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 challenge of the AGI era. Already, that question is becoming urgent. BlackRock CEO Larry Fink warned in his closely watched annual letter that AI “threatens to repeat that pattern at an even larger scale—concentrating wealth among the companies and investors positioned to capture it,” noting that the top 1% of U.S. households now hold more wealth than the bottom 90% and that AI is likely to exacerbate this gap.

Restrepo notes that in such an economy, “one approach is to redistribute these gains through universal income. Another is to treat compute as a public resource—akin to land or natural capital—and distribute its returns broadly.”

Two modes of automation

The paper also makes important distinctions about the path to that future, and not all of them are comforting for workers navigating the transition today. Restrepo identifies two modes of automation. In a “compute-binding” transition, AI adoption is constrained by available hardware; adjustment is gradual, wages follow continuous paths, and workers have time to reallocate.

In an “algorithm-binding” transition—the one that looks more like the current moment, with AI capabilities advancing in sudden leaps—the picture is jagged and destabilizing. “Inequality may rise sharply: workers whose tasks cannot yet be automated enjoy large temporary wage premiums, while others face sudden wage declines as theirs are,” he writes.

This bears a strong resemblance to what’s happening in the trades as of 2026, with electricians, plumbers and HVAC technicians commanding strong premiums, especially on data-center construction. Construction workers on data center projects currently earn an average of about $81,800 annually—roughly 32% more than those on non-data center builds—according to data from Skillit, an AI-powered hiring platform.

Some electricians are pulling in $260,000 a year, with electrical work accounting for an estimated 45% to 70% of total data center construction costs. The U.S. will need roughly 300,000 new electricians over the next decade, in addition to replacing the 200,000 expected to retire.

We won’t be poorer—but we may not be richer either

Restrepo does offer one piece of meaningful reassurance: workers as a group are not made worse off by the transition. Because AGI expands what the economy can produce, total labor income in the post-AGI world—across all workers—is higher than in the pre-AGI baseline.

The arrival of AI cannot make us collectively poorer, the paper argues, because we could always retreat to a no-AI zone and produce exactly as we did before. The fact that we don’t means the new arrangement is better in aggregate. “The arrival of AGI cannot make us collectively worse off,” Restrepo writes.

But that collective gain is cold comfort if it is concentrated at the top of the income distribution—among the companies, investors, and nations that own the data centers.

Indeed, 40% of Americans currently lack meaningful exposure to capital markets, according to Fink. And without structural intervention—he suggests tools like tokenization and expanded retirement investment options—the AI-driven boom will leave them further behind.

‘We Won’t Be Missed’

The paper’s title, borrowed from its closing argument, captures the existential wager of the AGI economy. “Historically, work provided not only income but also recognition that one’s efforts improved society’s well-being,” Restrepo writes. “Work gave people the sense that they would be missed. In an AGI world, that connection is severed.”

Today, he notes, if half the workforce stopped showing up, the economy would collapse. In the AGI world, we would not be missed.

For Restrepo—whose work with Nobel laureate Daron Acemoglu has shaped the economics profession’s understanding of automation for more than a decade—the message is not one of despair, but of clear-eyed reckoning. The question is not whether AI will take your job. It may be that your job was never important enough for the question to matter.

For this story, Fortune journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.