MIT research finds: Artificial intelligence can already replace 11.7% of the U.S. workforce

A study released by the Massachusetts Institute of Technology (MIT) on Wednesday shows that artificial intelligence has the potential to replace 11.7% of the U.S. labor market, equivalent to a wage scale of up to $1.2 trillion in the finance, healthcare, and professional services sectors.

This study utilized a labor simulation tool called the "Iceberg Index," co-developed by MIT and the Oak Ridge National Laboratory (ORNL). ORNL is a Department of Energy research facility located in eastern Tennessee, home to the Frontier supercomputer used for large-scale modeling.

The Iceberg Index simulates the interactions among 151 million workers across the U.S. and assesses how they would be affected by artificial intelligence and related policies. The Iceberg Index, released earlier this year, provides a forward-looking perspective on how AI will reshape the U.S. labor market—not just in coastal tech hubs but across every state in the country.

For U.S. lawmakers planning billions of dollars in retraining and upskilling investments, the Iceberg Index offers a detailed map indicating which regions are experiencing potential structural shocks, with precision even down to ZIP codes.

Co-lead researcher and ORNL director Prasanna Balaprakash stated:

Essentially, we are creating a digital twin system for the U.S. labor market. This index can run population-level experiments, revealing how AI will reshape tasks, skills, and labor mobility before actual employment changes occur.

Balaprakash, a member of the Tennessee AI Advisory Council, has also shared state-level results with the governor's team and the state AI director.

He noted that core industries in Tennessee, such as healthcare, nuclear energy, manufacturing, and transportation, still heavily rely on physical labor, making them relatively less susceptible to pure digital automation. The key question is how to leverage new technologies like robots and AI assistants to enhance these industries rather than undermine them.

The Iceberg Index treats 151 million workers as independent individual agents, each carrying their own skills, tasks, occupations, and geographic information. It maps over 32,000 skills to 923 occupations and 3,000 counties, then assesses which skills can be performed by existing AI systems.

Researchers found that the visible "tip of the iceberg"—such as layoffs and job transitions in the tech, computing, and IT sectors—accounts for only 2.2% of total wage exposure (approximately $211 billion). The real hidden exposure beneath the surface amounts to as much as $1.2 trillion in wages, including everyday functions in human resources, logistics, finance, and office administration, which are often overlooked by traditional automation predictions.

Researchers emphasize that the Iceberg Index is not a tool for predicting "when and where unemployment will occur," but rather aims to present a "skills-based snapshot" showing which skills today’s AI systems are already capable of performing, thereby helping policymakers explore various "what-if scenarios" in a structured manner before investing funds and legislating The research team collaborated with state governments in the United States to conduct simulations in advance. Tennessee, North Carolina, and Utah used their state-level labor data to validate the model and began to formulate policy scenarios based on the platform. Tennessee took the lead, citing the Iceberg Index in the official "AI Workforce Action Plan" released this month. Utah is preparing to release a similar report based on iceberg modeling.

North Carolina Senator DeAndrea Salvador, who works closely with MIT, stated that what attracted her was the research's ability to reveal impacts that traditional tools cannot capture. One of the most valuable features is the ability to drill down into regional-level data:

You can see specific data at the county level or even census block level, understand what skills are currently in demand, and then match those skills with their likelihood of being automated or augmented, assessing their impact on local GDP and employment changes. This type of simulation is particularly important in the context of states establishing AI task forces and research groups.

The Iceberg Index also challenges a common assumption—that AI risks will be concentrated primarily in technical positions in coastal tech hubs. Simulations show that occupations exposed to AI are spread across all 50 states, including inland and rural areas that are often excluded from AI discussions.

To bridge this gap, the Iceberg team created an interactive simulation environment that allows states to experiment with different policy levers—from adjusting labor budgets and optimizing training programs to exploring how changes in technology adoption will affect local employment and GDP.

The report states: "The Iceberg Initiative helps policymakers and business leaders identify risk hotspots, prioritize training and infrastructure investments, and test interventions before committing billions of dollars to implementation."

Currently, the team positions the Iceberg Index as a "sandbox" for states to prepare in advance for the impact of AI on the workforce. The goal is to enable everyone to start hands-on experimentation with different scenario options.

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