Are You a Sprinter, Marathoner or a Middle-Distance Runner?

David Brooks’ What machines can’t do is a delicious statement about the future of work. The subtexts, that stuff not really stated, are loaded with caveats, half-truths, yes and no, probably, and maybe so. Still, if you understand the need to pay close attention to your career future, the article is an inflection point. In other words, it’s a highly ***valuable career reference.

Technology, Brooks writes, seems to reward a few traits: enthusiastic curiosity, strategic discipline, process architects, decentralized networks and creative “essentialists.” I’m inevitably skeptical about trait theories in most any form, but still, Brooks has his finger on some important stuff. First, though, I want to dig in a bit deeper into the issue with insight from some really, really smart and knowledgeable economists.

Baseline: what can machines do?
The answer to this question is what you need to keep in front of your nose. Thankfully, Frank Levy (MIT) and Richard Murnane (Harvard) answered this most fundamental career question about 10 years ago. Their response remains cutting-edge.

Building on an essay by Herb Simon (Nobel Prizewinner), Levy and Murnane pointed out what machines can do...

 Computers are essentially substitutes for human information processing. If that information processing can be put into rules, then the computer can do it. So you’ll eventually lose your job--and career--if your task or competency can be simplified—and put into logical rules. A different way to expand computer substitution is to consider not the whole job, but just those tasks within the job that can be expressed in rules. A third way to expand substitution for American jobs is to use computers to support outsourcing jobs both here and in foreign countries. Since their book, The new division of labor, has been written, people have become extremely adept at creating rules and outsourcing in ways most would never have dreamed of—even a few years ago. Outsourcing “back office” stuff, for example, has become a monstrous business. My computer/software gurus take charge of my computer from Sri Lanka whenever I need help—and on a minute’s notice. Obviously, no big deal.

The warning that needs to be in place is that if you think your job or pieces of it can’t be translated into rules-based-logic, you’re in La-La Land. And that’s true with the most complex professions. My son-in-law, who’s a dermatology-pathologist with all the possible credentials thereto appertaining, regularly tells me of new rules-based-logic being applied to his high-powered profession.  

Baseline: what can’t machines do?
But despite its power, rules-based-logic suffers from two very important limitations.

. . . It can’t deal with new problems—those problems that were not anticipated by the people who wrote the rules. So it’s always playing catch-up. Levy and Murnane make the problem easy to understand. A customer brings in a newly purchased minivan with a nonfunctioning power seat. A technician uses a computerized tool to diagnose the problem. The software in the tool conducts If-Then-Do tests to search for problems that engineers have foreseen: a faulty switch, a break in the wire connecting the switch to the seat motor, a faulty seat motor, and so on. But in a new car, the many electric components can interact in ways engineers did not foresee. If the seat problem is caused by one of those interactions, the technician will be on his own. Good luck!

. . . Profoundly, it has no intuitive or tacit knowledge. Predicting the four-year-old’s walk, like a truck driver’s left turn, poses major challenges to a computer. We don’t know in advance where the girl will find obstacles or what they will look like and we don’t know when the truck driver will make a left turn. (Yeah, the auto industry has figured out how to create rules that warn us of oncoming trucks from any of 360 degrees, but still the issue of predictability is out of the picture.) Giving an inspiring speech, designing a new chair, administering anesthesia to a patient—these and many other tasks rely on tacit knowledge and pose limits on computer substitution.

Clearly, machines cannot easily substitute for humans in many jobs, but they can provide volumes of information at very low cost. And the one big truth that puts a monstrous caveat in all of this is that technology advances very, very fast. But the reassuring monstrous truth for professionals is that the more technology advances, the more it creates jobs it didn’t anticipate. Of course that also means that your success is tied to your ability to stay ahead of rules-based logic. Learning is the sine qua non!

But I love Brooks’ ability to reframe and summarize
Where Brooks really shines is in his ability to frame, reframe and summarize. So he takes our writing business and does precisely that. Technology rewards blog sprinters (the writers of blogs and Twitters about some interesting, immediate event in 300 words or less) and marathoners (those who can write large conceptual stories of 800 or more words). But it has hurt middle-distance runners (people who blog 400 to 600-word summaries of yesterday’s events). The internet has magnified the problem of short attention span.

I was initially worried about being a marathoner, especially when the blogging people recommended 300 to 400 words as a max blog. But, thank you, Google Analytics. I’ve learned that plenty of people spend the time on 1000 to 2000-word blogs if the stuff is really useful. My all-time number one blog, 7 behaviors that keep women from getting ahead, is a lengthy 1200 words and averages over 3 minutes per visitor. In contrast, a sprint blog gets 30 to 50 seconds of perusal.  

Just in case you’re curious, this blog has more than 900 words—and it’s a conceptual piece.

Flickr photo: ph0rk