Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will - Softcover

CHAPTER ONE

COMPUTERS ARE IMPROVING FASTER THAN YOU ARE

As Technology Becomes More Awesomely Able, What Will Be the High-Value Human Skills of Tomorrow?

I am standing on a stage, behind a waist-high podium with my first name on it. To my right is a woman named Vicki; she’s behind an identical podium with her name on it. Between us is a third podium with no one behind it, just the name “Watson” on the front. We are about to play Jeopardy!

This is the National Retail Federation’s mammoth annual conference at New York City’s Javits Center, and in addition to doing some onstage moderating, I have insanely agreed to compete against IBM’s Watson, the cognitive computing system, whose power the company wants to demonstrate to the 1,200 global retail leaders sitting in front of me. Watson’s celebrated defeat of Jeopardy!’s two greatest champions is almost a year old, so I’m not expecting this to go well. But I’m not prepared for what hits me.

We get to a category called “Before and After at the Movies.” Jeopardy! aficionados have seen this category many times over the years, but I have never heard of it. First clue, for $200: “Han Solo meets up with Lando Calrissian while time traveling with Marty McFly.”

Umm . . . what?

Watson has already buzzed in. “What is The Empire Strikes Back to the Future?” it responds correctly.

It picks the same category for $400: “James Bond fights the Soviets while trying to romance Ali MacGraw before she dies.” I’m still struggling with the concept, but Watson has already buzzed in. “What is From Russia with Love Story?” Right again.

By the time I figure this out, Watson is on the category’s last clue: “John Belushi & the boys set up their fraternity in the museum where crazy Vincent Price turns people into figurines.” The correct response, as Watson instantly knows, is “What is Animal House of Wax?” Watson has run the category.

My humiliation is not totally unrelieved. I do get some questions right in other categories, and Watson gets some wrong. But at the end of our one round I have been shellacked. I actually don’t remember the score, which must be how the psyche protects itself. I just know for sure that I have witnessed something profound.

Realize that Watson is not connected to the Internet. It’s a freestanding machine just like me, relying only on what it knows. It has been loaded with the entire contents of Wikipedia, for example, and much, much more. No one types the clues into Watson; it has to hear and understand the emcee’s spoken words, just as I do. In addition, Watson is intentionally slowed down by a built-in delay when buzzing in to answer a clue. We humans must use our prehistoric muscle systems to push a button that closes a circuit and sounds the buzzer. Watson could do it at light speed with an electronic signal, so the developers interposed a delay to level the playing field. Otherwise I’d never have a prayer of winning, even if we both knew the correct response. But, of course, even with the delay, I lost.

So let’s confront reality: Watson is smarter than I am. In fact, I’m surrounded by technology that’s better than I am at sophisticated tasks. Google’s autonomous car is a better driver than I am. The company has a whole fleet of vehicles that have driven hundreds of thousands of miles with only one accident while in autonomous mode, when one of the cars was rear-ended by a human driver at a stoplight. Computers are better than humans at screening documents for relevance in the discovery phase of litigation, an activity for which young lawyers used to bill at an impressive hourly rate. Computers are better at detecting some kinds of human emotion, despite our million years of evolution that was supposed to make us razor sharp at that skill.

One more thing. I competed against Watson in early 2012. Back then it was the size of a bedroom. As I write, it has shrunk to the size of three stacked pizza boxes, yet it’s also 2,400 percent faster.

More broadly, information technology is doubling in power roughly every two years. I am not—and I’ll guess that you’re not either.

A NIGHTMARE FUTURE?

The mind-bending progress of information technology makes it easier every day for us to imagine a nightmare future. Computers become so capable that they’re simply better at doing thousands of tasks that people now get paid to do. Sure, we’ll still need people to make high-level decisions and to develop even smarter computers, but we won’t need enough such workers to keep the broad mass of working-age people employed, or for their living standard to rise. And so, in the imaginary nightmare future, millions of people will lose out, unable finally to best the machine, struggling hopelessly to live the lives they thought they had earned.

In fact, as we shall see, substantial evidence suggests that technology advances really are playing a role in increasingly stubborn unemployment, slow wage growth, and the trend of college graduates taking jobs that don’t require a bachelor’s degree. If technology is actually a significant cause of those trends, then the miserable outlook becomes hard to dismiss.

But that nightmare future is not inevitable. Some people have suffered as technology has taken away their jobs, and more will do so. But we don’t need to suffer. The essential reality to grasp, larger than we may realize, is that the very nature of work is changing, and the skills that the economy values are changing. We’ve been through these historic shifts a few times before, most famously in the Industrial Revolution. Each time, those who didn’t recognize the shift, or refused to accept it, got left behind. But those who embraced it gained at least the chance to lead far better lives. That’s happening this time as well.

While we’ve seen the general phenomenon before, the way that work changes is different every time, and this time the changes are greater than ever. The skills that will prove most valuable are no longer the technical, classroom-taught, left-brain skills that economic advances have demanded from workers over the past 300 years. Those skills will remain vitally important, but important isn’t the same as valuable; they are becoming commoditized and thus a diminishing source of competitive advantage. The new high-value skills are instead part of our deepest nature, the abilities that literally define us as humans: sensing the thoughts and feelings of others, working productively in groups, building relationships, solving problems together, expressing ourselves with greater power than logic can ever achieve. These are fundamentally different types of skills than those the economy has valued most highly in the past. And unlike some previous revolutions in what the economy values, this one holds the promise of making our work lives not only rewarding financially, but also richer and more satisfying emotionally.

Step one in reaching that future is to think about it in a new way. We shouldn’t focus on beating computers at what they do. We’ll lose that contest. Nor should we even follow the inviting path of trying to divine what computers inherently cannot do—because they can do more every day.

The relentless advance of computer capability is of course merely Moore’s Law at work, as it has been for decades. Still, it’s hard for us to appreciate all the implications of this simple trend. That’s because most things in our world slow down as they get bigger and older; for evidence, just look in the mirror. It’s the same with other living things, singly or in groups. From protozoa to whales, everything eventually stops growing. So do organizations. A small start-up company can easily grow 100 percent a year, but a major Fortune 500 firm may struggle to grow 5 percent.

Technology isn’t constrained that way. It just keeps getting more powerful. Sony’s first transistor radio was advertised as pocket-sized, but it was actually too big, so the company had salesmen’s shirts specially made with extra-large pockets; that radio had five transistors. Intel’s latest chip, the size of your thumbnail, has five billion transistors, and its replacement will have ten billion. Today’s infotech systems, having become as awesomely powerful as they are, will be 100 percent more awesomely powerful in two years. Moore’s law must end eventually, but new technologies in development could be just as effective, and better algorithms are already multiplying computing power in some cases even more than hardware improvements are doing. To imagine that technology won’t keep advancing at a blistering pace seems unwise.

Consider what is being doubled. It isn’t just year-before-last’s achievement in computing power. What gets doubled every two years is everything that has been achieved in the history of computing power up to that point. Back when that progression meant going from five transistors in a device to ten, it didn’t much change the world. Now that it means going from five billion transistors on a tiny chip to ten billion to twenty billion to forty billion—that’s three doublings, just six years—it means literally more than we can imagine.

That’s because it’s so unlike everything else in our world in ways even beyond physical growth rates. For us humans, learning, like growing, gets harder with time. When humans learn to do something, we make slow progress at first—learning how to hold the golf club or turn the steering wheel smoothly—then rapid progress as we get the hang of it, and then our advancement slows down. Pretty soon, most of us are as good as we’re going to get. We can certainly keep improving through devoted practice, but each advance is typically a bit smaller than the one before.

Information technology is just the opposite. When a doubling of computing power for a given price meant going from five transistors to ten, it made a device smarter by only five transistors. Now, after many doublings, the current doubling will make a device smarter by five billion transistors, and the next one will make a device smarter by ten billion.

While people get more skilled by ever smaller increments, computers get more capable by ever larger ones.

The issue is clear and momentous. As technology becomes more capable, advancing inexorably by ever longer two-year strides and acquiring abilities that are increasingly complex and difficult, what will be the high-value human skills of tomorrow—the jobs that will pay well for us and our kids, the competencies that will distinguish winning companies, the traits of dominant nations? To put it starkly: What will people do better than computers?

CHAPTER TWO

GAUGING THE CHALLENGE

A Growing Army of Experts Wonder If Just Maybe the Luddites Aren’t Wrong Anymore.

In the movie Desk Set, a 1957 romantic comedy starring Katharine Hepburn and Spencer Tracy, Hepburn plays the head of the research department at a major TV network. Today a TV network’s research department focuses entirely on audience research, but back then it was a general information resource for anyone at the company, and yes, networks and other companies really had such departments. Equipped with two floors of reference works and other books, its staff stood ready to supply any information that any employee might ask for—the opening lines of Hiawatha, the weight of the earth, the names of Santa’s reindeer (all of which were queries for Hepburn’s department in the movie). That is, employees could pick up the phone, call Katharine Hepburn’s character, and ask in their own words for any information, and she and her staff would search a vast trove of data and return an answer far faster than the caller could ever have found it.

Hepburn’s character is named Miss Watson.

All is well until one day the network boss decides to install a computer—an “electronic brain,” they call it—named EMERAC (a clear reference to ENIAC and UNIVAC, the wonder machines of the era). It was invented by the Spencer Tracy character. Shortly before Miss Watson hears the news that EMERAC is coming to her department, she sees it demonstrated elsewhere, translating Russian into Chinese, among other feats. Her assessment, as expressed to her coworkers: “Frightening. Gave me the feeling that maybe, just maybe, people were a little bit outmoded.”

The Tracy character, Richard Sumner, shows up to install the machine, and Miss Watson and her staff assume they’ll be fired once it’s up and running. In a memorable scene, he demonstrates the machine to a group of network executives and explains its advantages:

Sumner: “The purpose of this machine, of course, is to free the worker—”

Miss Watson: “You can say that again.”

Sumner: “—to free the worker from the routine and repetitive tasks and liberate his time for more important work.”

Miss Watson and the rest of the research staff are indeed fired, but before they can clean out their desks, EMERAC botches some requests it can’t handle—a call for information on Corfu, for example, returns reams of useless data on the word “curfew,” while a staffer scurries into the stacks and gets the needed answers the old-fashioned way. And then it turns out that the researchers actually should not have received termination notices after all. An EMERAC computer in the payroll department had gone haywire and fired everyone in the company. The error is corrected, the research staffers keep their jobs and learn to work with EMERAC, Miss Watson wisely decides to marry the Spencer Tracy character and not the Gig Young character (part of the mandatory romantic subplot), and once again all is well.

Desk Set is extraordinarily prescient about some future capabilities and uses of computers, and also faithful to the fearful popular sentiment about them. Of course, Miss Watson is exactly the human predecessor of today’s Watson cognitive computing system. (Are the names a coincidence? The film’s opening credits include this intriguing one: “The filmmakers gratefully acknowledge the cooperation and assistance of the International Business Machines Corporation.” IBM’s founder was Thomas J. Watson, namesake of today’s Watson computing system, and his son was CEO at the time of the film.) EMERAC as explained by Sumner in the film is remarkably similar to today’s Watson: All the information in all those books in the research library—encyclopedias, atlases, Shakespeare’s plays—was fed into the machine, which could then respond instantly to natural-language requests (typed, not spoken) for information. Even in 1957 the idea was clear; the technology just wasn’t ready.

The research staffers’ fears about being replaced by a computer were also a sign of things to come. “I hear thousands of people are losing their jobs to these electronic brains,” one of them says. She heard right, and the thousands would become millions. At the same time, the corporate response intended to calm those fears has remained just what Sumner said in the movie—that computers would “free the worker from the routine and repetitive tasks” so he or she could do “more important work.” To this day it’s striking how everyone working on advanced information technology seems to feel defensive about the implicit threat of eliminating jobs and...