The claim that robots are taking our jobs has become so commonplace of late that it’s a bit of a cliché. Nonetheless, it has a strong element of truth to it. Not only are machines taking “blue collar” factory jobs—a process that’s been underway for years, and no longer much of a surprise except when a company like Foxconn announces it’s going to bring in a million robots (which are less likely to commit suicide, apparently)—but now mechanized/digital systems are quickly working their way up the employment value chain.
“Grey collar” service workers have been under pressure for awhile, especially those jobs (like travel agent) that involve pattern-matching; now jobs involving the composition of structured reports (such as basic journalism) have digital competition, and Google’s self-driving car portends a future of driverless taxicabs. But even “white collar” jobs, managerial and supervisory in particular, are being threatened—in part due to replacement, and in part due to declining necessity. After all, if the line workers have been replaced by machines, there’s little need for direct human oversight of the kind required by human workers, no? Stories of digital lawyers and surgeons simply accelerate the perception that robots really are taking over the workplace, and online education systems like the Khan Academy demonstrate how readily university-level learning can be conducted without direct human contact.
With advanced 3D printers and more adaptive robotic and computer systems on the near horizon, it’s easy to see that this trend will only continue.
Except for one arena, that is, and it’s a pretty interesting one. Jobs where empathy and “emotional intelligence” can be considered requirements, often personal service and “high touch” interactive positions, have by and large been immune to the creeping mechanization of the workplace. And here’s the twist: most of these empathy-driven jobs are performed by women.
Nursing, primary school teaching, personal grooming—these jobs require varying levels of education and knowledge, but all have a strong caretaker component, and demand the ability to understand the unspoken or non-obvious needs of patients/students/clients/etc. We’re years—perhaps even decades—away from a machine system that can effectively take on these roles; a computer able to demonstrate sufficient empathy to take care of a crying kindergartener is clearly approaching True AI status. As a result, we appear to be heading into a future where these “pink collar” jobs—empathy-driven, largely performed by women—are the most significant set of careers without any real machine substitute, and therefore without the downward wage pressure that mechanization usually produces.
This raises some big questions, of course, and not the least of which is how this will affect the social and economic status of these professions. Nurses may be more valued than surgeons; kindergarten teachers paid better than university professors. Would this lead to a shift in the gender composition of these jobs? In a culture that remains beholden to the concept that “men are the breadwinners,” might we see efforts to “masculinize” these roles? Recall that in the United States after World War II, there was a great deal of pressure on women to give up the “Rosie the Riveter”-type jobs they held during the war.
What I’m saying is this: there is a terrible habit that many of us in the futures game seem to have of generalizing potential disruptions. That is, if robots are taking our jobs, then they’re taking all of our jobs (except, ideally, for the jobs of futurists) and we start thinking through the implications from there. But disruptions aren’t so easily flattened; when Gibson said that the future’s here, it’s just not evenly distributed, he wasn’t just talking about geography, or even class. Big sociotechnoeconomic shifts don’t just appear and redraw the landscape, they have to adapt to the existing conditions, and will themselves be disrupted by deeply-rooted cultural forces. We also have a habit of expecting that the most well-off financially are the most likely to resist big changes—but what happens when the underlying notions of value themselves are changing?