MIT Study: AI Impact – 11.7% of US Jobs at Risk

A new study from the Massachusetts Institute of Technology (MIT) suggests that artificial intelligence (AI) is poised to impact a significant portion of the U.S. labor market, potentially displacing 11.7% of workers, representing a staggering $1.2 trillion in wages across sectors including finance, healthcare, and professional services.

The research leverages a sophisticated labor simulation tool known as the Iceberg Index, a collaborative creation of MIT and Oak Ridge National Laboratory (ORNL). This index creates a dynamic model of 151 million U.S. workers, tracking their interactions and analyzing the potential effects of AI adoption and related public policy.

The Iceberg Index offers a granular, forward-looking perspective on how AI might reshape employment landscapes nationwide. Its detailed mapping down to the zip code level provides crucial insights for lawmakers contemplating substantial investments in reskilling and training initiatives, pinpointing areas most susceptible to disruption.

“We are essentially building a digital twin of the U.S. labor market,” explains Prasanna Balaprakash, ORNL director and a lead researcher on the project. ORNL, a Department of Energy research center situated in eastern Tennessee, hosts the Frontier supercomputer, enabling large-scale simulations powering this research.

The power of the Iceberg Index lies in its ability to run population-level experiments, forecasting the evolving impacts of AI on tasks, skills, and workforce dynamics before these changes materialize in the broader economy, according to Balaprakash. This proactive approach distinguishes it from reactive analyses based on lagging economic indicators.

The index models each of the 151 million workers as an individual agent, characterized by specific skills, roles, occupations, and geographic location. It maps over 32,000 skills across 923 distinct occupations in 3,000 counties, then assesses the current capabilities of AI systems to perform those tasks. This deep data integration allows for a nuanced understanding of AI’s potential impact. Critically, it moves beyond simply identifying jobs at risk, but precisely identifies the *skills* within those roles that are most vulnerable to automation.

The findings reveal that the visible disruptions in tech, computing, and information technology sectors, such as recent layoffs, represent only 2.2% of total wage exposure, roughly $211 billion. The larger, less obvious impact lies beneath the surface, encompassing a potential $1.2 trillion in wage exposure spanning routine functions in human resources, logistics, finance, and office administration – areas often underestimated in typical automation projections. This “underwater” portion of the iceberg highlights the pervasive nature of AI’s potential impact, extending far beyond traditionally targeted sectors.

The researchers emphasize that the Iceberg Index is not a predictive model for specific job losses. Instead, it provides a skill-centric assessment of what AI can currently achieve, giving policymakers a framework to explore various hypothetical scenarios before committing significant resources and legislation. This “what-if” analysis capability represents a powerful tool for proactive workforce planning.

To validate the model and refine its predictive capabilities, the researchers collaborated with state governments. Tennessee, North Carolina, and Utah have used their own labor data to validate the Iceberg Index and are actively developing policy scenarios based on the platform’s simulations. This close partnership with state-level actors is crucial to ensure the practical applicability and relevance of the research.

Tennessee has already incorporated the Iceberg Index into its official AI Workforce Action Plan, released this month. Utah is also slated to publish a similar report derived from the Iceberg Index’s modeling. These concrete examples of states using the tool underscore its value and potential for informing policy decisions.

North Carolina State Sen. DeAndrea Salvador, a close collaborator with MIT on the project, cites the ability to uncover effects missed by conventional tools as a key benefit. She highlights the value of detailed local-level insights.

“You can drill down to county-specific data to essentially say, within a certain census block, here are the skills that are currently being utilized and then match those skills with the likelihood of them being automated or augmented, and what could that mean in terms of shifts in the state’s GDP in that area, but also in employment,” explained Salvador. This level of granularity is particularly important for tailoring workforce development programs to meet the specific needs of different communities.

Salvador emphasizes that such simulation capabilities are particularly valuable as states establish various AI task forces and working groups, providing a common evidence base for policy discussions.

The Iceberg Index also challenges the conventional wisdom that AI risk is confined to tech roles in coastal hubs. The simulations reveal potential impact across all 50 states, including inland and rural areas frequently excluded from the AI conversation. This geographical distribution highlights the importance of a national strategy for addressing AI’s workforce implications.

To address this issue, the Iceberg team has developed an interactive simulation environment enabling states to experiment with different policy levers – ranging from allocating workforce development funding and modifying training programs to exploring the potential impacts of technology adoption on local employment and gross domestic product. This experimentation capability is crucial for developing effective and targeted policy responses.

“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 report states. This emphasis on data-driven decision-making is crucial given the scale of potential investment required.

Balaprakash, who also serves on the Tennessee Artificial Intelligence Advisory Council, shared state-specific findings with the governor’s team and the state’s AI director. He notes that Tennessee’s core industries – healthcare, nuclear energy, manufacturing, and transportation – remain heavily reliant on physical labor, providing some resilience against purely digital automation. The challenge, he argues, is to leverage technologies like robotics and AI assistants to strengthen these industries, rather than diminishing them. This nuanced approach—focusing on augmentation rather than simply automation—is likely to be critical for successful AI integration in many sectors.

The Iceberg team views the Iceberg Index not as a definitive solution but as a sandbox for states to prepare for AI’s impact on their workforces, promoting a proactive and adaptive approach.

“It is really aimed towards getting in and starting to try out different scenarios,” Salvador concludes, emphasizing the iterative and collaborative nature of the ongoing effort to understand and manage the evolving relationship between AI and the American workforce.