Study Hacks Blog

The Difficulty of Deep Projects

For the sake of discussion, let’s define a deep project to be a pursuit that leverages your expertise to generate a large amount of new value. These projects require deep work to complete, are rarely urgent and often self-initiated (e.g., no one is demanding their immediate completion), and have the potential to significantly transform or advance your professional life.

Examples of deep projects include writing a highly original book, creating an irresistible piece of software, or introducing a new academic theory.

The problem with deep projects is that they’re complicated and really hard. Almost any other activity will seem more appealing in the moment — so they keep getting pushed aside as something that you’ll “get to soon.”

Recently, I’ve been experimenting with a habit that seems to help with this challenge.

I call it, the depth deck…

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The Disconnected Director

Ben Casnocha recently sent me a Hollywood Reporter interview with the director Christopher Nolan. About halfway through the transcript, the journalist asks Nolan if it’s true that he doesn’t have an e-mail address.

“It is true,” Nolan responds.

He then elaborates:

Well, I’ve never used email because I don’t find it would help me with anything I’m doing. I just couldn’t be bothered about it.

What interests me about Nolan’s answer is not the details of his technology choices (his ability to avoid e-mail is specific to his incredibly esoteric job), but instead the thought process he applied in making them.

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A Call to Read

Maura Kelly begins her 2012 manifesto in The Atlantic with a Pollan-esque exhortation:

Read books. As often as you can. Mostly classics.

Kelly is just one voice in the growing Slow Reading movement (c.f.., here and here). The motivating idea behind this movement is simple: it’s good for the soul and the mind to regularly read — without distraction or interruption — hard books.

There was a time when intellectual engagement necessarily included long hours reading old-fashioned paper tomes. But in an age when a digital attention economy is ascendant, it’s now possible to satisfy this curiosity without ever consuming more than a couple hundred highly digested and simplified words at a time.

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Deciding the Undecidable

In a recent blog post I introduced the notion of undecidable tasks — a particularly important type of work that’s not well covered by standard productivity advice.

These tasks are crucial to my job as an academic — as they are to many creative fields — so I devote a lot of attention to understanding how best to tackle them.

Today I want to share three tips along these lines that have worked well for me…

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A Useful Metaphor

In the first chapter of The Happiness Hypothesis, Jonathan Haidt introduces the metaphor of the rider and the elephant. When trying to conceptualize his own weakness in the face of his best intentions, he explains:

I [am] a rider on the back of an elephant. I’m holding the reins in my hands, and by pulling one way or the other I can tell the elephant to turn, to stop, or to go. I can direct things, but only when the elephant doesn’t have desires of his own. When the elephant really wants to do something, I’m no match for him.

Ever since I first read these words, they stuck with me as useful for understanding the working world in particular. The whole edifice that we now call “productivity advice” distills, I realized, to instructions for cajoling the elephant. If you’re not firm, it’ll do what it wants to do.

It’s against this backdrop that I present the following truism about this metaphorical quadruped: if you’re not exceptionally clear about where you want it to go, it will wander.

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The Decision Problem

In 1928, the mathematician David Hilbert posed a challenge he called the Entscheidungsproblem (which translates to  “decision problem”).

Roughly speaking, the problem asks whether there exists an effective procedure (what we would today call an “algorithm”) that can take as input a set of axioms and a mathematical statement, and then decide whether or not the statement can be proved using those axioms and standard logic rules.

Hilbert thought such a procedure probably existed.

Eight years later, in 1936, a twenty-four year old doctoral student named Alan Turing proved Hilbert wrong with a monumental (and surprisingly readable) paper titled, On Computable Numbers, with an Application to the Entscheidungsproblem.

In this paper, Turing proved that there exists problems that cannot be solved systematically (i.e., with an algorithm). He then argued that if you could solve Hilbert’s decision problem, you could use this powerful proof machine to solve one of these unsolvable problems: a contradiction!

Though Turing was working before computers, his framework and results formed the foundation of theoretical computer science (my field), as they can be used to explore what can and cannot be solved by computers.

Over time, theoreticians enumerated many problems that cannot be solved using a fixed series of steps. These came to be known as undecidable problems, while those that can be solved mechanistically were called decidable.

The history of theoretical computer science is interesting in its own right, especially given Hollywood’s recent interest in Turing.

But in this post, I want to argue a less expected connection: Turing’s conception of decidable and undecidable problems turns out to provide a useful metaphor for understanding how to increase your value in the knowledge work economy…

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