Carl Benedict Frey and Michael A. Osborne performed an analysis on 702 current occupations, and determined that almost half (47%) of jobs analyzed are at high risk of being ephemeralized out of existence (see The Future Of Employment: How susceptible Are Jobs To Computerization [pdf]).
The authors stated by ascribing their study to John Maynard Keynes’ 1933 prediction of future unemployment due to the growth of technology. As he put it, this occurs
due to our discovery of means of economising the use of labour outrunning the pace at which we can ﬁnd new uses for labour.
Since the ’30s we have seen the proof of his prediction in the disappearance of telephone operators (telephone switch), cashiers (ATMs), elevator operators (automated elevators), and dozens of other jobs. The leveling off of employment since the early part of this century could be the result of us reaching a new pace of technological expansion, which is running ahead of Keyne’s second curve, “finding new uses for labour,” by which he meant people.
As the authors show, the routine work that formerly paid middle income workers has “hollowed out”:
with falling prices of computing, problem-solving skills are becoming relatively productive, explaining the substantial employment growth in occupations involving cognitive tasks where skilled labour has a comparative advantage, as well as the persistent increase in returns to education (Katz and Murphy, 1992; Acemoglu, 2002; Autor and Dorn, 2013). The title “Lousy and Lovely Jobs”, of recent work by Goos and Manning (2007), thus captures the essence of the current trend towards labour market polarization, with growing employment in high-income cognitive jobs and low-income manual occupations, accompanied by a hollowing-out of middle-income routine jobs.
So, the trend is clear: low-cost computing, coupled to modern capabilities for converting routine work into algorithms, spells the end of a long list of jobs.
The authors used a novel technique to analyze jobs based on machine learning, data mining, machine vision, computation statistics, and other parts of artificial intelligence. In particular, they examined the state of the art in mobile robotics. They relied on the task characterization of Autor, et al, which distinguishes between cognitive and manual tasks on the one hand, and routine and non-routine tasks on the other. I will leave aside the mathematics used — Gaussian probability based on hand analyzing 70 jobs, but the starting point of their analysis was this question:
Can the tasks of this job be sufficiently specified, conditional on the availability of big data, to be performed by state of the art computer-controlled equipment?
And the factors involved to answer that question are objective O*Net variables of perception and manipulation, creative intelligence, and social intelligence.
And the outcome of their analysis:
The low risk occupations are those requiring the greatest degree of understanding of other people and creativity, like art, education, engineering, and healthcare. But the broad middle is already squished to nothingness (this is 2010 baseline data for the populations involved), and in the high risk occupations to the right 400 million jobs are 100% likely to be computerized. As the authors note,
According to our estimate, 47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two. It shall be noted that the probability axis can be seen as a rough timeline, where high probability occupations are likely to be substituted by computer capital relatively soon. Over the next decades, the extent of computerisation will be determined by the pace at which the above described engineering bottlenecks to automation can be overcome. Seen from this perspective, our findings could be interpreted as two waves of computerisation, separated by a “technological plateau”. In the first wave, we find that most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are likely to be substituted by computer capital.
The authors don’t say it, but “truck driver” is the number one occupation for men in the US, and “secretary” or some euphemism for it, is still the number one job in the US for women.
The Bottom Line
The findings resulting from this research line up fairly well with what we have seen in recent years. It begs a number of serious questions regarding transitions in the business of the near future, and the wrenching changes that these technologies will have in business and society. The authors end with this,
Our findings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence. For workers to win the race, however, they will have to acquire creative and social skills.
And it is unclear where they will gain those skills, and whether there will be enough jobs to go around even if people displaced by computerization do in fact learn them.