The Jevons Paradox and the Future of Knowledge Work

I keep thinking about this essay by Mike Fisher about what happens when automation makes work easier. His central argument challenges the assumption that’s baked into most AI-and-jobs discourse:

In every domain where automation becomes powerful, the pattern remains consistent. Human expertise becomes more valuable because the total volume of meaningful work increases. Early fears of automation nearly always assume a fixed amount of work being redistributed. But work is not fixed. Work expands when constraints are removed.

He anchors this on the Jevons Paradox—the 19th century observation that improved steam engine efficiency led to more coal consumption, not less. And then he traces the pattern through radiology, where the number of US radiologists grew from 30,723 in 2014 to 36,024 in 2023, despite Hinton’s 2016 prediction that deep learning would make them obsolete within five years.

He concludes:

AI will reshape the profession, but only in the sense that cars reshaped transportation or spreadsheets reshaped finance. Not by eliminating the field, but by expanding its scope. Not by reducing labor, but by elevating it. Not by shrinking opportunity, but by multiplying it. The world does not need fewer people who understand systems. It needs far more of them.

I find this framing useful because it shifts the question from “will AI take my job?” to “how will the work change as the volume increases?” That’s a much more interesting thing to figure out (which is also why I have been so focused on expanding my Product Second Brain).