The Once and Future History of Knowledge Work

Artificial intelligence is changing the value of knowledge work—something that Katie Parrott, as a knowledge worker herself, has been grappling with. So it’s a fitting topic of exploration for the debut piece of her column, Working Overtime, in which she’ll examine the historical impact of technology through the lens of workers and the systems they participate in. (Longtime Every readers may recognize her byline from when she served as our managing editor.) Knowledge work is a term, Katie writes, that’s long made her somewhat uncomfortable because of its elitist connotations. But the rise of AI offers an opportunity to reevaluate both the lingo and the role that so-called knowledge workers play in society and the economy.—Kate Lee

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In 1999, management consultant Peter Drucker made a prediction that was either prophecy or hubris: Knowledge workers, he wrote in California Management Review, would be “the most valuable asset of a 21st-century institution.” A quarter of the way through the century, however, the future of knowledge work has never been more uncertain—thanks in no small part to generative AI.

The term “knowledge work” has always made me uneasy. Coining the term in 1959, Drucker defined it as “high-level” professionals who use their theoretical and analytical expertise, often gained through formal education, to create products and services. I couldn’t begin to tell you how to wire a house or repair a car. Those jobs require immense skill and vast knowledge, yet they are rarely classified as “knowledge work.” This corporate lingo carries more than a tinge of classism, social hierarchy, and condescension—as if real work happens only in front of a computer screen, pushing pixels around and complaining about the hardship of having one’s camera on during Zoom calls.

That said, as a marketer, I very much fit Drucker’s definition of a knowledge worker, regardless of how that makes me feel. And as someone who would like to pay her bills for the next 50-plus years, I have a vested interest in understanding how AI will affect knowledge work—and thus, my career.

Perhaps it wouldn’t be such a bad thing if knowledge work were to see a shakeup in the wake of AI. After all, it’s become a little big for its own britches over the past couple of decades, dominating our collective understanding of what makes a “good job.” Maybe knowledge work deserves to be taken down a few notches.

With the proliferation of new models capable of doing everything from writing code to creating images to completing legal documents, we’re approaching an inflection point in the evolution of knowledge work. So it’s worth taking stock, both of the road that got us here and the roads we could go down next. Think of this essay as a visit from the ghosts of knowledge work past, present, and future, and then ask: What might the future hold for knowledge work—and what kind of work do we want to shape the rest of the 21st century?

The past: A brief history of knowledge work

Drucker may have coined “knowledge work,” but the concept didn’t spring forth from his brain fully formed and ready to complete Jira tasks. The term may only go back a few decades, but people have been doing knowledge work for millennia. Oral storytellers, monks inking manuscripts, gentlemen scientists writing treatises on the origin of species and other such ideas—all of these represent prototypical forms of what today we recognize as knowledge work.

The fate of knowledge work and technology have long gone hand in hand. If we’re tracing knowledge work’s modern iteration, the starting point is the Industrial Revolution. The introduction of factories, complex machines, and assembly lines meant that fewer people were needed to do manual labor, and in their place arose a need for management strategies and administrative and clerical work. By the time Drucker, a longtime New York University professor and winner of the Presidential Medal of Freedom, came up with the term “knowledge work” in the late 1950s, mainframe computers had already arrived, with their ability to crunch data at many, many multiples the speed of human data analysts.

And then came the personal computer in 1974. And Microsoft Excel in 1987. And public access to the World Wide Web in 1991. Each subsequent technological innovation accelerated the rate at which work could get done and, yes, rendered some occupations obsolete or less prevalent. But it also brought with it new forms of knowledge work and new knowledge workers with specialized skills to do that work. Computer programmers, information technology and cybersecurity professionals, web designers, digital marketers—all were created in response to modern technology.

As knowledge work has grown, so has its status. Economists talk about the “skill premium”—the difference in pay between “high-skill” workers with advanced education, training, or expertise, and “low-skill” workers with fewer qualifications or specialized skills. For decades, the skill premium has increased—even as the number of knowledge workers has grown, as economist Daniel Susskind wrote in his 2020 book A World Without Work. More than 60 percent of U.S. workers are classified as “white collar” as of 2023, yet these jobs continue to command higher wages than most “low-skilled,” “blue-collar” jobs. This flies in the face of conventional wisdom about supply and demand, which tells us that a product, even labor, grows less valuable the more supply exists. 

The forces of technological change have not been as kind to other segments of the economy. The past few decades have seen a “hollowing out” of the middle class—a sort of unequal barbell effect that, yes, has pushed some earners into the economy’s penthouse, but has pushed far more into the basement.

There are factors at play here besides technology—most significantly, the offshoring of manufacturing and the U.S.’s subsequent transition into a predominantly service-based economy. But even there, technology has played a starring role, facilitating communication and productivity even when teams are oceans apart. And so the pattern stands: When new technology is introduced, knowledge work benefits while other forms of labor take a hit. The question then becomes: Will generative AI continue this pattern, or break it?