The debate about what AI is doing to white-collar work has been loud, contentious, and — if you talk to Nela Richardson — almost entirely about the wrong thing.
Richardson is ADP’s chief economist, which means she sits atop one of the most complete real-time pictures of American work that exists — payroll data, job postings, and wage records, covering roughly one in six U.S. workers. She is also running what she calls “the great job unbundling,” a project launched this past January at Davos in partnership with the Stanford Digital Economy Lab and its resident AI thought leader, Erik Brynjolfsson.
“In the age of AI,” as Richardson has written, “work won’t be defined by job titles. It will be defined by what people actually do.” This is why her project seeks to measure the labor market not by job creation and destruction — the traditional economist’s scorecard — but by the creation and destruction of individual tasks within jobs. ADP has collected millions of job postings going back years and using natural language processing, Richardson’s team extracts specific work activities from the text of those postings and maps them against O*NET, the Department of Labor’s catalog of occupational tasks.
