MAY 31, 2017
The employment landscape of the future will look very different than it does today.
In plain black and white, it shows the jobs that exist today in contrast to the jobs that are expected to disappear as a result of automation in the workplace. Though, technically speaking, it is applying the probabilities of the widely-cited Frey & Osborne (2013) study to U.S. jobs as of 2016 to give an expected value to each job title.
In the near-future, many of today’s most common jobs may be changed profoundly. People working as retail salespersons, cashiers, fast food counter workers, and truck drivers will likely see opportunities in those fields dry up as automation takes place.
At the same time, jobs such as those in teaching and nursing are expected to stand the test of time, as they require empathy, creativity, and a human touch not yet available through machines. In the coming decades, it’s possible that these could even be professions that employ the most people overall.
In the vastly different employment landscape of the future, the worry is that low income workers will have fewer opportunities available to them as technology comes into play.
The good news? Historically this has not been true. As an example, nearly 500 years ago, Queen Elizabeth I had a similar fear when she denied a patent for an automated knitting machine. The thought was that the machine would kill jobs, though eventually factories and companies adopted similar technologies anyways. With the lower prices, higher demand for knitted goods, and more capital for investment, jobs for factory weavers actually quadrupled in the coming years.
As we’ve seen over time, while machines destroy jobs, they also often create new ones.
Composition of U.S. Job Market over the Last 150+ Years
The bad news? It is now clear that agricultural jobs of the early 20th century were replaced with the white collar jobs of today. However, it is much more difficult to forecast out how some of the jobs of the future will be created, especially for low income workers.
The knitting example above certainly applies in some situations – but in others, it’s hard to say what will happen. For example, with millions of unemployed long-haul truck drivers, what roles will these people be taking in the future job market?
Even with costs of transportation and logistics going down, increased demand, and more capital to invest, it seems that there’s going to be a lengthy period of time where many of these people will have trouble finding work.
Do they join the company to help manage the many more trucks that are self-driving? It’s unlikely, and that is the part of the optimism about automation and future jobs that is the hardest to reconcile.