Enterprise AI Analysis Report
The Iceberg Index: Measuring Workforce Exposure in the AI Economy
Artificial Intelligence is reshaping America's over $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 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 across 3,000 counties and interacting with thousands of AI tools.
The Iceberg Index
A skills-centered KPI for the AI economy. It measures the percentage of wage value of skills that AI systems can perform within each occupation, revealing where human and AI capabilities overlap.
Executive Impact: Key Metrics & Projections
Project Iceberg reveals the true scale of AI's economic transformation, far beyond conventional estimates. Understand the hidden exposures and strategic implications for your enterprise.
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Visible Tech Adoption (Surface Index)
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Wage Value in Tech Occupations
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Hidden Cognitive Automation (Iceberg Index)
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Wage Value in Admin/Finance/Professional Services
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Larger Exposure Beyond Tech
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Workers Simulated
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Skills Covered
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AI Tools Cataloged
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Project Iceberg: Sandbox for Human-AI Workforce
Project Iceberg simulates the emerging human-AI workforce. It models how 151 million American workers and emerging AI capabilities interact, allowing states to explore policy scenarios and assess potential workforce exposure patterns before committing billions to infrastructure and training programs. Built on MIT's Large Population Models and powered by Oak Ridge National Laboratory's Frontier supercomputer, Iceberg turns trillions of workforce data points into scenario-planning capability.
The Iceberg Index evaluates each occupation along three dimensions: the skills required, the automatability of those skills, and the value of the work in wages and employment. Together these factors yield a consistent measure of technical exposure that can be aggregated across occupations, industries, and regions. Formally, the Index for a given occupation weights each skill by its relative importance, automatability score, and prevalence, producing a single exposure value between 0 and 100%.
Validation with Real-world Data
Our methodology is rigorously validated through two tests: skill-based occupational representations and exposure predictions aligning with actual AI usage. Skill-based embeddings predict 85% of observed career transitions, confirming our framework captures genuine labor market structure. State-level exposure predictions show 69% geographic agreement with actual AI usage patterns (Anthropic Economic Index), particularly strong at the extremes (e.g., Washington, California, Colorado as leaders; Wyoming, Mississippi, Alaska as laggards).
This validation confirms the Index as a leading indicator, identifying structural exposure before widespread adoption, and ensuring its relevance for proactive workforce planning.
Key Insights from the Iceberg Index
The Iceberg Index reveals that visible tech sector disruption is just the tip. Cognitive automation spans administrative, financial, and professional services nationwide, with a fivefold larger wage value exposure than concentrated tech hubs. This hidden exposure creates significant blind spots for traditional workforce planning, especially in manufacturing states.
Understanding the structure of this exposure – whether concentrated or distributed – is crucial for designing effective, tailored strategies. Traditional economic metrics largely fail to capture these systemic workforce transformations, underscoring the need for a skills-centered approach.
Project Iceberg Methodology
CAPTURE: Understand Human Workforce
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ANALYZE: Measure AI Workforce
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SIMULATE: Model Human-AI Interaction
Project Iceberg simulates the human-AI workforce, mapping 151 million workers and 13,000+ AI tools to anticipate disruption and test interventions.
| Feature | Physical Economy (Census Visible) | AI Economy (Census Blind Spot) |
|---|---|---|
| Manufacturing Jobs | Factory locations mapped | AI-mediated work: No geographic anchor |
| Service Employment | Business address tracked | Human-AI teams: No job category exists |
| Worker Residences | Household surveys capture | AI Coordination: Siloed in private analytics |
| Economic Activity | Geographic boundaries | Digital value creation: Platform controlled data |
| Overall Insight | Physical economy = measurable economy. People work in places, Census counts people and places. | AI economy ≠ measurable economy. AI automation tools have no state id, work through private platforms census can't see. |
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Skill-based predictions of career transitions are accurate.
Validation against independent data from millions of AI usage interactions shows strong agreement on leading and aspiring states, confirming our approach captures genuine adoption behavior. Skill-based representations predict 85% of career transitions, and exposure predictions achieve 69% geographic agreement with actual usage patterns.
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Visible Tech Sector Exposure (Surface Index).
Headlines focus on tech layoffs, but these affect occupations representing only 2% of labor market wage value. The hidden mass beyond visible tech sectors is five times larger.
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Nationwide Cognitive Automation Exposure (Iceberg Index).
Administrative and financial tasks where AI demonstrates capability span five times more wage value than visible tech disruption—and are geographically distributed across all states, not just coastal. States like Delaware and South Dakota show higher Index values than California.
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Higher White-Collar Exposure in Manufacturing Belt.
Midwest states like Ohio and Michigan face double-digit technical exposure in white-collar work while states may focus on physical automation. Cognitive and administrative exposure from validated AI capabilities measures upto ten times higher than technology-sector exposure.
Varies
Exposure concentration determines strategy.
The same level of technical exposure can require entirely different responses. Concentrated patterns enables sector-specific action, while distributed patterns demands multi-sector coordination.
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Traditional metrics explain less than 5% of Iceberg Index variation.
GDP and unemployment track today's visible tech disruption, but they fail to capture the nationwide spread of white-collar automation revealed by the Iceberg Index.
Case Study: Lessons from the Internet Era
Early-moving regions captured durable advantages during the internet era. For example, North Carolina's Research Triangle matured into a global research hub; Texas scaled Austin into a top tech market; Tennessee and Kentucky became national logistics leaders; and Utah's “Silicon Slopes” rose as a cloud computing center.
Similar dynamics will now shape AI adoption patterns. Regions that align workforce development, infrastructure investment, and industry strategy early may establish competitive advantages, while delayed response could result in widening disparities. Project Iceberg provides analytical infrastructure to support evidence-based workforce planning as AI capabilities expand across the economy.
Advanced ROI Calculator
Estimate the potential efficiency gains and cost savings for your enterprise by integrating AI-driven automation.
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Your AI Transformation Roadmap
A phased approach to integrate AI capabilities, optimize workflows, and empower your workforce for the new economy.
Phase 01: Strategic Assessment & Planning
Conduct a deep dive using the Iceberg Index framework to identify high-exposure roles and skills, quantify potential impact, and align AI strategy with business goals. Define pilot projects.
Phase 02: Pilot Implementation & Workforce Reskilling
Deploy AI solutions in targeted pilot areas. Initiate tailored training programs to reskill employees for AI-augmented roles, focusing on oversight, testing, and new value creation tasks.
Phase 03: Scaled Integration & Performance Monitoring
Expand successful pilots across the enterprise, integrating AI tools into core workflows. Establish continuous monitoring and feedback loops to optimize AI performance and adapt to evolving labor market dynamics.
Phase 04: Continuous Innovation & Future-Proofing
Foster a culture of AI-driven innovation, exploring new capabilities and proactively adapting to technological advancements. Develop agile workforce planning models to maintain competitive advantage.
Ready to Navigate the AI Economy?
The Iceberg Index provides foresight for strategic AI workforce planning. Don't wait for disruption – act proactively.
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