\nWorkers more and more will come to be classified 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? \u2026 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. That\u2019s why average is over.<\/p>\n<\/blockquote>\n
But what about management? I interviewed Cowen last month about his vision of the future, and where he sees managers fitting into it. An edited version of our conversation follows.<\/p>\n
WF:\u00a0What do you see as the main career lessons of the book?<\/strong><\/p>\n
TC:<\/strong>\u00a0One thing the book suggests is that only being technically skilled may not be that useful, because those jobs can be outsourced or even turned over to smart machines. But people who can bridge that gap between technical skills and knowing some sector in a way that\u2019s more creative or more intuitive, that\u2019s where the large payoffs will come.<\/p>\n
A classic example is Mark Zuckerberg with Facebook. Obviously a great programmer, but had he just gone out to be paid as a programmer he wouldn\u2019t be that well off. He was a psychology major \u2014 he understood how to appeal to users, to get them to come back to the site. So he had that integrative knowledge.<\/p>\n
For people who are not technically skilled, marketing, persuasion, cooperation, management, and setting expectations are all things that computers are very far from being good at.\u00a0It comes down to just communicating with other human beings.<\/p>\n
WF:<\/strong>\u00a0Where does management fit into this?<\/strong><\/p>\n
TC:<\/strong>\u00a0In any company, you need someone to manage the others, and management is a very hard skill. Relative returns to managers have been rising steadily; good managers are hard to find. And, again, computers are not close to being able to do that. So I think the age of the marketer, the age of the manager are actually our immediate future.<\/p>\n
WF:\u00a0I was talking recently with Andrew McAfee of MIT, whom you reference the book, and he mentioned the way software might even replace some pieces of management, though not managers themselves. Do you see algorithms and software encroaching majorly into that area?<\/strong><\/p>\n
TC:<\/strong>\u00a0If it\u2019s just measuring how hard people work, how long they\u2019re at their desks, how good a job they do, how good a doctor or a salesman is adjusting for quality of customer, there\u2019s a huge role there for software. But to actually replace managers, for the most part I don\u2019t see that. At least not for the time horizon I\u2019m writing about, 10 to 20 years out.<\/p>\n
WF:<\/strong>\u00a0One thing that McAfee talks about is the idea that people want to get their information from a human; they can be very distrustful of a computer just spitting out a recommendation.<\/strong><\/p>\n
Do you see that being true in a management context as well? That part of my job, if I\u2019m running a company or division, is being able to understand machine intelligence and deliver it personally?<\/strong><\/p>\n
TC:<\/strong>\u00a0That\u2019s right. You will translate what the machine says and try to motivate people to do it. Professors and teachers will be more like coaches or tutors, rather than carriers of information. They\u2019ll steer you to the program, tell you which classes to take, and be a kind of role model to get you excited about doing the work.<\/p>\n
It\u2019s a very important skill, and hard to learn. But I think you\u2019ll see this kind of pattern again and again.<\/p>\n
WF:<\/strong>\u00a0How else do you see management changing?<\/strong><\/p>\n
TC:<\/strong>\u00a0Management \u2014 for all the change we like to talk about \u2014 has actually been pretty static for a while. But smart machines and smart software are going to change management drastically, and in general we\u2019re not ready for this. We will need truly new managerial thinking, not just new in the cliched sense of repackaging with new rhetoric and new categories.<\/p>\n
WF:<\/strong>\u00a0I wanted to ask you a little more about the machine-human teams. Freestyle chess \u2014 where human and computer teams play together, and\u00a0outperform either on their own<\/a>\u00a0\u2013 is the example throughout the book.<\/strong><\/p>\n
What skills does the freestyle chess master have that the grandmaster doesn\u2019t?<\/strong><\/p>\n
TC:<\/strong>\u00a0The program, of course, does most of the calculations. The one skill the human needs when playing freestyle is how to ask the program good questions. Knowing what questions to ask is how you beat a solo program playing against you, and you don\u2019t even have to be very good at chess. You need to understand chess at some core level, and you need to understand what different programs can and cannot understand.<\/p>\n
It\u2019s a kind of meta rationality. Knowing not to overrule the programs very much, but also knowing they\u2019re not perfect, and knowing when to probe. And I think that, in management, those will be the important human skills.<\/p>\n
WF:<\/strong>\u00a0How do you determine where the line is between when the employee is able to add value above and beyond what computer intelligence is giving them, and when the software itself can replace them?<\/strong><\/p>\n
For example, I\u2019m imagining some analytics software. Someone is very skilled at using it, they\u2019re drawing some conclusions, they\u2019re presenting them to people. But the next version of software may have built-in the ability to make those inferences. What skills keep that human from being replaced?<\/strong><\/p>\n
TC:\u00a0<\/b>People who can judge that there\u2019s more to the matter than the software can grab; people who can judge the fact that there\u2019s a need for a different kind of software for the problem; people who know when to leave the software alone and get out of its way.<\/p>\n
Those are difficult to acquire and often quite intangible skills, but I think they\u2019re increasingly valuable. You can think of other professional areas, like law or medicine, where you let the software do a lot of the work but you can\u2019t uncritically defer to it. Software is bad at common sense in a lot of ways and it misses a lot of context. It\u2019s people who can provide context.<\/p>\n
WF:\u00a0You have an interesting section in the book with respect to specialization in science. Do you see the path towards greater specialization as the path to career security? Or is there still a role for generalists?<\/strong><\/p>\n
TC:<\/strong>\u00a0There\u2019s a role for both, but you need to ask whether these terms lose some of their meaning. If someone says to you \u201cI specialize in being a generalist,\u201d it\u2019s not actually a crazy claim. Most people cannot be a generalist, and you have to work really hard at a bunch of particular things to be good at it. You\u2019re specializing in doing that. These are people who integrate and understand the contributions of others \u2014 that\u2019s a lot like managing. So what you call generalists \u2014 I would not oppose them to specialists \u2014 there\u2019s a big and growing role for them.<\/p>\n
WF:<\/strong>\u00a0You can\u2019t go a day without seeing a story about who should learn to code or not learn to code. The same thing with respect to statistics. Given the way you see the labor market breaking out, are there specific things you advise people go out and learn?<\/strong><\/p>\n
TC:<\/strong>\u00a0Statistics will be an increasingly big area. And even knowing a little can have a pretty high return. Coding\u2019s tricky. If you can learn it, great. But if you can\u2019t do it right, you really shouldn\u2019t bother. There\u2019s no half way.<\/p>\n
But if you\u2019re a manager or you work in health care, you might not ever be doing statistics, but if you can grasp some basic stuff that you can teach yourself, there\u2019s a very high return. And it\u2019s really quite feasible, unlike coding where it\u2019s a major undertaking. If you\u2019re a doctor trying to figure out which parts of the hospital are bringing in the money and someone hands you statistics \u2014 if you\u2019re helpless, that\u2019s really bad.<\/p>\n
WF:<\/strong>\u00a0One area that strikes me as one of the more difficult for machine intelligence is strategy. Where to position your business in a marketplace seems like on the far end of what machines can tell us.<\/strong><\/p>\n
TC:<\/strong>\u00a0Yeah, that\u2019s all humans, though you might consult machines for background information. But in no sense are machines close to being able to do that. That\u2019s a very long way away.<\/p>\n
All of our sectors are all on different paths, and the differential timing actually will be useful because we won\u2019t have to figure it out all at once. We\u2019ll get lessons from different areas sequentially and adjust. Humans will switch into the sectors they\u2019re still good at in a rolling way. And that will make this socially more stable and better for most people.<\/p>\n
If you woke up one morning and the machines were better than you at everything, that would be pretty disconcerting. That\u2019s the science fiction scenario but it\u2019s not that realistic.<\/p>\n<\/div>\n
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Totally agree with this assessment… you do have to work really hard to be a generalist. Am relieved to hear I have a future… If someone says to you \u201cI specialize in being a generalist,\u201d it\u2019s not actually a crazy claim. Most people cannot be a generalist, and you have to work really hard at … Continue reading Specilising in being a generalist<\/span>