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The Directions of Technical Change

Author

Listed:
  • Miklos Koren
  • Zsofia Barany
  • Ulrich Wohak

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.

Suggested Citation

  • Miklos Koren & Zsofia Barany & Ulrich Wohak, 2026. "The Directions of Technical Change," Papers 2602.12958, arXiv.org, revised Feb 2026.
    • Handle: RePEc:arx:papers:2602.12958
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    References listed on IDEAS

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