The Iceberg Index: Measuring Workforce Exposure Across the AI Economy
🤖 AI Summary
Conventional labor metrics inadequately capture AI’s deep technical exposure across occupations, overlooking overlaps between AI capabilities and human skills—and their upstream effects.
Method: We construct a large-scale population model covering 151 million workers, representing each as an autonomous agent endowed with 32,000 granular skills, and simulate their collaborative interactions with thousands of AI tools. We introduce the “Iceberg Index”—a skill-centered metric quantifying the wage value of AI-substitutable skills within each occupation.
Results: Cognitive automation generates $1.2 trillion in latent exposure—five times larger than explicit exposure in tech sectors—concentrated in administrative, financial, and professional services, with broad geographic dispersion. Traditional economic indicators explain less than 5% of this exposure. Our approach transcends employment-count–driven paradigms, enabling the first comprehensive, skill-level assessment of occupational AI exposure.
Application Category
📝 Abstract
Artificial Intelligence is reshaping America's $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks 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 after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx $211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approx $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 these capabilities may spread under scenarios, Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize investments, and test interventions before committing billions to implementation
Problem
Research questions and friction points this paper is trying to address.
Measuring AI's workforce exposure beyond visible technology sectors
Capturing AI's technical capability to perform occupational skills
Addressing limitations of traditional workforce metrics for AI economy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Simulates labor market using Large Population Models
Introduces skills-centered Iceberg Index metric
Measures wage value of AI-performable occupational skills
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