Details: iceberg.mit.edu
Research: lpm.media.mit.edu
Abstract: Artificial Intelligence is reshaping America’s $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI automates quality control in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes, not how AI capabilities overlap with human skills across occupations.
Project Iceberg addresses this gap by simulating the human–AI labor market, representing 151 million workers, 32,000 skills, and 3,000 counties interacting with 13,000 AI tools. It introduces the Iceberg Index, a skills-centered metric that quantifies where AI technical capabilities and human occupational skills overlap, weighted by wage value. The Index measures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. The Index shows that while visible AI adoption is concentrated in computing and technology (2.2\% of wage value, about \$211 billion), technical capability extends far beyond the surface to cognitive and administrative work across finance, healthcare, and professional services (11.7\%, about \$1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5\% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy.
By simulating how capabilities may spread under alternative scenarios, Project Iceberg enables policymakers to identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation.