other time in history. Jack Dorsey’s success without depth is common at this elite level of management. Once we’ve stipulated this reality, we must then step back to remind ourselves that it doesn’t undermine the general value of depth. Why? Because the necessity of distraction in these executives’ work lives is highly specific to their particular jobs. A good chief executive is essentially a hard-to-automate decision engine, not unlike IBM’s Jeopardy!-playing Watson system. They have built up a hard-won repository of experience and have honed and proved an instinct for their market. They’re then presented inputs throughout the day—in the form of e-mails, meetings, site visits, and the like—that they must process and act on. To ask a CEO to spend four hours thinking deeply about a single problem is a waste of what makes him or her valuable. It’s better to hire three smart subordinates to think deeply about the problem and then bring their solutions to the executive for a final decision.

This specificity is important because it tells us that if you’re a high-level executive at a major company, you probably don’t need the advice in the pages that follow. On the other hand, it also tells us that you cannot extrapolate the approach of these executives to other jobs. The fact that Dorsey encourages interruption or Kerry Trainor checks his e-mail constantly doesn’t mean that you’ll share their success if you follow suit: Their behaviors are characteristic of their specific roles as corporate officers.

This rule of specificity should be applied to similar counterexamples that come to mind while reading the rest of this book. There are, we must continually remember, certain corners of our economy where depth is not valued. In addition to executives, we can also include, for example, certain types of salesmen and lobbyists, for whom constant connection is their most valued currency. There are even those who manage to grind out distracted success in fields where depth would help.

But at the same time, don’t be too hasty to label your job as necessarily non-deep. Just because your current habits make deep work difficult doesn’t mean that this lack of depth is fundamental to doing your job well. In the next chapter, for example, I tell the story of a group of high-powered management consultants who were convinced that constant e-mail connectivity was necessary for them to service their clients. When a Harvard professor forced them to disconnect more regularly (as part of a research study), they found, to their surprise, that this connectivity didn’t matter nearly as much as they had assumed. The clients didn’t really need to reach them at all times and their performance as consultants improved once their attention became less fractured.

Similarly, several managers I know tried to convince me that they’re most valuable when they’re able to respond quickly to their teams’ problems, preventing project logjams. They see their role as enabling others’ productivity, not necessarily protecting their own. Follow-up discussions, however, soon uncovered that this goal didn’t really require attention-fracturing connectivity. Indeed, many software companies now deploy the Scrum project management methodology, which replaces a lot of this ad hoc messaging with regular, highly structured, and ruthlessly efficient status meetings (often held standing up to minimize the urge to bloviate). This approach frees up more managerial time for thinking deeply about the problems their teams are tackling, often improving the overall value of what they produce.

Put another way: Deep work is not the only skill valuable in our economy, and it’s possible to do well without fostering this ability, but the niches where this is advisable are increasingly rare. Unless you have strong evidence that distraction is important for your specific profession, you’re best served, for the reasons argued earlier in this chapter, by giving serious consideration to depth.