Deep Work: Rules for Focused Success in a Distracted World(7)

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.