I was asked to develop a briefing on the future of work by an investment banking firm, and one of the questions that came up was ‘what are the most critical or essential work skills for the future’? I’ve decided to dedicate a series to the question, and over the next few months I will enumerate 10 or so skills.

Tyler Cohen, in Average Is Over, used the metaphor of freestyle chess — where humans can use chess programs to determine their moves (a topic that Diego Rasskin Gutman also touched on in Chess Metaphors: Artificial Intelligence and the Human Mind). Last year, Joshua Rothman interviewed Cohen on that subject in Are Computers Making Society More Unequal?:

Rothman: One of the most interesting sections of the book is about “freestyle” chess competitions, in which humans and computers play on teams together — often the computers make the moves, but sometimes the humans intervene. How has chess software changed the “labor market” in chess players?

Cohen: When humans team up with computers to play chess, the humans who do best are not necessarily the strongest players. They’re the ones who are modest, and who know when to listen to the computer. Often, what the human adds is knowledge of when the computer needs to look more deeply. If you’re a really good freestyle player, you consult a bunch of different programs, which have different properties, and you analyze the game position on all of them. You try to spot, very quickly, where the programs disagree, and you tell them to look more deeply there. They may disagree along a number of lines, and then you have to make some judgments. That’s hard — but the good humans do that better than computers do. Even very strong computers don’t have that meta-rational sense of when things are ambiguous. Today, the human-plus-machine teams are better than machines by themselves. It shows how there may always be room for a human element.

Rothman: You believe that, in the future, the most well-compensated workers will be something like freestyle chess players.

Cohen: Think in terms of this future middle-class job: You read medical scans, and you work alongside a computer. The computer does most of the judging, but there are some special or unusual scans where you say, “Hmm, that’s not quite right — I need a doctor to look at this again and study it more carefully.” You’ll need to know something about medicine, but it won’t be the same as being a doctor. You’ll need to know something about how these programs work, but it won’t be the same as being a programmer. You’ll need to be really good at judging, and being dispassionate, and you’ll have to have a sense of what computers can and cannot do. It’s about working with the machine: knowing when to hold back, when to intervene.

We are transitioning to a world in which tools like chess programs will be an essential part of our work, and we will, first of all, have to learn to use them fluently. We will have to balance their strengths and blind spots, just as we do with people, and as programs grow even more capable, we will have to learn when to stand to the side when it is time for the algorithms to take the next step, make the next move, or to decide who to hire or fire.

I looked into a 2011 report from the Institute for the Future — Future Work Skills 2020 — to see if their ideas align with Cohen’s. The first on the list was sense-making, and they used chess as an example of a skillset where humans cannot hope to defeat computers:

1 Sense-Making definition: ability to determine the deeper meaning or significance of what is being expressed

As smart machines take over rote, routine manufacturing and services jobs, there will be an increasing demand for the kinds of skills machines are not good at. These are higher- level thinking skills that cannot be codified. We call these sense-making skills, skills that help us create unique insights critical to decision making.

When IBM’s supercomputer, Deep Blue, defeated chess grandmaster Gary Kasparov, many took this of a sign of its superior thinking skills. But Deep Blue had won with brute number-crunching force (its ability to evaluate millions of possible moves per second), not by applying the kind of human intelligence that helps us to live our lives. A computer may be able to beat a human in a game of chess or Jeopardy by sheer force of its computational abilities, but if you ask it whether it wants to play pool, it won’t be able to tell whether you are talking about swimming, financial portfolios, or billiards.

As computing pioneer Jaron Lanier points out, despite important advances in Artificial Intelligence (AI) research it is still the case that, “if we ask what thinking is, so that we can then ask how to foster it, we encounter an astonishing and terrifying answer: we don’t know.” As we renegotiate the human/machine division of labor in the next decade, critical thinking or sense-making will emerge as a skill workers increasingly need to capitalize on.

But as Cohen points out, freestylers beat the best chess programs.

I would mix that concept of sense-making, to the more specific concept of being able to apply or cooperate with other information tools and resources creatively, which I call freestyling. Freestylers can adapt to novel situations, apply innovative ideas and techniques, and work with others to accomplish some aim. This includes the capacity to lead when necessary, and follow others’ lead when not, which is the defining attribute of leanership, or what Lazlo Bock at Google calls emergent leadership (see Lazlo Bock talks about hiring at Google, and why the GPA is irrelevant).

So, the first skill in this series — but not necessarily most important — is freestyling.