Average Is Over: Why the Skills Required for Great Jobs Are Changing

Coding might be the answer for some. But the ability to apply advanced technology to effectively and efficiently solve real world problems may be a more valuable and scarcer resource.

Servicing and enhancing machines themselves will continue to be an important skill. These devices contribute to and even manage much of our daily existence.

However, those skills are broadly available. The whining about a skills gap is really a managerial complaint about the cost of acquiring and keeping good talent in an acutely competitive global economy. Many enterprises simply do not - or can not afford to pay what the available talent is worth on the open market.

The challenge is in finding people who have the competence, imagination and vision to take those extraordinary tools and in combination with them, generate extraordinary results. JL

Shane Parrish reviews Tyler Cowen's new book in Farnam Street:

“Information isn’t what’s scarce; it’s the willingness to do something with it,”
“Welcome to the Hyper-Meritocracy,” Cowen writes in his latest book Average Is Over.
This is an important book. Cowen is his typical thought-provoking self, showing us a possible (and probable, in my opinion) future where the skills needed to succeed will differ from those today.
Welcome to a world of extremes. On one hand many people are “seeing the erosion of their economic futures.” On the other hand, “the very top earners, who often have advanced postsecondary degrees, are earning much more.”
This is how the book got its title: Average is Over.

This maxim will apply to the quality of your job, to your earnings, to where you live, to your education and to the education of your children, and maybe even to your most intimate relationships. Marriages, families, businesses, countries, cities, and regions all will see a greater split in material outcomes; namely, they will either rise to the top in terms of quality or make do with unimpressive results.

Cowen believes that workers will increasingly fall into two categories.

The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? Worst of all, are you competing against the computer? Are computers helping people in China and India compete against you?
If you and your skills are a complement to the computer , your wage and labor market prospects are likely to be cheery . If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other.

Welcome to the age of machine intelligence.

It’s becoming increasingly clear that mechanized intelligence can solve a rapidly expanding repertoire of problems. Solutions began appearing on the margins of the world’s interests . Deep Blue, an IBM computer, defeated the then– world champion Garry Kasparov in a chess match in 1997. Watson, a computer program, beat Ken Jennings— the human champion— on Jeopardy! in 2010, surpassing most expectations as to how quickly this would happen.
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We’re close to the point where the available knowledge at the hands of the individual, for questions that can be posed clearly and articulately, is not so far from the knowledge of the entire world. Whether it is through Siri, Google, or Wikipedia, there is now almost always a way to ask and— more importantly— a way to receive the answer in relatively digestible form.
It must be emphasized that every time you use Google you are relying on machine intelligence. Every time Facebook recommends a new friend for you or sends an ad your way. Every time you use GPS to find your way to a party.
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Date-matching algorithms are steering our love lives and replacing the matchmaker. Match.com recently improved its services, and as of summer 2011 more than half of the emails sent on the service originate from recommended matches, rather than from unaided individual choices. Better algorithms often are seen as the future of the sector, whether or not they really find the best person for us. Arguably the machine recommendations are a way of tricking the user into making a plausible date choice rather than cruising more profiles and postponing a decision; that possibility illustrates our willingness to defer to the machines, even when they aren’t necessarily better at the task at hand.

Think we’re ages away from machines doing amazing things? Do you remember the New York Times Story that illustrated how Target, through algorithms, knew a teenage girl was pregnant before her father.
In an age of machine intelligence, where will most of the benefits go?

To put the question in the bluntest possible way, let’s say that machine intelligence helps us make a lot more things more cheaply, as indeed it is doing. Where will most of the benefits go? In accord with economic reasoning, they will go to that which is scarce.
In today’s global economy here is what is scarce:
1. Quality land and natural resources
2. Intellectual property, or good ideas about what should be produced.
3. Quality labor with unique skills
Here is what is not scarce these days:
1. Unskilled labor, as more countries join the global economy
2. Money in the bank or held in government securities, which you can think of as simple capital, not attached to any special ownership rights (we know there is a lot of it because it has been earning zero or negative real rates of return)

As machines become more powerful, the people who benefit will be the people who are “adept at working with computers and with related devices for communications and information processing.” The way to earn well will be to augment the value of tech, even if only by a small bit.

That means humans with strong math and analytic skills, humans who are comfortable working with computers because they understand their operation, and humans who intuitively grasp how computers can be used for marketing and for other non-techie tasks. It’s not just about programming skills; it is also often about developing the hardware connected with software, understanding what kind of internet ads connect with their human viewers, or understanding what shape and color makes an iPhone attractive in a given market. Computer nerds will indeed do well, but not everyone will have to become a computer nerd.

The key to the future is the ability to “mix technical knowledge with solving real world problems.”
There is a chapter in Cowen’s book called “The Freestyle future.” Rather than type out lengthy excerpts from the book, Cowen explains the concept briefly in this interview:

Russ: So let’s talk about what you’ve learned as a chess fan. And you write at some length. At first I was rather taken aback by this, but I grew to find it quite fascinating. You write at some length about the role of machines in chess tournaments, and particularly in freestyle. Talk about that and why it’s a nice potential template for future human interaction.
Cowen: Freestyle is a form of chess where a human teams up with a computer. So, if you play human-and-computer against computer, for the most part human-and-computer, if it’s a practiced human, will beat the computer. Even though computers per se are much stronger than humans at chess, it’s the team that’s stronger than either one. And I think this is a good metaphor for a lot of what our job market future will look like. So there’s a big chunk of the book that looks rather closely at freestyle chess and tries to see what we can learn from it.
Russ: The thing I found most provocative about that is that the best freestyle teams do not necessarily have the best human players. In fact that could be something of a handicap.
Cowen: That’s right. The really good human players are too tempted to override the computer and substitute in their own judgment. The best freestyle teams, they are quite epistemically modest, the human or humans involved. And what they are really good at is asking questions. So they’ll run two or three different computer programs and then just check on where do those programs disagree. And then they’ll probe more on those points. And that’s what the humans do well that the computers, at least not yet, aren’t able to copy. So it’s knowing what questions to ask that has become the important human skill in this freestyle endeavor.
Russ: So, applying that to the medical diagnosis example you gave earlier, it suggests I don’t want the guy or the woman who had the best grades in medical school or the most arrogant–which is often in today’s world, can be, the best doctor. I might want the most modest doctor, or not the most modest, but someone who is willing to let the diagnosis provided by the machine be the “right” one.
Cowen: That’s right. So, wisdom and modesty will become much greater epistemic virtues in the future scheme. I think that’s overall a good thing. We should revere those qualities more. And we will have to, looking forward.

We have to ask questions. And we have to be “meta-rational,” to borrow a term from decision theory. “That is,” Cowen writes, “I must realize that in most situations the judgment of (Shredder, the computer chess program) is simply better than my own, and defer accordingly. I am most likely to succeed in overriding the judgement of Shredder in complex strategic positions, in some endgames, when the program is fooling around with questionable opening choices, and when the program is getting greedy for material. … I can’t out-calculate the machine unless it boils down to the machine’s shorter time horizon, and I don’t always know if the length of the time horizon is the key issue.”
Most of us don’t want to listen to the machines. We think we’re smarter and we don’t know enough to know where we are smarter and where we’re not. Without knowing we operate outside of our circle of competence. So as much as anything the future will mean knowing our limits and wanting/being willing to listen to machines. This goes against the entire “go with your gut” industry.
In another interview, Cowen says:

So I think as humans we’re somewhat programmed to be a bit rebellious and to not want to be controlled, which is perfectly understandable given that others are trying to control us as often as they are. But that’s going to mean in those new settings, which we’ve never biologically evolved to handle, we’re going to screw up an awful lot.

What are the broader lessons we can take away?

1. Human-computer teams are the best teams.
2. The person working the smart machine doesn’t have to be an expert in the task at hand.
3. Below some critical level of skill, adding a man to the machine will make the team less effective than the machine working alone.
4. Knowing one’s limits is more important than it used to be.

If we merge Cowen’s thoughts in Average is Over with How Children Succeed and the concept of Grit, we come to the conclusion that in a world of information, what will be scarce is the ability to sit down in a quiet room and apply yourself. “Information isn’t what’s scarce; it’s the willingness to do something with it,” Cowen argues. But maybe we’re getting lazy.

So if you’re an individual, say from China or India, and you’re really smart and motivated, you’re going to do much better in this new world than say 10 or 20 years ago.
But there are a lot of people in the wealthier countries, I wouldn’t describe them as lazy, but they’re not super motivated. They think they can more or less get by. I think in relative terms those people are already starting to see lower wages because they’re just not quite the prize commodities they think they are. They’ll do okay. They’ll be able to get jobs, but they’re not really individuals who are going to see a lot of income growth, and I think this could be a rude awakening to a lot of people.

Echoing some of the advice of Genevieve Bell and Christian Madsbjerg Cowen supports humanities.

I think there will be a lot of so-called soft humanities roots that could have potentially big payoffs for hard, smart workers. It’s not all about how we all become programmers, and a lot of that kind of work can be outsourced or given to smart machines anyway.
So I would just stress to people that the value of really beginning to understand how other people think, to the extent you can acquire that in education, if that’s what you love, if that’s what you’re good at, that’s great. Not everyone has to jump on the computer science bandwagon. Though, of course, many people should.