AI automation exemplifies technologies that entail task-level fixed costs, e.g., model training or fine-tuning. We integrate such fixed costs into a general theory of automation and show that AI’s comparative advantage becomes scale-dependent at the task level. We derive the elasticities characterizing the resulting production function, including the endogenous labor–AI substitution elasticity. We apply a quantitative version of the model to computer vision automation, disciplining fixed (training) and marginal (inference) costs using AI scaling laws, estimated from a fine-tuning experiment. Calibrated to 2023 U.S. data and recent estimates of the decline in compute prices, the model projects rapid automation, reaching 23% of firms (60% of employment) by 2035. The labor share traces a U-shape as the substitution elasticity declines from above to below one. Scale accounts for the majority (∼63%) of cross-task variation in AI comparative advantage.