A new study from the Massachusetts Institute of Technology (MIT) finds that existing AI systems can already replace 11.7% of the U.S. labor market, representing as much as $1.2 trillion in wages across finance, health care, logistics, and professional services. The findings are based on the newly developed Iceberg Index, a large-scale labor simulation platform created by MIT in collaboration with Oak Ridge National Laboratory (ORNL).

The research offers one of the clearest views yet of how current AI capabilities match real-world job tasks—providing a granular perspective that goes beyond typical forecasts or industry surveys.

The Iceberg Index simulates how 151 million U.S. workers interact across the labor market and how their roles may shift as AI tools affect tasks, skills, and regional economies. The system maps more than 32,000 skills across 923 occupations in 3,000 counties, then identifies where today’s AI models can already perform those skills.

Announced earlier this year, the Iceberg Index provides a forward-looking view of how AI may reshape the labor market across the entire country, not just in coastal technology hubs. Designed to help lawmakers prepare billion-dollar reskilling and training initiatives, the platform offers a granular map of where disruption is emerging—down to the zip-code level.

“Basically, we are creating a digital twin for the U.S. labor market,” said Prasanna Balaprakash, ORNL director and co-leader of the project. ORNL, a Department of Energy research center in eastern Tennessee, is home to the Frontier supercomputer, which powers many of the large-scale modeling efforts behind the tool.

According to Balaprakash, the index runs population-level experiments that show how AI reshapes tasks, skills, and labor flows long before those shifts appear in the real economy.

The study found that the most visible AI-related workforce changes—layoffs and job reshuffling in computing, software, and other tech-adjacent fields—represent only a small portion of total exposure. These high-profile disruptions account for just 2.2% of total wage exposure, or roughly $211 billion.

Beneath the surface, however, the index highlights much wider exposure across routine functions in human resources, office administration, finance, logistics, and other support roles. These areas often receive less attention in automation forecasts but represent the majority of the $1.2 trillion in wages potentially affected.

The researchers stress that the Iceberg Index is not designed to predict exact job losses or timelines. Instead, it provides a skill-level snapshot of what AI can do today—and a structured model for exploring “what-if” policy scenarios before states commit major investments.

Multiple state governments are already working with MIT to validate the model using their internal labor datasets. Tennessee, North Carolina, and Utah have started using the platform to test their own labor data and policy responses and have begun building statewide AI workforce action plans.

Tennessee was the first to cite the Iceberg Index in its official AI Workforce Action Plan, released this month. Utah is preparing a similar report based on Iceberg modeling. North Carolina state senator DeAndrea Salvador, who has collaborated closely with MIT on the project, said the tool’s ability to provide county-level and census-block-level detail is one of its most valuable features.

Salvador said the real value of the tool is its ability to drill down to county- and census-block-level data, showing which skills workers in a given area are performing today and how likely those skills are to be automated or augmented by AI. She noted that the model can also help states understand how those shifts might affect local GDP, employment levels, and broader economic activity.

As states form overlapping AI task forces and advisory groups, researchers say the Iceberg Index offers a unified model for testing interventions, adjusting reskilling budgets, and forecasting the downstream effects of technology adoption.

One of the study’s key findings challenges a common assumption about AI: that its effects will remain concentrated in major tech hubs. Instead, the index shows exposed occupations spread across all 50 states, including rural and inland regions that historically receive less attention in national AI policy discussions.

To address these gaps, the Iceberg Index includes an interactive simulation environment that allows states to test different policy levers—from shifting workforce dollars and training investments to modeling how robotics, automation, and AI assistants may affect local industries and GDP.

According to the report, “Project Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation.”

The research team has worked closely with Tennessee’s governor’s office and the state’s AI leadership to study impacts across key local sectors, including health care, nuclear energy, manufacturing, and transportation. Many of these industries still rely heavily on physical work, which provides some insulation from purely digital automation.

The challenge, according to Balaprakash, is determining how new tools—such as robotics and AI-driven assistants—can strengthen these industries rather than displace them.

For now, the researchers emphasize that the Iceberg Index is an evolving platform rather than a finished product. The goal is to give states and policymakers a flexible sandbox to explore potential scenarios as AI adoption accelerates.

“It is really aimed towards getting in and starting to try out different scenarios,” Salvador said.

Q: What does the MIT study claim about AI replacing workers?
A: The study finds that 11.7% of U.S. jobs—representing $1.2 trillion in wages—could already be performed by current AI systems.

Q: What is the Iceberg Index?
A: It is a large-scale labor simulation tool modeling 151 million workers, 32,000 skills, and 923 occupations across 3,000 counties, built by MIT and Oak Ridge National Laboratory.

Q: Does the index predict when jobs will be lost?
A: No. It provides a skills-based view of exposure, not a timeline for automation.

Q: Which workers face the greatest exposure?
A: Routine roles in HR, office administration, logistics, finance, and other support functions show the highest wage exposure.

Q: Which states are already using the model?
A: Tennessee, North Carolina, and Utah have validated data with MIT and are building AI workforce strategies using the platform.

The MIT study shows that AI’s impact on the workforce may be broader—and more geographically distributed—than many earlier reports suggested. While headlines often focus on layoffs in tech, the Iceberg Index suggests that the majority of exposure lies in routine functions spread across every region of the country.

For policymakers, the findings highlight why investments in reskilling, training, and AI awareness are no longer optional. Understanding exposure at the skill level—not just the job title level—is crucial for building durable workforce strategies.

As AI adoption accelerates, the states and industries that invest early in understanding these patterns will be better positioned to adapt. The Iceberg Index offers one of the most detailed tools yet for navigating these transitions and preparing the workforce for what comes next.

As AI becomes embedded in everyday work, trust and resilience will depend not only on advancing the technology, but on ensuring that workers and local economies are ready for the change.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.