A World Without Email: Reimagining Work in an Age of Communication Overload

by Cal Newport

The Hyperactive Hive Mind A workflow centered around ongoing conversation fueled by unstructured and unscheduled messages delivered through digital communication tools like email and instant messenger services.

As I’ll detail, pioneering research in psychology and neuroscience reveals that these context switches, even if brief, induce a heavy cost in terms of mental energy—reducing cognitive performance and creating a sense of exhaustion and reduced efficacy. In the moment, the ability to quickly delegate tasks or solicit feedback might seem like an act of streamlining, but as I’ll show, in the long run, it’s likely reducing productivity, requiring more time and more expenses to get the same total amount of work accomplished.

I introduce a framework I call attention capital theory that argues for creating workflows built around processes specifically designed to help us get the most out of our human brains while minimizing unnecessary miseries.

I’ll show, driven by the ideas of the immensely influential business thinker Peter Drucker, we tend to think of knowledge workers as autonomous black boxes—ignoring the details of how they get their work done and focusing instead on providing them with clear objectives and motivational leadership. This is a mistake. There is massive potential productivity currently latent in the knowledge sector. To unlock it will require much more systematic thinking about how best to organize the fundamental objective of getting a collection of human brains hooked together in networks to produce the most possible value in the most sustainable way. Hint: the right answer is unlikely to involve checking email once every six minutes.

One such signal delivered through this unconscious network is called, aptly enough, influence. It describes the degree to which one person can cause another to match their speaking pattern.

This brings us back to email. The case study of Adrian Stone and IBM is pure technological determinism: a tool introduced for a simple purpose (to make existing communication practices more efficient) had an unexpected result (a shift toward the hyperactive hive mind style of collaboration). The speed of this transformation, which required less than a week to get rolling, underscores how powerful these forces can be once unleashed.

As argued earlier, email helped solve a practical problem generated by the growing size of offices: the need for efficient asynchronous communication—that is, a fast way to send messages back and forth without requiring the sender and receiver to be communicating at the same time. Instead of having to play phone tag with a colleague from the other side of your office building, you can replace this real-time conversation with a short message, delivered when convenient for you, and then read when convenient for the recipient.

Lasse Rheingans and I aren’t the only ones to notice the stakes on the table in this discussion. In the same 1999 article cited earlier, Peter Drucker notes that in terms of productivity thinking, knowledge work was where industrial manufacturing was in 1900—that is, right before the radical experiments that increased productivity by fifty times. We’re poised, in other words, to make similarly massive increases in the economic effectiveness of the knowledge sector, if we’re willing to get serious about questioning how we work. Drucker calls this push to make knowledge work more productive the “central challenge” of our times, writing: “It is on [knowledge work] productivity, above all, that the future prosperity—and indeed the future survival—of the developed economies will increasingly depend.”

The Attention Capital Principle The productivity of the knowledge sector can be significantly increased if we identify workflows that better optimize the human brain’s ability to sustainably add value to information.

Devesh’s company employs a group of largely remote employees spread out across the United States and Europe. This geographic diversity, which covers a wide range of time zones, required a dependence on asynchronous communication tools like email.

What Drucker realized was that knowledge work was too skilled and creative to be broken down into a series of repetitive tasks that could be prescribed to workers by managers, as was the case with manual labor.

Knowledge work is better understood as the combination of two components: work execution and workflow.

In knowledge work, this equation becomes murkier. When trying to engineer your workflows to generate a better return from your attention capital, what are you looking for? What is the cognitive work equivalent of production speed?

Drawing on these observations, I suggest the following design principle for developing approaches to work that provide better returns from your personal or organizational attention capital: seek workflows that (1) minimize mid-task context switches and (2) minimize the sense of communication overload. These two properties are the knowledge work equivalent of Henry Ford’s obsession with speed.

Regardless of the source of these interruptions, when it comes to producing value with your brain, the more you’re able to complete one thing at a time, sticking with a task until done before moving on to the next, the more efficiently and effectively you’ll work.

The optimal way to deploy our human brains is sequentially.

Imagine you want to make a major change to your own or your organization’s workflow. How can you avoid the inconveniences associated with this experimentation? You can’t. You must instead adjust your mindset so that you no longer fear these annoyances.

We still talk about “innovation,” but this term now applies almost exclusively to the products and services we offer, not the means by which we produce them. When it comes to the latter topic, business thinkers tend to focus on secondary factors, like better leadership or clearer objectives to help stimulate productivity. Little attention is dedicated to the actual mechanics of how work is assigned, executed, and reviewed.

In Ford’s world, the workers were dispensable, while in the knowledge world, our brains are the source of all value. If anything, the hyperactive hive mind already has us trapped in a digital Modern Times, futilely trying to keep up with email messages that arrive faster and faster. The attention capital principle can help us move past this misery.

This is what goes wrong if you defy Carpenter’s model and instead attempt to deploy a brand-new workflow on your team by fiat. Regardless of the workflow’s inherent benefits, you might be accidentally shifting your team’s sense of control from the internal to the external, sapping motivation and making it unlikely that they’ll stick with the changes. On the other hand, if your team members are involved in the construction of the new workflow and, equally important, feel like they’re able to improve it as deficiencies arise, then the control remains internal and the workflow is much more likely to be embraced.

There are three steps necessary to keep these experiments collaborative. The first is education. It’s important that your team understand the difference between workflows and work execution, and why the hyperactive hive mind is just one workflow among many—and probably not a very good one.

As explained, this applies when you transform your personal workflow, moving your daily rhythms away from the unpredictable back-and-forth of the hyperactive hive mind. These shifts are likely to create changes that are visible to your colleagues and clients, with the most notable being that you’re no longer always answering emails or instant messages promptly. Other people will, in other words, need to shift their expectations about working with you.

The Process Principle Introducing smart production processes to knowledge work can dramatically increase performance and make the work much less draining.

To move past the shortcomings of the hyperactive hive mind workflow, we must abandon our Rousseauian optimism that knowledge workers left to a state of nature will thrive. To get the most out of our attention capital, we need processes, and this is true for both organizations and individual knowledge workers.

A good production process, in other words, should minimize both ambiguity about what’s going on and the amount of unscheduled communication required to accomplish this work. Notice, nothing about these properties restricts the knowledge worker’s autonomy in figuring out how they get their work done; the focus remains on coordinating this work.

This is the third time we’ve encountered a similar pattern: information about knowledge work arranged into columns of cards on a board. Alex’s team uses both physical chalkboards and virtual boards implemented by Asana. Optimize Enterprises relies on Flow. Devesh, from the last chapter, uses Trello.

To understand agile, you must understand what it replaces. Software development used to rely on lumbering, complicated project plans that would quixotically attempt to figure out in advance all the work required to produce a major piece of software. The idea was that, given one of these plans, often lovingly rendered in striated, multicolored Gantt charts, you could know exactly how many programmers to assign at each stage and provide your customers with accurate release schedules.

Individual Task Board Practice #1: Use More Than One Board

Individual Task Board Practice #2: Schedule Regular Solo Review Meetings

Individual Task Board Practice #3: Add a “To Discuss” Column

Individual Task Board Practice #4: Add a “Waiting to Hear Back” Column

This style of automatic production process plays an important role in many knowledge work settings. Not all processes, however, can be made automatic. For this strategy to apply, the process in question must produce some output in a highly repeatable fashion, where the same steps are implemented, in the same order, by the same people, each time. The types of processes optimized with task boards, by contrast, are more diverse and dynamic, requiring collaborative decision making to figure out what tasks to tackle next and who should be responsible for them.

Underlying this framework is a simple but profound idea: by adding complexity to the rules we use to structure our communication, the actual amount of information required by the interactions can be reduced.

This coordination requires communication, and whether or not you use this terminology, this in turn requires the people involved to agree in advance on a set of rules about how and when the communication occurs—what we call a coordination protocol.

The Protocol Principle Designing rules that optimize when and how coordination occurs in the workplace is a pain in the short term but can result in significantly more productive operation in the long term.

There’s a reason why automated meeting-scheduling companies like x.ai are receiving so much attention from investors: even the most die-hard hyperactive hive mind booster can’t ignore the raw time-wasting inefficiency of the way most knowledge workers currently tackle this increasingly common task. The standard protocol for setting up meetings is what I call energy-minimizing email ping-pong.

I would go so far as to say that anyone whose job requires more than one or two scheduled events in a typical week absolutely should be using a scheduling service or, if the work demands it, a part-time assistant. There’s really no reason why anyone should still have to waste cognitive cycles in dragged-out scheduling conversations. You might think that the gains here are small—how hard is it to send some emails?—but if you’re like me, you’ll likely be surprised by the feeling of a burden being lifted when you eliminate all these ongoing scheduling conversations, which have a way of nibbling at the borders of your concentration, driving you again and again back into the hive mind chatter.

When you’re faced with an overwhelming incoming stream of unrelated tasks, you don’t have enough margin in your schedule to create smarter alternative workflows—there’s just too much bombarding you to individually tame everything with optimized processes.

This reality creates a nasty, productivity-sapping circularity. When you’re overloaded, you’re forced to fall back on the flexibility of the hive mind. This workflow, however, leads to even more fragmentation of your attention, making you even less efficient in getting things done. The result: overload increases!

If we want to tame the hyperactive hive mind, therefore, we must first tame the trend toward non-specialization.

The Specialization Principle In the knowledge sector, working on fewer things, but doing each thing with more quality and accountability, can be the foundation for significantly more productivity.

They lead busy lives, and the thought of reducing that busyness seems like a step backward. “I know how addictive busyness [is],” she writes, but this “whirlwind” isn’t compatible with producing accomplishments that provide lasting meaning and pride.

Work Reduction Strategy #1: Outsource What You Don’t Do Well

Work Reduction Strategy #2: Trade Accountability for Autonomy

You’re either producing good code, or you’re obviously not. Some are simply not comfortable with this blunt assessment of what they’re actually accomplishing.

One of the key ideas from our extreme programming case study was the importance of working on one objective at a time, without interruption, until it’s complete. This commitment to working in sprints is now widely held throughout the software development world, even in teams that don’t adopt the full set of strict XP rules.

During a Scrum sprint, a team works exclusively on a single specific deliverable, such as adding a new feature to a software product—no complex task lists, schedules filled with meetings, or intricate daily planning processes are needed.10 This productivity hack has become an accepted best practice in this field.

Design sprints are meant to help you figure out where your team or organization should focus its efforts. In a traditional workplace, these decisions typically unfold over months of meetings and debates, augmented with numerous email threads, ultimately leading to costly investments in new products or strategies that all too often fall short. A design sprint attempts to compress this work, from the initial debates all the way to receiving market feedback on the resulting decisions, into one highly efficient workweek. On the first day, you figure out the problem you’re trying to solve. On the second day, you sketch out competing solutions. On the third day, you make the tough decision about which solution you want to explore, transforming it into a hypothesis that can be tested. On the fourth day, you throw together a rough prototype that allows you to test the hypothesis, and on the fifth and final day, you put real clients in front of the prototype and learn from their feedback.

As Janz’s analysis points out, a major source of service overload in academia is the asymmetry inherent in asking for someone’s help. If you run an administrative unit within a university, or are tasked with forming a committee, then from your perspective, asking me or Bruce Janz to attend some meetings or participate in a survey or review some files seems completely reasonable. You’re not demanding a huge time commitment, and our minor assistance is crucial for you to succeed with your major objective. For us to say no would seem uncivil, if not downright antisocial. The problem, of course, is that these requests accumulate. If two dozen other units and committees all make these same reasonable requests, suddenly we’re desperately overwhelmed by work that has little to do with our main objectives of research and teaching—a recipe not just for inefficiency, but outright frustration.

In knowledge work more generally, approximating something like my hypothetical service budget could be a powerful strategy for pushing back against overload. There are three keys for a strategy of this type to work. First, it must start from the premise that your time and attention are limited. Second, it must quantify how much of your time and attention is currently dedicated to whatever category of work you’re attempting to budget. And third, whoever is responsible for determining how much work of this type you have to do must confront your current commitments when asking you to do more, even if this person is you.