Abstract

We develop a quantitative task-based model of automation in which deploying machines for a task, in addition to variable (marginal) costs, requires fixed costs (e.g., fine-tuning for AI models). A task is automated when its production has sufficient scale to amortize the fixed cost. Applying the model to the context of computer vision automation, we discipline the fixed and maringal costs using AI scaling laws relating performance to training costs. Estimating this relationship in a fine-tuning vision AI experiment, we additionally rely on LLM-generated measures of task complexity and error tolerance to infer the computational costs of automation for all vision-related production tasks in the economy. Calibrated to the U.S. firm-level adoption of computer vision AI in 2023, and fed with current estimates of the trend in falling computing costs, the model projects rapid diffusion, reaching 23% of firms (60% employment-weighted) by 2035, and shows that scale advantage accounts for roughly three-quarters of cross-task variation in comparative advantage. As computing costs fall, real output rises by about 7% by 2075, real wages increase throughout, and the labor share follows a U-shape as the aggregate elasticity of substitution between labor and AI declines with deeper automation.

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