Cal Newport's Blog, page 28

My New Book: Digital Minimalism

A Manual for a Focused Life

I’m excited to officially announce my new book, Digital Minimalism: Choosing a Focused Life in a Noisy World, which will be published on February, 5th.

My last book, Deep Work, tackled the impact of new technologies on the world of work. After it came out, many readers began asking me about the equally important impact of these tools on their personal lives. This new book is my response.

In it, I argue that we have been too casual in adopting alluring new technologies, and as a result our quality of life is diminishing. To solve this problem I propose a philosophy of technology use called digital minimalism in which you radically reduce the time you spend starting at screens, focusing on a small number of digital activities that strongly support things you deeply value, and then happily ignoring the rest.

In addition to arguing why minimalism is a necessary answer to our increasing digital discontentment, I take the reader inside the vibrant subculture of digital minimalists who have already found great satisfaction and authentic meaning in taking back control of their technological lives — highlighting the key principles they use to succeed in adopting this philosophy.

(Among other things, you’ll learn the detailed story of the digital declutter experiment I ran last January as part of my book research, which ended up growing to over 1,600 participants and receiving coverage in the New York Times.)

I will, of course, be writing quite a bit more about these ideas and this book in the weeks ahead. My purpose for now is mainly to bring you up to speed on what I’m up to.

As a final logistical note: if you have already preordered this book, or are planning to preorder it, hold on to your digital receipt, as I’ll soon be offering a large preorder package — including advance content from the book, a detailed look inside my personal productivity systems, and access to private Q&As — as my way of saying thank you. (Preorders are incredibly important for a book launch, so I’m incredibly grateful for anyone who takes the time to support me in this way.)

Stay tuned!

Is Facebook the AOL of the 2010s? A Skeptical Examination of Social Media Network Effects.

The Law

In economics, a network effect is a positive benefit created by a new user buying a product or joining a service. In the context of computer networks, these benefits are commonly believed to scale quickly with the number of users.

In technology circles, perhaps the best known instantiation of network effects is Metcalfe’s Law, named for Ethernet co-inventor Bob Metcalfe, who was likely inspired by similar theories developed at Bell Telephone in the early 20th century.

This law concerned the value of the Ethernet network cards sold by Metcalfe’s company 3Com. It states that given a network with N users, buying one additional Ethernet card provides you with N new possible network connections (e.g., from the new card to each of the N existing users).

It then follows, roughly speaking, that the value of N network cards grows as N^2 instead of N. Once a network achieves a certain critical size, therefore, the value it returns will quickly begin to far exceed the cost of joining it, creating a powerful positive feedback loop.

Metcalfe’s Law is incredibly influential in Silicon Valley, where it’s often applied to justify the monopoly status of the social media conglomerates. If a network like Facebook has over a 1,000,000,000 users, the law tells us, then its value to users grows as (1,000,000,000)^2 — a quantity so vast that any attempt to compete with this giant must be futile.

It’s widely believed among many Silicon Valley types that this calculus helps explains the lack of venture capital investment in new social media start-ups in recent years. The power of network effects in this sector is unimpeachable.

But should they be?

AOL Redux

I’ve long harbored suspicions about how network effects are referenced to justify massive social media conglomerates.

If you examine the canonical examples of these effects, such as 3Com’s Ethernet cards or Bell’s telephones, you’ll notice that joining the network in question is the only way to connect to other people in that general manner.

In 1908, if you didn’t own a Bell telephone, you couldn’t talk in real time to people over distance. In 1988, if your computer didn’t have an Ethernet card, it couldn’t connect to other devices in your office. In these scenarios, buying the relevant product shifted you from completely disconnected to massively connected.

Social media, however, is different.

In 2018, joining a network like Facebook enables you to connect with or monitor the status of people you know using digital networks. Unlike telephones or Ethernet cards, however, you don’t need a private network like Facebook for these benefits. Both the Internet and SMS, among other technologies, already provide many different tools, protocols, and services for connecting and disseminating information digitally.

Case in point: I’ve never had a social media account, and yet I constantly enjoy connecting to people, and posting and monitoring information using digital networks.

So what then exactly do massive social media platforms like Facebook provide? A more honest answer is that they offer a more convenient experience than the wilder, less centralized social internet, but not something fundamentally unique.

There’s value in convenience, but not Metcalfe’s Law level, dominating value. In some sense, Facebook is to the social internet today what AOL was to the world wide web in the 1990s — a walled garden that provides a gentle on-ramp to the capabilities of a more exuberant decentralized network roiling beyond its boundaries.

These are thoughts I’m in the early stages of developing, so I’m interested in any pointers you can share about people smarter than me exploring similar ideas. But it increasingly seems to me that social media giants like Facebook offer at best network enhancements to its users, not the mythical network effects that helped make the monopolies of past eras so inescapable.

On Bryce Harper and the Impact of Social Media on Athletes

#teamnoscroll

As a big time Washington Nationals fan, I’ve been watching Bryce Harper play here in D.C. since he was first brought up to the majors at the age of 19. As you might therefore imagine, I’ve been closely following his free agency this fall.

It was due to this hardball diligence that I recently noticed a small sports page news item that intersects with the types of topics we like to discuss here. A couple weeks ago, Harper declared he was going on a social media fast. He even ironically (oxymoronically?) introduced a hash tag for his effort: #teamnoscroll.

I applaud Harper for his public step back from social media, especially during a period of intense scrutiny where checking the latest buzz would only increase his anxiety.

But reading about #teamnoscroll prompted an interesting thought: Why aren’t more superstar athletes permanently disengaged from social media?

At the elite level, athletes differentiate themselves by maximizing every physical and cognitive advantage (for more on this, see Ben Bergeron’s Chasing Excellence). Superstars become superstars, in large part, because they pair once in a generation gifts with relentless training.

There’s a reason Lebron James spends $1.5 million dollars a year on his off-season training: the return on investment is worth it.

But then there’s social media. These services create cognitive drag by subjecting you to a compulsive mix of drama and distraction. If you’re famous, this drag is even more pronounced.

For the average user, this reality might prove a nuisance, but for athletes performing at the top levels of their sports, the result could be the difference between a solid career and the hall of fame; a 5-year $25 million dollar deal, and a 10-year $350 million deal.

And yet, many of the same athletes that measure their food on a scale are somehow fine scrolling on a whim.

In my role as an author who writes about focus, I’ve had the opportunity to discuss deep work with front office professional sports types. The general sense I’m getting is that pro teams are becoming increasingly wary about the impact of technology on cognitive fitness.

Some have told me that it’s the sports agents who are exacerbating this problem by encouraging their clients to “build their brand” through social media. I have a hard time believing this is true because it’s self-defeating. I can’t imagine that star agents like Scott Boras, with his binders full of advanced analytics breaking down every contribution of his clients, would be blind to the extra edge provided by an unusually focused mind.

I wouldn’t be surprised, in other words, if we start to see more of a systematic move away from social media by top figures in sports as the full impact of this technology is better understood.

To make this more concrete: few things would make me happier than to see much less of Bryce Harper on Instagram, and many more years of his ferocious swing here at Nats Park.

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Speaking of social connection and work, my friend Dan Schawbel has a new book out this week called Back to Human. It provides a compelling and evidence-based argument for the importance of old-fashioned, non-technological interaction in leading successful teams. I’ve always liked Schawbel’s work, but this book hits particularly close to the types of issues I write about here —  in particular, the unintended consequences of tools like email, and what we can do to mitigate these issues. Check it out…

(Photo by Keith Allison.)

On Physician Burnout and the Plight of the Modern Knowledge Worker

On Screens and Surgeons

Atul Gawande has a fascinating article in the most recent issue of the New Yorker about the negative consequences of the electronic medical records revolution. There are many points in this piece that are relevant to the topics we discuss here, but there was one observation in particular that I found particularly alarming.

Gawande introduces the Berkeley psychologist Christina Maslach, who is one of the leading experts on occupational burnout: her Maslach Burnout Inventory has been used for almost four decades to track worker well-being.

One of the striking findings from Maslach’s research is that the burnout rate among physicians has been rapidly rising over the last decade. Interestingly, this rate differs between different specialities — sometimes in unexpected ways.

Neurosurgeons, for example, report lower levels of burnout than emergency physicians, even though the surgeons work longer hours and experience poorer work-life balance than ER doctors.

As Gawande reports, this puzzle was partly solved when a research team from the Mayo Clinic looked closer at the causes of physician burnout. Their discovery: one of the strongest predictors of burnout was how much time the doctor spent starting at a computer screen.

Surgeons spend most of their clinical time performing surgeries. Emergency physicians, by contrast, spend an increasing amount of this time wrangling information into electronic medical systems. Gawande cites a 2016 study that finds the average physician now spends two hours at a computer screen for every hour they spend working with patients.

Incomplete Solutions

Electronic medical records present a complicated case. As Gawande emphasizes, this technology undoubtedly represents the future of medical care — it solves many problems, and going back to ad hoc, handwritten systems is no more viable than the acolytes of Ned Ludd demanding the return of hand-driven looms.

The solutions Gawande outline include two major themes. The first is making these systems smaller, more agile, and more responsive to the way specific physicians actually practice, instead of trying to introduce massive, monolithic software that generically applies to many different specialities.

The second theme is introducing more administrative help to mediate between the doctor’s clinical work and interactions with the electronic systems (c.f., my recent article on intellectual specialization).

What caught my attention as I read this article, however, is that many knowledge work fields have experienced a similar shift where individuals now spend increasing amounts of their day interacting with screens instead of performing the high-value activities for which they were trained (just ask any professor, computer programmer or lawyer).

For us, it’s email and instant messenger instead of electronic medical systems, but there’s no reason to believe that the effect wouldn’t be the same: more ancillary screen time produces less well-being and, eventually, more burnout.

In the rarified and focused world of medical care, there are solutions to this screen creep problem. But where are the solutions for the rest of us? This is arguably one of the biggest problems facing our increasingly knowledge-based economy, and yet few currently take it seriously.

You Are Not a Talent Agent (So Why Do You Work Like One?)

The CAA Way

I’m currently reading Michael Ovitz’s engaging new memoir. Even if you don’t know Ovitz, you definitely know his clients’ work. He’s the super-agent who co-founded the domineering CAA talent agency, and during the 1980s and 90’s become one of the most powerful figures in Hollywood.

In his memoir, Ovitz emphasizes the importance of communication in the talent business. For a talent agent, he notes, your time is one of the primary resources you have to offer, so to succeed in this field, you have to constantly talk to clients, potential clients, ex-clients you might want back, and all the assorted figures in the entertainment world orbit who might have information helpful to your clients.

One of the cardinal rules during the early years of CAA was that you always returned every call the same day. Ovitz personally exemplified this rule. He would start making calls as soon as he woke up and continue making calls until right before he went to bed. He would make hundreds of calls every day.

The importance of these touches were so important that he had a small sign that read “communicate” placed on every phone in the I. M. Pei-designed CAA headquarters.

Here’s what struck me as I read about this: in the late 1970s, when Ovitz was helping CAA gain a toehold in the entertainment industry, the need to be constantly communicating was an artificial and unnatural behavior — something that had to be purposefully instilled and enforced in his agents.

Today, by contrast, almost every knowledge worker acts like a CAA agent. We may have replaced telephones with email and instant messenger, but the underlying behavior is the same: a constant whirring of contact from when we first wake up to right before we go to bed.

The problem, of course, is that most knowledge workers are not CAA agents. Indeed, for most knowledge workers, constant communication probably makes them worse at doing the thing they supposedly do best.

Viewed with some objective distance, this is a puzzling development.

I can’t help but wonder when some new Michael Ovitz-style figure will arise, in a sector like computer programming or academia where unbroken concentration unambiguously produces value, and once again help drive his or her organization to immense success by putting small signs on each employee’s desk — except this time, they’ll read: “think, don’t talk.”

The Mona Lisa Doesn’t Tweet

A Social Media Icon

Seth Godin recently noted the following on his always insightful blog:

“The Mona Lisa has a huge social media presence. Her picture is everywhere. But she doesn’t tweet. She’s big on social media because she’s an icon, but she’s not an icon because she’s big on social media.”

This perfectly sums up a point I often find myself tying to make when arguing that people don’t need to engage social media to advance their career.

In my experience, if you push people — especially young people — about why they think social media is crucial for their professional life, you’ll eventually uncover a belief that an important factor holding them back is that people in power simply haven’t noticed their specialness.

Social media platforms, they’ve been taught, provide a method to correct this information asymmetry by making it easy for them to demonstrate their specialness to the world (potentially bypassing some dreaded “gatekeepers” along the way), and therefore reap the attention that they know deep down they already deserve.

As Godin hints, however, reality is both simpler and starker.

If you can produce things that are rare and valuable, good things are likely to follow: opportunities will become more interesting and plentiful, you’ll gain more autonomy over your career, and yes, people might even start talking about you on social media.

On the other hand, if you’re not producing something rare and valuable, no amount of social media “grooming” will convince people to care (with a few rare exceptions).

The natural conclusion to draw from these observations is that you’re almost certainly better off taking the 135 minutes per day the average social media user spends on these services and instead dedicate them to deliberately improving your ability to do valuable things.

(Hat tip to my friends The Minimalists for pointing me toward Godin’s post.)

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Unrelated note: I recently read an advance copy of James Clear’s new book, Atomic Habits. His thesis is that small but carefully selected habits can, over time, create massively positive results — not just in terms of what you accomplish, but also in terms of the type of person you become. James’s exposition rings true with what’ve I learned hanging around interesting people and high achievers. I recommend you give this book a closer look.

The Average User Checks Email 5.6 Hours Per Weekday. This Is Not Good.

A Stark Survey

A couple months ago, Adobe released the results of its fourth annual Consumer Email Survey. Drawing from data gathered earlier in the summer from over 1000 panel participants, the survey provides a snapshot of current consumer email habits.

Among other results, it reveals that self-reported time spent checking work email has decreased slightly to 3.1 hours per weekday on average. By contrast, the average time spent checking personal email has increased by almost 20% to 2.5 hours per weekday.

Combined: the average daily time spent checking email is now 5.6 hours — up almost a half hour since 2017.

These numbers are self-reported and therefore should not be taken too literally, and if you look at the histogram provided by Adobe, it’s clear that the variance is significant. The survey still captures, however, the stark reality that the average professional is now dedicating a substantial fraction of their waking hours to sending and receiving digital messages.

An Inescapable Conclusion

No one doubts the reality that it’s more efficient to hit “send” than to print a memo or mail a letter, but as observations like the above become more extreme, the claim that email is a straightforward productivity booster has become increasingly indefensible — the dynamics at play are more complex and decidedly dire.

We cannot, in other words, escape the necessity to radically rethink how we work in the age of computer networks. To use a metaphor appropriate to the October season: survey results like those reported by Adobe are making it unmistakably clear that Frankenstein’s digital monster has escaped the lab.

On the Law of Diminishing Specialization

On Productive Technology and its Discontents

Recently, I’ve been dipping in and out of Edward Tenner’s provocative 1996 book, When Things Bites Back. In following one of Tenner’s footnotes I came across a fascinating 1992 academic study from the National Review of Productivity, authored by the Georgia Tech economist Peter G. Sassone. 

The paper has an innocuous title, “Survey Finds Low Office Productivity Linked to Staffing Imbalances,” but its findings are profoundly relevant to our recent discussion of attention capital theory, and the value of deep work more generally.

Beginning in 1985, Sassone began a series of twenty office productivity case studies spread over different departments in five major U.S. corporations. His initial goal was to measure the bottomline benefits of the front office computer systems that were new at the time, but as he notes, this soon changed:

“[I]t became apparent that [my] data collection and analysis techniques were yielding important productivity insights beyond the cost justification of office computer systems.”

Deploying a technique called work value analysis, Sassone measured not only the amount of work conducted by his subjects, but also the skill level required for the work. He found that managers and other skilled professionals were spending surprisingly large percentages of their time working on tasks that could be completed by comparably lower-level employees.

He identified several factors that explain this observation, but a major culprit was the rise of “productivity-enhancing” computer systems. This new technology made it possible for managers and professionals to tackle administrative tasks that used to require dedicated support staff.

The positive impact of this change was that companies needed less support staff. The negative impact was that it reduced the ability of managers and professionals to spend concentrated time working on the things they did best.

Among other examples uncovered in his case studies, Sassone highlighted:

A corporate marketing department where senior marketing professional were spending more than a day per week of their time preparing charts and graphs for presentations.
A large commercial bank where corporate bankers were devoting more than a quarter of their time to handling routine interactions with clients.

This reduction in the typical deep-to-shallow work ratio (see Rule #1 in Deep Work) became so pronounced as computer technology invaded the front office that Sassone gave it a downright Newportian name: The Law of Diminishing Specialization.

What makes Sassone’s study particularly fascinating is that he used rigorous data collection and analysis methods to answer the question of whether or not this diminishing specialization was a good trade-off from a financial perspective.

His conclusion: no.

Reducing administrative positions saves some money. But the losses due to the corresponding reduction in high-level employees’ ability to perform deep work — a diminishment of “intellectual specialization” — outweighs these savings.

As Sassone explains:

“The results of a comparison of a ‘typical’ department, with a department with a reasonable high level of intellectual specialization were startling. The typical office could save over 15 percent of its payroll costs by restructuring its staff and increasing the intellectual specialization of its workers.”

To make this more concrete, he calculated:

“[T]he typical office can save about $7,400 [around $13,200 in 2018 dollars] per employee per year by restructuring its office staffs and improving its levels of intellectual specialization.”

In other words, Sassone found that the corporate divisions he studied could produce the same amount of valuable output by reducing the number of managers and professionals while increasing the number of administrative staff.

This rebalancing works because more administrative support means the higher level employees can spend more time working deeply on the activities that produce the most value. Because the former are cheaper to hire than the latter, the result is the same work for less total staffing costs.

An important lesson lurks in these results that’s just as relevant now as it was then, back in the early days of the front office IT revolution: optimizing people’s ability to create value using their brains is complicated. Just because a given technology makes things easier doesn’t mean that it makes an organization more effective, you have to keep returning to the foundational question of what best supports the challenge of thinking hard about valuable things.

Some More Thoughts on Human APIs

An Idea Revisited

Last week, I wrote an article exploring the idea of using human APIs to optimize value production in knowledge work organizations. It generated fascinating discussion both in my email inbox and the post comment thread.

To help prod this discussion forward, I thought it might be useful if I try (not necessarily successfully) to respond to a few of the more common concerns I heard about the hAPI concept…

Concern #1: Human APIs would induce stultifying bureaucracy.

The idea of implementing strict routines for professional interaction conjures hellish images of TPS reports and forms filled out in triplicate.

This is a reasonable fear. To create a bureaucracy, however, requires more than just a commitment to systems, but also an obsession with these systems that becomes divorced from the actual objectives of the organization. This requires special circumstances, such as an organization becoming large and slow enough, with sparse enough competition, that it can support ranks of career bureaucrats without promptly going out of business.

It’s perfectly consistent to imagine a firm that embraces the structure of hAPIs, but also maintains an obsessive focus on producing value, dynamically adjusting these protocols as needed whenever they notice undue friction or discover a more effective alternative.

Keep in mind, for example, that the original Ford assembly line was incredibly systematic and rigid as compared to their older method for building cars, but this structure yielded, at least at first, a much more profitable and dynamic company.

Concern #2: Human APIs would kill creativity.

It’s commonly believed that creativity requires free-form discussion and thinking. Accordingly, too much structure around work processes would suppress this spark.

I think this concern is based on an overly-limited vision of hAPIs. One could imagine, for example, an hAPI that includes regular, unstructured, in-person discussions, or provides a clear mechanism for instigating a brainstorming session when inspiration strikes.

Nothing about the hAPI concept requires that communication be restricted to succinct, asynchronous, electronic missives. If anything, the exercise of understanding clearly what type of communication and coordination is most useful, and thinking about how to optimize this behavior, could help creative teams reach a new level of effectiveness.

Concern #3: Human APIs would limit human interaction (making everyone miserable).

Some fear that structuring communication would impede the casual conversation and serendipitous encounters that play a key role in modern organizational life.

To me, however, there’s a clear difference between formalizing standard communication and eliminating casual interaction. An organization that deploys hAPIs could still encourage chatter over coffee and in the lunchroom, or shooting the breeze in a friend’s office to recharge after a hard work session.

Making regular communication more effective doesn’t require that you squash the irregular variety.

The Human API Manifesto

The Bezos Mandate

In 2002, Amazon founder and CEO Jeff Bezos sent a mandate to his employees that has since become legendary in IT circles. It reads as follows:

All teams will henceforth expose their data and functionality through service interfaces.
Teams must communicate with each other through these interfaces.
There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.
It doesn’t matter what technology they use. HTTP, Corba, Pubsub, custom protocols — doesn’t matter.
All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.
Anyone who doesn’t do this will be fired.
Thank you; have a nice day!

This directive, which some informally call Bezos’s “API Manifesto,” transformed Amazon.

To be sure, transitioning to these formal APIs made life harder in the short term for its engineers. It was also expensive, both in terms of the money spent to develop the new interfaces, and the time lost that could have been dedicated to projects producing immediate revenue.

But once the company embraced Bezos’s mandate, it was able to operate its systems much more efficiently. It also enabled the launch of the public-facing Amazon Web Services, which now produces a much needed influx of profit, and allowed Amazon’s web store to easily expand to encompass outside merchants, a key piece in their retail strategy.

The impact of the API Manifesto has since expanded to the IT industry as a whole. From start-ups to massive organizations, the idea that information systems are more valuable when interacting through clearly specified and well supported API’s has become common.

Last week, for example, the cofounder of an IT firm told me the story of how he helped a large financial services firm implement an API for a set of services that were previously accessed in an ad hoc manner  (think: batched FTP).

It cost the firm a little over a million dollars to make this transition. He estimates it now helps them earn an additional $100 million in revenue each year through a combination of cost savings and the new customer acquisition applications enabled by providing a clearly specified and accessible interface for these services.

On Attention Capital

When I heard about the API manifesto, a provocative thought popped into my head: could these same underlying ideas apply to communication between people?

To provide some background to this question, let me first remind readers that my attention capital theory argues that the most valuable capital resource in a knowledge work organization is the brains of its employees. Or, to be more specific, the capacity of these brains to focus on information, process it through neurons, and then output more valuable information.

Success in knowledge work is about getting the best possible return on this attention capital, much as success in the industrial sector is about getting the best possible return from physical capital (factory equipment, trucks, shipping containers, etc.).

I believe that many knowledge work organizations currently get sub-standard returns on their attention capital because the workflows they deploy — which are often unspecified and emerged haphazardly  — depend too heavily on constant, unstructured communication, which conflicts with the way the human brain operates, reducing these brains’ capacity to think deeply and produce valuable output.

The natural follow up question to this observation is to ask what work would look like without constant unstructured messaging. It’s this follow-up question that brings me back to APIs…

The Human API Manifesto

Imagine if a Jeff Bezos figure at a major knowledge work firm sent a mandate to his or her employees that read something like this:

Our work will be seen as a collection of processes that take in specific inputs and produce specific outputs. Individuals are associated with the processes that they support.
Each process has well-defined and well-documentated communication protocols specifying how information comes into the process and how it leaves. It also has protocols specifying how the individuals associated with the process internally coordinate, including when and how this coordination occurs. We call these protocols human APIs (hAPIs).
There will be no other form of inter-personal communication allowed: no generic email inbox or instant messenger channel that can be used for any purpose, and no casually dropping by someone’s office to make a request. The only communication is through hAPIs.
Different hAPI specifications will include different technologies. Some will include no technologies at all. The details of the tools used to implement these protocols are less important than the protocols themselves.
If a particular request or notification seems too minor to justify its own process, or hAPI within an existing process, consider eliminating it. The need to specify hAPIs will help our organization focus more relentlessly on activities that create real value, and help eliminate minor asks that are convenient in the moment, but end up reducing the return on our attention capital in the long term.
Anyone who doesn’t do this will be fired.
Thank you; have a nice day!

Brilliant or Blunder?

A mandate like this would either turn out brilliant or a colossal blunder, which is exactly why it intrigues me.

The arguments in favor of this being brilliant include the observation that well-crafted protocols can minimize the cognitive overhead required to keep track of the different projects and tasks on your plate. Instead of drowning in an ever-filling pool of messages, you can instead work to satisfy a clear set of expectations and optimized action.

The structured nature of this communication also eliminates the requirement to constantly monitor general-purpose communication channels, which helps minimize attention residue — generating a non-trivial boost in your cognitive capacity. As I argued in Deep Work, if you can avoid constant “quick checks” of inboxes and channels, you can learn hard things faster, and produce higher quality output in less time.

In addition, well-documented hAPIs make it easier to integrate new hires or seamlessly hand off responsibilities when someone is sick or away on vacation, enabling a much more flexible deployment of an organization’s attention capital.

And as hinted above, the specificity required to implement hAPIs forces an organization to be transparent about all the ways their attention capital is being tapped, supporting a move toward long term value production and away from short term convenience.

On the other hand, there are many reasons to suspect this human API approach could prove disastrous if embraced.

For one thing, it would be a massive pain to have to reduce the messy ambiguity of the typical knowledge work organization to a set of clearly specified processes and hAPIs.

Even once completed, this approach might not be nearly agile enough to keep up with unexpected needs or demands, creating lots of hard edges at which projects are stalled or opportunities missed.

It’s also possible that my attention capital theory is wrong. The current trend in workflows within the knowledge sector is to prioritize flexible coordination over maximizing cognitive output. Maybe this is actually the best thing to do.

Finally, it’s worth acknowledging the practical difficulty of getting an entire organization to actually buy in to such a radical transformation (for more on this, see Sam Carpenter’s Work the System).

An Ambiguous Conclusion

Something like the human API approach might be the key to evolving the knowledge sector to a new level of effectiveness. Or it’s stupid.

I can’t quite tell.

If you’ve had any experience with this type of approach (for better or for worse) in your own organization, I’d love to hear about it (interesting@calnewport.com). If the idea sparks a strong reaction in you (for better or for worse), I’d love for you to elaborate in the comments.