AI Vulnerability Score™ Methodology
Full transparency on how we calculate your score. Every input, weight, transformation, and data source is documented here.
What This Score Measures
The AI Vulnerability Score is a 0–100 composite index that measures how well you can absorb an AI-driven income disruption. It uniquely combines occupation-level AI displacement exposure with household balance-sheet resilience into a single, actionable number.
The score architecture follows a threat (20%) – buffer (53%) – adaptability (27%) hierarchy modeled after actuarial risk assessment, grounded in FICO methodology principles.
Six Input Variables
Your job category mapped to a displacement risk score based on task automatability, physical presence requirements, interpersonal complexity, novel judgment, regulatory barriers, and LLM-specific exposure.
Liquid savings divided by monthly essential expenses. The strongest single predictor of surviving any income shock per CFPB, Vanguard, and Financial Resilience Institute research.
Total monthly debt obligations divided by gross monthly income. High DTI accelerates runway depletion because fixed debt payments consume replacement income.
How applicable your current skills are to roles outside your current occupation. Brookings found 6.1 million workers face both high AI exposure AND low adaptive capacity.
Percentage of total household income not dependent on primary employment. Even modest diversification meaningfully reduces vulnerability.
Liquid assets divided by total assets. Captures wealth accessibility beyond runway — a person with $500K in home equity but $2K cash is more vulnerable than someone with less total wealth but more liquidity.
Non-Linear Scoring
Each raw input is transformed through a variable-specific non-linear function before weighting. Linear scoring would misrepresent the demonstrably non-linear relationship between these variables and resilience.
For example, financial runway uses a concave (square root) transformation — going from 0 to 3 months of savings produces a much larger score improvement than going from 12 to 15 months. This reflects research showing even small emergency savings buffers dramatically reduce financial distress.
Interaction Terms
High Risk × Low Skill: When occupation risk is high AND skill transferability is low, a penalty is applied. This reflects Brookings' finding that this combination creates qualitatively different risk.
Runway × DTI: High DTI accelerates runway depletion. Effective runway is adjusted downward based on DTI level, reflecting the reality that debt payments consume replacement income during disruption.
Score Range and Calibration
The score uses a 15–95 effective range within the 0–100 scale, following FICO methodology. A floor of 15 prevents nihilistic interpretations. A ceiling of 95 maintains aspiration.
Important Limitations
AI displacement predictions carry significant uncertainty. The occupation risk scores are based on current research but may shift as AI capabilities evolve. The weights are literature-informed but not regression-validated against actual displacement outcomes.
This tool is one input among many for understanding your financial resilience. It is educational content, not personalized financial advice. Consult a licensed financial professional for individualized guidance.
Data Sources
- Eloundou et al., "GPTs are GPTs" (Science, 2024)
- Goldman Sachs, AI and Economic Growth Research (2023–2025)
- World Economic Forum, Future of Jobs Report (2025)
- Brookings Institution, Adaptive Capacity Research (2019–2026)
- Federal Reserve, Survey of Consumer Finances (2022)
- Federal Reserve, SHED Survey (2024)
- BLS, Displaced Workers Survey (2024)
- Vanguard, How America Saves (2025)
- CFPB, Financial Resilience Research
- ILO, Generative AI and Jobs Working Papers (2023–2025)
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