By Hugh CameronShareNewsweek is a Trust Project memberAI is already sweeping the U.S. workforce, resulting in thousands of job cuts as the technology allows firms to operate with leaner headcounts, and studies find that its capacity to automate human tasks could put millions of roles on the chopping block.
In a recent study from the Massachusetts Institute of Technology, researchers found that AI would already be able to replace 12 percent of the U.S. workforce, together representing around $1.2 trillion in wages across sectors, including tech, finance and health care.
Why It Matters
The researchers note that over 100,000 job losses have been linked to AI restructuring already in 2025, and projections regarding the technology’s long-term impacts suggest the figures could surpass the nine-digit mark over the next decade.
What To Know
For their study, the researchers employed a simulation tool called the Iceberg Index, developed alongside the Oak Ridge National Laboratory, which analyzes the interactions of 151 million employees across 923 occupations in 3,000 counties to evaluate which of their tasks current AI tech would be able to perform.
Combining this Iceberg Index with a “Surface Index”—modelling the dominance of exposed sectors such as tech and computing—and current levels of visible adoption, the study identified the states most vulnerable to potential AI replacement—based on their “automation exposure concentration.”
“This approach reveals clear regional patterns,” the study reads. “States in the Northeast Corridor tend to show tightly clustered exposure, often concentrated in finance and technology.
“In contrast, states in the Manufacturing Belt exhibit more distributed profiles, with risk spread across production, logistics, administration, and related services.”
Washington and Virginia were among the highest-ranked due to the dominance of exposed sectors like finance and technology. While California’s strong tech sector increases its vulnerabilities, it is brought to the same level as Utah and a tier below Delaware, whose “concentrated finance and administrative sectors present sharper automation targets than California’s diversified workforce."
States like Mississippi and Wyoming showed lower vulnerability due to “their limited technology sector presence and fewer occupations with skill profiles matching current AI adoption patterns.”
Some states, like Texas and North Carolina, were classified as more highly exposed despite current usage levels, which researchers said reflects “workforce structures with high technical vulnerability regardless of current adoption levels.”
However, the researchers noted that their projections regarding AI exposure did not equate to “displacement outcomes,” and said that the actual effects on employment will hinge on how workers, employers and policymakers adapt.
“Real-world impacts depend on adoption choices, firm strategies, worker adaptation, societal acceptance and policy interventions,” they wrote. “The [Iceberg] Index functions as a capability map enabling scenario-based planning rather than deterministic forecasting.”
What People Are Saying
Researchers, in the conclusion to the study, wrote: “The Iceberg Index provides measurable intelligence for critical workforce decisions: where to invest in training, which skills to prioritize, how to balance infrastructure with human capital. It reveals not only visible disruption in technology sectors but the larger transformation beneath the surface. By measuring exposure before adoption reshapes work, the Index enables states to prepare rather than react—turning AI into a navigable transition.”
What Happens Next
Estimates regarding the labor market impact of AI adoption continue to vary widely. Some suggest that the effect will be qualitative—changing roles rather than eliminating them en masse—while others offer more concerning forecasts. Management consulting firm McKinsey, for example, estimates that AI could replace around 40 percent of U.S. professions, having previously predicted that as many as 30 percent of jobs could be automated by 2030.
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