Deep Work: Rules for Focused Success in a Distracted World

by Cal Newport

Deep work is necessary to wring every last drop of value out of your current intellectual capacity.

Indeed, if you study the lives of other influential figures from both distant and recent history, you’ll find that a commitment to deep work is a common theme.

Twain’s study was so isolated from the main house that his family took to blowing a horn to attract his attention for meals.

Allen is joined in his rejection of computers by Peter Higgs, a theoretical physicist who performs his work in such disconnected isolation that journalists couldn’t find him after it was announced he had won the Nobel Prize.

Microsoft CEO Bill Gates famously conducted “Think Weeks” twice a year, during which he would isolate himself (often in a lakeside cottage) to do nothing but read and think big thoughts.

In aggregate, the rise of these tools, combined with ubiquitous access to them through smartphones and networked office computers, has fragmented most knowledge workers’ attention into slivers.

A 2012 McKinsey study found that the average knowledge worker now spends more than 60 percent of the workweek engaged in electronic communication and Internet searching, with close to 30 percent of a worker’s time dedicated to reading and answering e-mail alone.

“What the Net seems to be doing is chipping away my capacity for concentration and contemplation,” admitted journalist Nicholas Carr, in an oft-cited 2008 Atlantic article.

Most new developers dedicate a four-year college education to learning the ropes before their first job—and even then, competition for the best spots is fierce.

Learning something complex like computer programming requires intense uninterrupted concentration on cognitively demanding concepts—the type of concentration that drove Carl Jung to the woods surrounding Lake Zurich.

After two months locked away studying, Benn attended the notoriously difficult Dev Bootcamp: a hundred-hour-a-week crash course in Web application programming.

Given both his preparation and his newly honed ability for deep work, Benn excelled. “Some people show up not prepared,” he said.

Only half the students who started the program with Benn ended up graduating on time. Benn not only graduated, but was also the top student in his class.

We have an information economy that’s dependent on complex systems that change rapidly.

To remain valuable in our economy, therefore, you must master the art of quickly learning complicated things.

The real rewards are reserved not for those who are comfortable using Facebook (a shallow task, easily replicated), but instead for those who are comfortable building the innovative distributed systems that run the service (a decidedly deep task, hard to replicate).

I build my days around a core of carefully chosen deep work, with the shallow activities I absolutely cannot avoid batched into smaller bursts at the peripheries of my schedule. Three to four hours a day, five days a week, of uninterrupted and carefully directed concentration, it turns out, can produce a lot of valuable output.

More generally, the lack of distraction in my life tones down that background hum of nervous mental energy that seems to increasingly pervade people’s daily lives.

“Our technologies are racing ahead but many of our skills and organizations are lagging behind.”

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.

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.”

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.

“Hearing a succession of mediocre singers does not add up to a single outstanding performance.”

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.

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.

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?

It’s no wonder that a venture capitalist I interviewed for my last book admitted to me with some concern, “Everyone wants my job.”

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.

Two Core Abilities for Thriving in the New Economy The ability to quickly master hard things. The ability to produce at an elite level, in terms of both quality and speed.

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.

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.

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.

If you don’t produce, you won’t thrive—no matter how skilled or talented you are.

“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.”

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.”

As Ericsson emphasizes, “Diffused attention is almost antithetical to the focused attention required by deliberate practice” (emphasis mine).

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.

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.

But this sequence of thinking about thinking points to an inescapable conclusion: To learn hard things quickly, you must focus intensely without distraction.

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.

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.

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)

By maximizing his intensity when he works, he maximizes the results he produces per unit of time spent working.

In a 2009 paper, titled, intriguingly, “Why Is It So Hard to Do My Work?,” Leroy introduced an effect she called attention residue.

“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.

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.

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.

The attention residue left by such unresolved switches dampens your performance.