In the battle with robots, human workers are winning

Why do I still have a job?

It’s a question readers ask me often, but I mean it more universally: Why do so many of us still have jobs?

It’s 2022, and computers keep stunning us with their achievements. Artificial intelligence systems are writing, drawing, creating videos, diagnosing diseases, dreaming up new molecules for medicine and doing much else to make their parents very proud. Yet somehow we sacks of meat — though prone to exhaustion, distraction, injury and sometimes spectacular error — remain in high demand. How did this happen? Weren’t humans supposed to have been replaced by now — or at least severely undermined by the indefatigable go-getter robots who were said to be gunning for our jobs?

I’ve been thinking about this a lot recently. In part it’s because I was among the worriers — I started warning about the coming robotic threat to human employment in 2011. As the decade progressed and artificial intelligence systems began to surpass even their inventors’ expectations, evidence for the danger seemed to pile up. In 2013, a study by an Oxford economist and an AI scientist estimated that 47 per cent of jobs are “at risk” of being replaced by computers. In 2017, the McKinsey Global Institute estimated that automation could displace hundreds of millions of workers by 2030, and global economic leaders were discussing what to do about the “robocalypse”. In the 2020 campaign, AI’s threat to employment became a topic of presidential debates.

Even then, predictions of robot dominance were not quite panning out, but the pandemic and its aftermath ought to radically shift our thinking. Now, as central bankers around the world are rushing to cool labour markets and tame inflation — a lot of policymakers are hoping that this week’s employment report shows — declining demand for new workers — a few economic and technological truths have become evident.

First, humans have been underestimated. It turns out that we (well, many of us) are really amazing at what we do, and for the foreseeable future we are likely to prove indispensable across a range of industries, especially column-writing. Computers, meanwhile, have been overestimated. Though machines can look indomitable in demonstrations, in the real world AI has turned out to be a poorer replacement for humans than its boosters have prophesied.

What’s more, the entire project of pitting AI against people is beginning to look pretty silly, because the likeliest outcome is what has pretty much always happened when humans acquire new technologies — the technology augments our capabilities rather than replaces us. Is “this time different”, as many Cassandras took to warning over the past few years? It’s looking like not. Decades from now I suspect we’ll have seen that artificial intelligence and people are like peanut butter and jelly: better together.

It was a recent paper by Michael Handel, a sociologist at the Bureau of Labor Statistics, that helped me clarify the picture. Handel has been studying the relationship between technology and jobs for decades, and he’s been skeptical of the claim that technology is advancing faster than human workers can adapt to the changes. In the recent analysis, he examined long-term employment trends across more than two dozen job categories that technologists have warned were particularly vulnerable to automation. Among these were financial advisers, translators, lawyers, doctors, fast-food workers, retail workers, truck drivers, journalists and, poetically, computer programmers.

His upshot: Humans are pretty handily winning the job market. Job categories that a few years ago were said to be doomed by AI are doing just fine. The data show “little support” for “the idea of a general acceleration of job loss or a structural break with trends predating the AI revolution”, Handel writes.

Consider radiologists, high-paid medical doctors who undergo years of specialty training to diagnose diseases through imaging procedures like X-rays and MRIs. As a matter of technology, what radiologists do looks highly susceptible to automation. Machine learning systems have made computers very good at this sort of task; if you feed a computer enough chest X-rays showing diseases, for instance, it can learn to diagnose those conditions — often faster and with accuracy rivaling or exceeding that of human doctors.

Such developments once provoked alarm in the field. In 2016, an article in The Journal of the American College of Radiology warned that machine learning “could end radiology as a thriving speciality”. The same year, Geoffrey Hinton, one of the originators of machine learning, said that “people should stop training radiologists now” because it was “completely obvious that within five years deep learning is going to be better than radiologists”.

This article originally appeared in The New York Times