The prevailing anxiety surrounding artificial intelligence and employment has often suggested a scenario where machines are set to take over all jobs, leaving only the most imaginative and inherently human roles intact. However, a new study by a prominent economist in the field of automation challenges this perspective and offers a viewpoint that is both comforting and somewhat disconcerting.
Pascual Restrepo, an associate professor of economics at Yale University and a leading researcher in automation's impact on labor markets, posits in a working paper published by the National Bureau of Economic Research that widespread automation of human jobs may not occur in a world dominated by artificial general intelligence (AGI). His argument isn't centered on AI's inability to automate tasks but rather on the assessment that many current jobs may not be significant enough to warrant replacement.
In his paper, titled "We Won’t Be Missed: Work and Growth in the AGI World," Restrepo suggests that instead of automating a broad spectrum of careers, AI might focus its computational power on "bottleneck" jobs that are crucial for future advancements, such as addressing existential threats, preventing asteroid impacts, or developing fusion energy. Consequently, many roles within the labor market could remain unchanged.
Restrepo clarifies that AGI doesn't render human abilities obsolete but rather shifts their value. As computational resources become scarce, human skills will be re-evaluated on how much it costs to replicate them with technology. In a scenario where compute power and human skills are the only scarce resources, average wages may rise, but the role of human labor would diminish relative to economic growth.
His research differentiates between two categories of work in the AI-driven economy. "Bottleneck" work includes essential tasks necessary for progress—energy production, infrastructure maintenance, scientific advancement, and national security. In contrast, "supplementary" work comprises roles that the economy could forgo yet still grow, such as arts, customer service, design, and labor in hospitality. While bottleneck tasks could be automated, supplementary roles might not attract sufficient computational resources for full automation, leading to their preservation.
This distinction provides some solace for those in creative and interpersonal professions, who may find their jobs relatively safe—not due to any unique human qualities, but simply because the cost of automating these areas would be prohibitively high compared to more pressing challenges that AI needs to address.
However, Restrepo adds a more sobering note: surviving automation doesn’t guarantee benefiting from economic growth. In an AGI-dominant future, he predicts a decoupling of wages from GDP growth. Currently, when the economy expands, workers usually see a corresponding increase in wages. This link may disappear when AI manages the essential tasks needed for economic advancement, leading to the replacement of human contributions with computable costs.
One of Restrepo's most striking conclusions is that labor’s proportion of GDP could potentially approach zero. The total computational capacity in the economy could vastly exceed that of human brains, which means that human labor would become increasingly marginal in comparison to computational gains. Consequently, wealth generation would likely concentrate with those who own computational resources rather than workers themselves.
This perspective presents significant implications regarding the ownership of AI technology. Concerns have already surfaced regarding wealth inequalities exacerbated by AI, with figures like BlackRock CEO Larry Fink noting that AI could replicate patterns of wealth concentration even more pronounced than those seen today, where a tiny fraction of Americans holds a vastly disproportionate share of wealth.
Restrepo suggests that addressing this impending disparity may involve strategies like universal basic income or treating computational resources as public assets that can be equitably distributed.
The discussion around automation transitions further complicates the picture. Restrepo outlines two distinct paths: a "compute-binding" scenario, where AI integration is gradual and provides time for workers to adapt, and an "algorithm-binding" phase, characterized by rapid advancements that could lead to abrupt wage variations and heightened inequality within the workforce.
As evidenced by current trends in skilled trades in 2026, certain professions are witnessing significant wage premiums as demand surges, notably in data center constructions, highlighting how these dynamics are unfolding already.
Restrepo reassures that the collective economic position of workers will not necessarily decline due to AGI, given that the economy's output can surpass current levels, but warns that gains may not be distributed equally. The reality is that while total labor income could rise, if the benefits are concentrated among a wealthy few, many may remain economically disadvantaged.
The paper's title encapsulates the existential stakes of an AGI-dominated society: "We Won’t Be Missed." It implies a future where the intrinsic value of work—once tied to societal contribution and acknowledgment—is diminished, potentially severing the connection between effort and societal regard.
Restrepo’s insights underscore that the crucial issue is not merely the automation of jobs but the relevance of those jobs in the face of increasingly advanced AI systems. It may not be a question of whether AI will replace your job, but rather whether that job ever held substantial significance in the broader economy.
In summary, while AI might catapult productivity to new heights, the resulting economic landscape will require consideration of ownership and equitable distribution, as the growth from AI advances may not trickle down to the workforce as it once did.