English communication

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.