TG Srinivasan
Andrej Karpathy’s AI Exposure Dashboard provides an occupational scoring metric using data from the Bureau of Labor Statistics (BLS) Occupational Outlook Handbook to assess the vulnerability of various professions to artificial intelligence (AI). This note evaluates the dashboard’s heuristic approach against findings from the formal labour economics literature. The dashboard’s results are found to directionally align with established task-based exposure models, indicating high exposure for cognitive, computer-mediated occupations and low exposure for physically embodied work. However, as an exploratory metric, the tool remains subject to significant economic limitations, notably the omission of demand elasticity, within-occupation task heterogeneity, and general equilibrium adjustments.
Recent advancements in generative AI have prompted widespread efforts to quantify occupational exposure. Karpathy’s AI Exposure Dashboard functions as a transparent exploratory occupation-scoring exercise rather than a structural forecast of job loss. Evaluating 342 BLS occupations that represent over 143.06 million US jobs, the metric identifies 38.1% of occupations and 34.3% of total employment as highly exposed to AI (defined as a score of 7 or above on a 10-point scale).
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