A Yale economist says AGI won’t automate most jobs—because they’re not worth the trouble

The conventional fear about artificial intelligence and jobs runs something like this: the robots are coming for everything, and only the most creative, deeply human work will survive. A new paper by one of the world’s leading economists of automation turns that assumption on its head—and in doing so, arrives at a conclusion that is simultaneously more reassuring and more unsettling than the standard nightmare scenario.

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Pascual Restrepo, an associate professor of economics at Yale University and one of the field’s foremost researchers on automation and labor markets, argues in a working paper published by the National Bureau of Economic Research that most human work won’t be automated in an era of artificial general intelligence. The reason isn’t that AI lacks the capability. It’s that most of what people do for a living simply isn’t important enough to bother replacing.

“The model opens up the intriguing possibility that much of today’s work may not be essential for future growth and may never be automated,” Restrepo writes in the paper, titled We Won’t Be Missed: Work and Growth in the AGI World. “Instead, compute may be directed toward bottleneck work critical for future progress—such as reducing existential risks, defending against asteroids, or mastering fusion energy—leaving large parts of the labor market unchanged.”

Not obsolete—just irrelevant

The main point, he argues, is that fundamentally, “AGI does not render human skills obsolete; it revalues them.” The new scarcity in the economy isn’t skilled labor or intelligence; it’s compute. This means that skills are valued at the opportunity cost of compute required to replicate them.

“In fact, if compute and human skill are the only scarce resources, average wages are higher in a post-AGI world. On the other hand, labor’s relative role shrinks.”

His analysis extends this logic to assume that compute will go to the areas that are most valuable for economic growth, leaving jobs that are less important to be filled by humans.

Two kinds of work in the AI economy

The paper draws a sharp distinction between two types of work. “Bottleneck” work consists of tasks that are essential for economic growth—things like producing energy, maintaining infrastructure, advancing science, and national security.

“Supplementary” work, by contrast, is everything the economy can do without and still expand: arts and crafts, customer support, hospitality, design, academic research, even the work of professional economists. In Restrepo’s framework, the economy will eventually automate every bottleneck task using compute—the raw computational resources of AI systems. But supplementary work? AI may simply ignore it.

That sounds like good news for the baristas and the novelists. Jobs in hospitality, live performance, and socially intensive work could survive largely intact, Restrepo argues, not because of any special human magic, but because the massive computing resources needed to fully replicate them would never justify the expense when AI has bigger problems to solve.

Crucial bottleneck work, in Restrepo’s telling, is very science-fiction sounding: “reducing existential risks, defending against asteroids, or mastering fusion energy.” Socially intensive work, on the other hand will include hospitality, live performances and entertainment: non-essential for future growth, costly to replicate with compute, and thus likely to remain human. “These domains could continue to offer familiar and meaningful work.”

Surviving automation is not the same as sharing in growth

But here is where the paper delivers its more sobering message. Surviving automation and prospering from economic growth are two very different things.

In an AGI world, Restrepo shows, wages would become decoupled from GDP. Today, as the economy grows, workers tend to share in that growth as wages rise and living standards improve. In the post-AGI economy he models, that link breaks. Once AI systems handle all the tasks essential for growth, economic expansion is driven entirely by adding computational resources.

Human work, whether essential or supplementary, is valued not by its contribution to growth, but by what it would cost to replace it with compute. That ceiling is, in the long run, a low one.

Labor’s share of GDP goes to zero

The paper’s starkest finding is that labor’s share of GDP converges to zero. Total computational resources in the economy could eventually reach 10⁵⁴ floating-point operations per second. The computing power of all human brains combined amounts to roughly 10¹⁸ flops.

In an economy where wages are anchored to what compute would cost to replicate human work, human labor becomes economically marginal—not worthless, but negligibly small relative to the overall pie. “Most income will accrue to owners of computing resources,” the paper concludes.

That means the distribution question of who owns the compute becomes the defining political and economic challen