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But then the knowledge sector emerged as a major force in the mid-twentieth century, and this profitable dependence on crisp, quantitative, formal notions of productivity all but vanished. There was, as it turns out, a good reason for this abandonment: the old notions of productivity that worked so well in farming and manufacturing didn't seem to apply to this new style of cognitive work. One problem is the variability of effort. When the infamous efficiency consultant Frederick Winslow Taylor was hired to improve productivity at Bethlehem Steel in the early twentieth century, he could assume that each worker at the foundry was responsible for a single, clear task, like shoveling slag iron. This made it possible for him to precisely measure their output per unit of time and seek ways to improve this metric. In this particular example, Taylor ended up designing a better shovel for the foundry workers that carefully balanced the desire to move more iron per scoop while also avoiding unproductive overexertion. (In case you're wondering, he determined the optimal shovel load was twenty-one pounds.)

In knowledge work, by contrast, individuals are often wrangling complicated and constantly shifting workloads. You might be working on a client report at the same time that you're gathering testimonials for the company website and organizing an office party, all the while updating a conflict of interest statement that human resources just emailed you about. In this setting, there's no clear single output to track. And even if you do wade through this swamp of activity to identify the work that matters most—recall Davenport's example of counting a professor's academic publications-there's no easy way to control for the impact of unrelated obligations on each individual's ability to produce. I might have published more academic papers than you last year, but this might have been, in part, due to a time-consuming but important committee that you chaired. In this scenario, am I really a more productive employee?

A Henry Ford-style approach of improving systems instead of individuals also struggled to take hold in the knowledge work context. Manufacturing processes are precisely defined. At every stage of his development of the assembly line, Ford could detail exactly how Model Ts were produced in his factory. In the knowledge sector, by contrast, decisions about organizing and executing work are largely left up to individuals to figure out on their own. Companies might standardize the software that their employees use, but systems for assigning, managing, organizing, collaborating on, and ultimately executing tasks are typically left up to each individual. "The knowledge worker cannot be supervised closely or in detail," argued Peter Drucker in his influential 1967 book, The Effective Executive. "He can only be helped. But he must direct himself."

Knowledge work organizations took this recommendation seriously. The carefully engineered systems of factories were replaced with the "personal productivity" of offices, in which individuals deploy their own ad hoc and often ill-defined collection of tools and hacks to make sense of their jobs, with no one really knowing how anyone else is managing their work. In such a haphazard setting, there's no system to easily improve, no knowledge equivalent of the ten times productivity boost attributed to the assembly line. Drucker himself eventually grew to recognize the difficulties of pursuing productivity amid so much autonomy. "I think he did believe it was hard to improve...we let the inmates run the asylum, let them do the work as they wish," Tom Davenport told me, recalling conversations he had with Drucker in the 1990s.

These realities created a real problem for the emergent knowledge sector. Without concrete productivity metrics to measure and well-defined processes to improve, companies weren't clear how they should manage their employees. And as freelancers and small entrepreneurs in the sector became more prevalent, these individuals, responsible only for themselves, weren't sure how they should manage themselves. It was from this uncertainty that a simple alternative emerged: using visible activity as a crude proxy for actual productivity. If you can see me in my office—or, if I'm remote, see my email replies and chat messages arriving regularly—then, at the very least, you know I'm doing something. The more activity you see, the more you can assume that I'm contributing to the organization's bottom line. Similarly, the busier I am as a freelancer or entrepreneur, the more I can be assured I'm doing all I can to get after it.

As the twentieth century progressed, this visible-activity heuristic became the dominant way we began thinking about productivity in knowledge work. It's why we gather in office buildings using the same forty-hour workweeks originally developed for limiting the physical fatigue of factory labor, and why we feel guilty about ignoring our inboxes, or experience internalized pressure to volunteer or "perform busyness" when we see the boss is nearby. In the absence of more sophisticated measures of effectiveness, we also gravitate away from deeper efforts toward shallower, more concrete tasks that can be more easily checked off a to-do list. Long work sessions that don't immediately produce obvious contrails of effort become a source of anxiety—it's safer to chime in on email threads and "jump on" calls than to put your head down and create a bold new strategy. In her response to my reader survey, a social worker who identified herself only as N described the necessity of "not taking breaks, rushing, and hurrying all day," while a project manager named Doug explained that doing his job well reduced to "churning out lots of artifacts," whether they really mattered or not.