The Once and Future History of Knowledge Work

This shift has ushered in what Every’s Dan Shipper calls an allocation economy, where the value of work increasingly hinges not on traditional labor but on how we allocate scarce resources—time, attention, and focus. In this new paradigm, the question becomes less about what AI can do and more about how we choose to use it, what we allow it to repl... See more

all forecast that the workers most likely to see “exposure” to generative AI are more educated and in higher-paid fields such as computer science, business and finance, legal, and management.

Option 1: The optimistic scenario

Technology continues to do what it has done for more than a century: create new jobs. Meanwhile, AI lowers the barrier to entry, more people can become knowledge workers, and knowledge work itself becomes democratized. The economic pie grows and everyone gets a bigger slice. As more people join the ranks, the exclus

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While much of the discourse around AI’s impact tends to polarize around “more jobs” or “fewer jobs,” there is a third alternative: different jobs**.** Some economists predict that, unlike industrial automation, AI will reduce the skill premium, acting as a corrective force on the disparity between wages for “skilled” and “unskilled” labor. They arg

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Technology has historically had two countervailing effects on labor—what economists call the “substituting force,” which replaces labor, and the “complementary force,” which augments it. Generative AI optimists argue the complementary force has won out through each subsequent wave of technology. A classic example is the ATM, which should have repla

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not everyone who studies AI’s impact is so bullish. With generative AI poised to automate an increasing number of business activities—as much as 70 percent , according to McKinsey—critics worry there simply won’t be enough work left over for most workers to do. For the first time in history, the argument goes, technology’s substituting force will o

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Well, the joke’s on us. As it turns out, machines can do all of those things better, faster, or at the very least cheaper than humans can. The Pew Research Center, the Brookings Institution, and McKinsey and Company all forecast that the workers most likely to see “exposure” to generative AI are more educated and in higher-paid fields such as compu

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This shift has ushered in what Every’s Dan Shipper calls an allocation economy, where the value of work increasingly hinges not on traditional labor but on how we allocate scarce resources—time, attention, and focus. In this new paradigm, the question becomes less about what AI can do and more about how we choose to use it, what we allow it to repl... See more