Occupations, Tasks and Generative AI: A Computable General Equilibrium Analysis

This paper develops a task-based computable general equilibrium model to analyse the long-run economic effects of generative AI (GenAI) on the Australian economy. Each occupation performs a continuum of tasks executed in three modes: with raw labour; with AI-augmented labour; or automated using equipment and AI services. Task-level productivities in AI-using modes are draws from correlated Frechet distributions, captur ing heterogeneous within-occupation exposure. The model covers 45 industries and 97 occupations, calibrated to occupation-level GenAI exposure scores. The reference simulation yields a 29.8% real GDP increase: roughly one third from task-level productivity gains, the rest from capital deepening and general equilibrium reallocation. Real consumption - our long-run welfare metric - rises by 16.2%, substantially less because additional investment is required to equip automated tasks. Augmentation accounts for more tasks than automation in nearly all industries and occupations. Labour-market adjustment is dominated by within-occupation change - extensive-margin task reallocation equivalent to two thirds of current work - rather than net employment shifts between occupations. Losses con centrate in clerical, administrative, and sales roles, while most blue-collar occupations gain. Real wage effects are weakly correlated with initial wages; the rising capital share of income may matter more for distribution. Sensitivity analysis shows aggregate outcomes hinge on the distribution of task-level productivity gains: fatter tails roughly double the GDP gain while preserving the adjustment pattern, whereas variation in the dependence parameter shifts the augmentation - automation balance and the incidence of adjustment. Conventional substitution elasticities matter less.