Range: How Generalists Triumph in a Specialized World

by David Epstein

“deliberate practice,” the only kind that counts in the now-ubiquitous ten-thousand-hours rule to expertise. The “rule” represents the idea that the number of accumulated hours of highly specialized training is the sole factor in skill development, no matter the domain.

“Late Specialization” as “the Key to Success”; another, “Making It to the Top in Team Sports: Start Later, Intensify, and Be Determined.”

learning itself is best done slowly to accumulate lasting knowledge, even when that means performing poorly on tests of immediate progress. That is, the most effective learning looks inefficient; it looks like falling behind.

Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.

The challenge we all face is how to maintain the benefits of breadth, diverse experience, interdisciplinary thinking, and delayed concentration in a world that increasingly incentivizes, even demands, hyperspecialization.

While it is undoubtedly true that there are areas that require individuals with Tiger’s precocity and clarity of purpose, as complexity increases—as technology spins the world into vaster webs of interconnected systems in which each individual only sees a small part—we also need more Rogers: people who start broad and embrace diverse experiences and perspectives while they progress. People with range.

Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question.

In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.

In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons.

Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.

Susan Polgar has written, “you can get a lot further by being very good in tactics”—that is, knowing a lot of patterns—“and have only a basic understanding of strategy.”

Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.

“AI systems are like savants.” They need stable structures and narrow worlds.

The world is not golf, and most of it isn’t even tennis. As Robin Hogarth put it, much of the world is “Martian tennis.” You can see the players on a court with balls and rackets, but nobody has shared the rules. It is up to you to derive them, and they are subject to change without notice.

The more constrained and repetitive a challenge, the more likely it will be automated, while great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one.

successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it. Less successful problem solvers are more like most students in the Ambiguous Sorting Task: they mentally classify problems only by superficial, overtly stated features, like the domain context. For the best performers, they wrote, problem solving “begins with the typing of the problem.” As education pioneer John Dewey put it in Logic, The Theory of Inquiry, “a problem well put is half-solved.”

“Match quality” is a term economists use to describe the degree of fit between the work someone does and who they are—their abilities and proclivities.