The Most Human Human (12 page)

Micromanagement and out-of-control executive compensation are odd in a way that dovetails precisely with what’s odd about our rationalist, disembodied, brain-in-a-vat ideas about ourselves. When I fight off a disease bent on my cellular destruction, when I marvelously
distribute energy and collect waste with astonishing alacrity even in my most seemingly fatigued moments, when I slip on ice and gyrate crazily but do not fall, when I unconsciously counter-steer my way into a sharp bicycle turn, taking advantage of physics I do not understand using a technique I am not even aware of using, when I somehow catch the dropped oranges before I know I’ve dropped them, when my wounds heal in my ignorance, I realize how much bigger I am than I think I am. And how much more important, nine times out of ten, those lower-level processes are to my overall well-being than the higher-level ones that tend to be the ones getting me bent out of shape or making me feel disappointed or proud.

Software development gurus Andy Hunt and Dave Thomas make the point that with a certain latitude of freedom and autonomy, a greater sense of
ownership
of a project emerges, as well as a sense of
artistry;
as they note, the stonemasons who helped build cathedrals were far from drones—they were “seriously high quality craftsmen.”

I tend to think about large projects and companies not as pyramidal/hierarchical, per se, so much as fractal. The level of decision making and artistry should be the same at every level of scale.

The corporate isn’t always a great example of this. The corporeal is, as is another kind of organization with the body in its etymology—the U.S. Marine Corps. Consider this, from their classic handbook
Warfighting:

In some sense this question of management style, of individual responsibility and agency, cuts not only across traditional divisions of “blue”- and “white”-collar work, but also between “skilled” and “unskilled” work. A formulaic mental process rigidly repeated time and again is not so different from a physical process repeated in this way. (That is to say, there’s such a thing as thinking unthinkingly.) Likewise, a complex or sophisticated or learnèd process repeated time and again is not so different from a simple process repeated. More important than either of these distinctions, arguably, is the question of how much reacting locally or site-specificity, how much freshness in approach the job requires—or permits.

In March 2010, National Public Radio’s
This American Life
did a segment on the joint General Motors and Toyota plant NUMMI. One of the biggest differences, it turned out, between the two companies was that at Toyota, “when a worker makes a suggestion that saves money, he gets a bonus of a few hundred dollars or so. Everyone’s expected to be looking for ways to improve the production process. All the time. This is the Japanese concept of
kaizen
, continuous improvement.” One American GM worker was part of a group that
traveled to Japan to try building cars on the Toyota assembly line, and the difference in the experience stunned him:

One of the results of this kind of participation is that “IQ-doubling” effect that Ferriss describes. You’re not just
doing
something; you’re doing that very human thing of simultaneously stepping back and considering the process itself. Another effect:
pride
. NUMMI’s union leader, Bruce Lee, said he’d never felt about the cars he built like he did once he was participating in the process: “Oh, I was so proud of ’em you can’t believe.”

The notion of computer therapists of course raises one of the major things that people think of when AI comes to mind: losing their jobs. Automation and mechanization have been reshaping the job market
for several centuries at this point, and whether these changes have been positive or negative is a contentious issue. One side argues that machines take human jobs away; the other side argues that increased mechanization has resulted in economic efficiency that raises the standard of living for all, and that has released humans from a number of unpleasant tasks. The corollary to the “advance” of technology seems to be that familiar human “retreat,” for better and for worse.

We call a present-day technophobe a “Luddite,” which comes from a group of British workers who from 1811 to 1812 protested the mechanization of the textile industry by sabotaging mechanical looms:
⁴
this debate has been going on—in words and deeds—for centuries. But software, and particularly AI, changes this debate profoundly—because suddenly we see the mechanization of
mental
work. As Matthew Crawford argues in the 2009 book
Shop Class as Soulcraft
, “The new frontier of capitalism lies in doing to office work what was previously done to factory work: draining it of its cognitive elements.”

I would also like to note something, though, about the
process
by which jobs once performed by humans get taken over by machines, namely, that there’s a crucial intermediate phase to that process: where
humans
do the job
mechanically
.

Note that the “blue collar and white” workers complaining about their robotic work environments in Terkel’s 1974 book
Working
are bemoaning not jobs they’ve
lost
, but the jobs they
have
.

This “draining” of the job to “robotic” behavior happens in many cases long before the technology to automate those jobs exists. Ergo, it must be due to capitalist rather than technological pressures. Once the jobs have been “mechanized” in this way, the much later process
by which those jobs actually get taken over by machines (or, soon, AIs) seems like a perfectly sensible response, and, by that point, perhaps a relief. To my mind, the troubling and tragic part of the equation is the
first
half—the reduction of a “human” job to a “mechanical” one—and less so the second. So fears over AI would seem to miss the point.

Micromanagement; the
kaizen
-less assembly line; the over-standardization of brittle procedures and protocols … these problems are precisely the same problem, and pose precisely the same danger, as does AI. In all four cases, a robot will be doing your job. The only difference is that in the first three, the robot will be you.

We’re in the midst of an interesting moment for AI chatbots, which are finally starting to show commercial promise. Just recently, the Alaska Airlines website wanted me to chat with “Jenn” instead of using their customer service telephone number (I declined), and that’s just the most recent example of many.
⁵
But before chatbots, there was, of course, the much-loathed automated telephone menu systems. And before that, human operators were behaving like chatbot automatons. Operator Heather Lamb, for instance, says in
Working
, “There are about seven or eight phrases that you use and that’s it. ‘Good morning, may I help you?’ ‘Operator, may I help you?’ ‘Good afternoon.’
‘Good evening.’ ‘What number did you want?’ ‘Would you repeat that again?’ ‘I have a collect call for you from so-and-so, will you accept the charge?’ ‘It’ll be a dollar twenty cents.’ That’s all you can say. A big thing is not to talk with a customer … I’m a communications person but I can’t communicate.”

I’ve called directory assistance a handful of times in recent years: the folks are as monosyllabic as possible, and brusque to the point of inhumanity. If my interaction with them is “human” in any way, it is only in the way that stepping on a stranger’s toes on the bus and getting scowled at is “human.” It’s not their fault, of course—they’re being forced to act like bots. In this particular case, I’ll take the robot: at least I don’t have to feel like a nuisance.

Now, if the people at directory assistance lived nearby, and could offer useful suggestions like “Oh, do you mean
Dave’s
downtown or the
David’s
up on Fifteenth?” or “But actually, if you’re looking for a good steak my personal recommendation would be …,” that would be a different story entirely. But they don’t live near where you’re calling (scaling), they aren’t given the time to engage with you (efficiency), and they can’t really deviate from the script (pure technique).

Just today, I call to activate my new credit card and end up on the phone for a good ten minutes: the woman is getting snowed on in northern Colorado, wishing for milder weather, and I’m getting rained on in Seattle, wishing for a wintrier winter. Being from the Jersey shore, I’ve grown up accustomed to snowy winters and muggy summers. Sometimes I love the Northwest’s temperateness; sometimes I miss the Northeast’s intensity. The shore, she says, wow, I’ve never seen the ocean … And on from there. My roommate, passing through the living room, assumes it’s an old friend on the line. Eventually the card is activated, and I snip the old one and wish her the best.

Maybe it’s not until we experience machines that we appreciate the human. As film critic Pauline Kael put it, “Trash has given us an appetite for art.” The inhuman has not only given us an
appetite
for the human; it’s teaching us what it
is
.

It’s clear from all of this that AI is not really the enemy. In fact, it may be that AI is what extricates us from this process—and what identifies it. Friends of mine who work in software talk about how a component of their job often involves working directly on problems while simultaneously developing automated tools to work on those problems. Are they writing themselves out of a job? No, the consensus seems to be that they move on to progressively harder, subtler, and more complex problems, problems that demand more thought and judgment. They make their jobs, in other words, more human.

Likewise, friends of mine
not
in software—in PR, marketing, you name It—are increasingly saying to me: “Can you teach me how to program? The more you talk about scripting … I’m pretty sure I can automate half my job.” In almost all cases they are right.

I think, quite earnestly, that all high school students should be taught how to program. It will give our next generation a well-deserved indignation at the repetitiveness and rule-bound-ness of some of the things they’ll be asked to do. And it will also give them the solution.

You can almost think of the rise of AI not as an infection or cancer of the job market—the disease is
efficiency
—but as a kind of maggot therapy: it consumes only those portions that are no longer human, restoring us to health.

French prose poet Francis Ponge’s
Selected Poems
begins with the following: “Astonishing that I can forget, forget so easily and for so long every time, the only principle according to which interesting works can be written, and written well.” Art cannot be scaled.

I remember my undergraduate thesis adviser, the fiction writer Brian Evenson, saying that writing books has never gotten any easier for him, because as he gets more adept at producing a certain kind of work, he grows, at an identical rate, less satisfied with repeating those old methods and practices that were so successful in the past. He won’t let his old models scale. He won’t let his job get any easier. To me that’s exhilarating, the economy-in-all-senses-of-the-word-be-damned battle cry of the artist.