The Impact of Generative AI on Knowledge Workers’ Skills, Productivity and Career Development
DOI:
https://doi.org/10.30750/ijarst.120402Keywords:
Generative AI, knowledge workers, skill development, productivity, career development, deskillingAbstract
Generative AI raises the productivity of knowledge workers, most for those with the least skill, and this compression of performance differences has been widely reported. Less attention has been paid to what happens to the underlying skills of workers who rely on the tools, and to the careers that depend on those skills. This paper develops a dynamic model in which generative AI lifts output in proportion to the gap between a worker’s skill and the frontier, while its effect on skill development depends on how the worker uses it: novices who substitute the tool for practice develop skill more slowly, experts who use it to extend their reach develop faster. A simulation over eight years for novice, intermediate and expert workers shows that novices gain 93% in productivity in year one but only 36% by year eight, and end with lower underlying skill (0.42) than novices without the tool (0.50); intermediates gain 27% falling to 20% with slightly higher skill; experts gain 4% with slightly higher skill. Because career progression depends on skill rather than on tool-assisted output, novices face a career penalty masked by their productivity. The paper draws implications for individual workers, for organisations designing roles and development, and for the structure of knowledge-work careers in which the apprenticeship stage is eroded.
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