Modeling the Coevolution of Human Skills and AI Adoption
Abstract
The value of relying on generative AI depends on two quantities that evolve with its use: users' unaided skill, which may decline without practice, and the originality of AI-assisted work, which may decline as more people rely on similar tools. We model this process as a coevolutionary game in which individuals choose between autonomous and AI-assisted work, with autonomous practice strengthening their skills while reliance on AI gradually erodes them. When the reinforcing effect of skill erosion outweighs the crowding cost of lost originality, the population exhibits two alternative stable states: low AI adoption with high average skill, and high AI adoption with low average skill. Because skill is individual, autonomous workers remain more skilled than AI users, making autonomous work less vulnerable to population-wide skill decline. The strength of this effect is governed by a timescale ratio: the rate at which skills adapt relative to the rate at which individuals change strategy. The same ratio determines the full distribution of skills, which we derive analytically. Populations with identical AI adoption and average skill can differ sharply: when strategies change frequently, skills remain homogeneous and collective lock-in to high AI adoption is stronger; when strategies persist, lock-in weakens but the population can polarize into highly skilled autonomous workers and AI-dependent individuals. Modeling output overlap further shows that more diverse AI outputs can make widespread adoption harder to reverse. Together, these results expose tradeoffs between the benefits of widespread AI adoption and its long-run risks, making collective lock-in and skill polarization distinct intervention targets.
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BibTeX
@misc{frigerio2026modeling,
title = {{Modeling the Coevolution of Human Skills and AI Adoption}},
author = {Giacomo Frigerio and Susanna Caruso and Giacomo Livan and Vito Latora},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24794},
eprint = {2610.24794},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24794},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Modeling the Coevolution of Human Skills and AI Adoption
AU - Giacomo Frigerio
AU - Susanna Caruso
AU - Giacomo Livan
AU - Vito Latora
PY - 2026
DA - 2026/10/17
PB - arXiv
DO - 10.48550/arxiv.2610.24794
UR - https://arxiv.org/abs/2610.24794
AB - The value of relying on generative AI depends on two quantities that evolve with its use: users' unaided skill, which may decline without practice, and the originality of AI-assisted work, which may decline as more people rely on similar tools. We model this process as a coevolutionary game in which individuals choose between autonomous and AI-assisted work, with autonomous practice strengthening their skills while reliance on AI gradually erodes them. When the reinforcing effect of skill erosion outweighs the crowding cost of lost originality, the population exhibits two alternative stable states: low AI adoption with high average skill, and high AI adoption with low average skill. Because skill is individual, autonomous workers remain more skilled than AI users, making autonomous work less vulnerable to population-wide skill decline. The strength of this effect is governed by a timescale ratio: the rate at which skills adapt relative to the rate at which individuals change strategy. The same ratio determines the full distribution of skills, which we derive analytically. Populations with identical AI adoption and average skill can differ sharply: when strategies change frequently, skills remain homogeneous and collective lock-in to high AI adoption is stronger; when strategies persist, lock-in weakens but the population can polarize into highly skilled autonomous workers and AI-dependent individuals. Modeling output overlap further shows that more diverse AI outputs can make widespread adoption harder to reverse. Together, these results expose tradeoffs between the benefits of widespread AI adoption and its long-run risks, making collective lock-in and skill polarization distinct intervention targets.
ER -