In the legal drama “Suits,” recently revived on Netflix, the character Louis Litt seethes with resentment as he grinds away at his cutthroat corporate law firm. Though he puts in more hours and brings in bigger clients, his slicker rival gets promoted ahead of him.
For the Louis Litts of the world, artificial intelligence is going to be one bitter pill. The poison? Uplevelling. That’s the term to describe the great equalizing force that AI adoption brings to the workplace. It goes like this: When AI tools are introduced into the workflow, the worst performers get the biggest boost in productivity. Those who were already at the top of the pyramid also get a boost, but much less of one.
In a recently published paper, Ethan Mollick, an associate professor at The Wharton School at The University of Pennsylvania, along with several colleagues, monitored how employees at the Boston Consulting Group performed when using AI to complete their work. Those who had previously performed the worst at their jobs saw their productivity jump 43% with AI. The top performers, meanwhile, increased output by only 17%. In the end, everyone is performing within a much tighter band.
At first glance, this means a workplace suddenly operating at Red Bull-level productivity. The C-suite is thrilled. Down the hall at HR, however, it’s a nightmare. The entire structure of the office – from seniority to compensation and recruiting – are about to be upended. It’s not clear how it will ever be neatly put back together again.
“I do not think enough people are considering what it means when a technology raises all workers to the top tiers of performance,” writes Mollick.
One thing it means: The top performers who stayed up late to prepare and showed up early to perform now see a diminishing return on their effort. The lazy, disheveled employees who never seemed to catch on are now performing at almost the same level.
The premium placed on effort will begin to disappear. Your worst performers are feeling energized. Your best performers are demoralized. Louis Litt is drowning in his own bile.
The disruption goes beyond office politics. It infects everything from training to pay. Imagine the certified paralegal with years of experience under her belt peering over her cubicle at the newbie with no qualifications doing the same work.
The Thomson Reuters’ “The Future of Professionals Report” examines how this will overturn the current system of credentials that both workers and employers currently depend upon. In this scenario, a Juris Doctor or Certified Public Accountant, two degrees which require years of preparation and investment, no longer command the same premium.
“As automation and AI solutions make completing traditional legal tasks easier, it could become more appropriate for such tasks to be completed by a paralegal or more junior professional,” the report concluded.
One person surveyed by Thomson Reuters put it more bluntly: “The average tax firm has little-to-no use for a CPA compared to an EA,” or “enrolled agent,” a significantly more junior credential.
The downstream effects flow straight to universities and community colleges. In recent years these places have added numerous master’s degrees and certificate programs designed to help workers meet all sorts of qualifications which, in a few years, may be irrelevant.
AI is not the first innovation to turn skill into a commodity. We’ve seen the assembly line, the steam shovel, the CPU. Each of those brought with it brutal dislocations of labor followed, in due course, by greater prosperity.
The pace of AI adoption is happening more quickly and impacting more sectors than any of those previous shits in labor. In order to mitigate the inevitable pain before the eventual reward, all these structures – from compensation to education and beyond – must learn how to bend so they don’t break.
Gabriel Kahn is a professor of professional practice at the USC Annenberg School of Journalism, where he co-director of the Media, Economics and Entrepreneurship program. In 2018, he launched Crosstown, a project that uses data to generate local news. Before joining USC, Khan was an editor and foreign correspondent for The Wall Street Journal.