Who\u2019s more at risk of being replaced by automation: a crossing guard or an air traffic controller? Both prevent collisions, but only one requires years of specialized training. An air traffic controller could do a crossing guard\u2019s job \u2013 but not the other way around. And while the guard\u2019s work is untouched by automation, the controller\u2019s tools are increasingly automated to reduce human error.<\/p>\n
The answer, says MIT economist David Autor<\/a>, is nuanced. In a talk hosted by the Stanford Digital Economy Lab<\/a> at the Stanford Institute for Human-Centered AI<\/a>, Autor challenged the assumption that automation exposure simply means job loss. \u201cExposure is not a very useful term,\u201d Autor said. \u201cIs it the case that if you\u2019re exposed, you\u2019re hosed?\u201d\u00a0<\/p>\n Not necessarily, he said. He pointed to Uber: Wages for taxi drivers stagnated, but employment rose 249% from 2000 to 2020 as automation lowered the barrier to entry. In contrast, proofreaders saw wages rise but job numbers decline as automation removed simpler tasks while adding expert tasks that made the role more specialized.\u00a0<\/p>\n \u201cProofreading used to mean spell-checking. Now it\u2019s about helping people write,\u201d Autor said.<\/p>\n In short, Autor found that automation both replaces and augments expertise \u2013 it depends on whether rote tasks are removed and expert ones added, and how specialized a role becomes as a result.<\/p>\n An Objective Model to Track Automation\u2019s Impact<\/p>\n To evaluate the impact of automation on occupations, Autor tracked the addition and removal of tasks in job descriptions from 1977 to 2018, alongside shifts in wages and employment. To determine what constituted an expert versus an inexpert job, Autor drew from Zipf\u2019s law and the efficient coding hypothesis. These concepts explain how language evolves to include common shortcut words in order to streamline communication. Initially, these jargon words are understood by a select few experts, but they enter the common vernacular over time.\u00a0<\/p>\n \u201cI will be able to say \u2018LLM\u2019 or \u2018GPT\u2019 or whatever, and you\u2019ll all know what I mean,\u201d Autor said. \u201cYou wouldn\u2019t have known what I meant five years ago, but you know what I mean now.\u201d<\/p>\n Autor used this idea to distinguish between common \u2013 or nonexpert \u2013 words and less frequent \u2013 or expert \u2013 words as he evaluated job descriptions between 1977 and 2018.<\/p>\n Impact of Exposure Depends on Expert Supply<\/p>\n Autor found that for jobs that gained inexpert tasks but lost expertise, wages declined as technology made it possible for more people to do more of the tasks required \u2013 like in the Uber scenario. Knowing the most highly trafficked places to pick up passengers and the right routes to get them to their destinations were no longer expert tasks \u2013 more people could do the job. Taxi driving became less specialized.<\/p>\n Autor\u2019s model found that the opposite trends occurred \u2013 employment went down and wages rose \u2013 for jobs that lost inexpert tasks but gained expert tasks that upgraded their expertise levels. In the proofreader example, inexpert tasks like \u201cplaces proof and copy side by side on reading board\u201d went away, but \u201cconsult reference books to check references with rules of grammar and composition\u201d were added.<\/p>\n \u201cExpertise is much closer to a supply change,\u201d he added. \u201cWhen expertise falls, it\u2019s a reduction in barriers. When expertise requirements rise, it\u2019s an increase in barriers.\u201d<\/p>\n Predicting a Job\u2019s Future by Assessing Routine Tasks<\/p>\n Autor used a large language model to classify tasks into three categories: abstract tasks that require creativity, reasoning, and interpersonal skills; routine tasks that follow clear, repetitive rules; and manual tasks that involve physical effort and common sense but little formal training. He found that 64.5% of removed tasks in his data set were routine, while 75.6% of added tasks were abstract. In other words, jobs with many routine tasks in 1977 had far fewer routine tasks by 2018.<\/p>\n What does that imply for a job\u2019s expertise level? In some occupations, losing routine tasks led to lower wages. In others, it increased specialization and pay.<\/p>\n \u201cFor some things you\u2019re taking away the supporting activities. You\u2019re allowing people to specialize and focus on their comparative advantage,\u201d Autor said. \u201cFor other sets of occupations, you\u2019re taking away their primary activity, and so you\u2019re removing what\u2019s special about that occupation and reducing it down to the generic activities that many more people could do,\u201d he said.<\/p>\n Autor noted that these results show the \u201cexposure paradox\u201d in action. \u201cThese are exposed occupations, but the exposure has completely different meanings for how that work is going to change,\u201d he said.<\/p>\n","protected":false},"excerpt":{"rendered":"Who\u2019s more at risk of being replaced by automation: a crossing guard or an air traffic controller? 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