The Directions of Technical Change

TL;DR

This paper models AI adoption as a team-production decision influenced by task-specific capabilities, highlighting how improvements in AI lead to expanded adoption directions and partial utilization between thresholds.

Contribution

It introduces a high-dimensional, task-based model of AI adoption that accounts for endogenous task directions and the role of worker supervision, extending existing technical change models.

Findings

AI capability improvements expand adopted task directions.

Small capability gains near entry thresholds cause large adoption expansions.

Optimal AI use is partial between entry and full adoption thresholds.

Abstract

Generative AI is directional: it performs well in some task directions and poorly in others. Knowledge work is directional and endogenous as well: workers can satisfy the same job requirements with different mixes of tasks. We develop a high-dimensional model of AI adoption in which a worker uses a tool when it raises their output. Both the worker and the AI tool can perform a variety of tasks, which we model as convex production possibility sets. Because the tool requires supervision from the worker's own time and attention budget, adoption is a team-production decision, similar to hiring a coworker. The key sufficient statistics are the worker's pre-AI shadow prices: these equal the output gain from a small relaxation in each task direction, and they generally differ from the worker's observed activity mix. As AI capability improves, the set of adopted directions expands in a cone centered on these autarky prices. Near the entry threshold, small capability improvements generate large extensive-margin expansions in adoption. The model also delivers a structured intensive margin: between the entry and all-in thresholds, optimal use is partial. We parametrize the model in a simple but flexible way that nests most existing task-based models of technical change.