arXiv:2604.03501v5 Announce Type: replace-cross Abstract: Experimental evidence suggests that AI tools raise worker productivity, but also that sustained use can erode the expertise on which those gains depend. To explore the consequences of this tradeoff, we develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill. We decompose the tool's productivity effect into two channels, one independent of worker expertise and one that scales with it. The model produces three main results. Firs
The proliferation of AI tools in white-collar work has created an urgent need to understand their long-term impact on human skill and productivity.
This research provides a critical framework for understanding the 'augmentation trap' where immediate AI-driven productivity gains can lead to a erosion of human expertise, impacting long-term organizational capability.
The understanding of AI's productivity impact now includes a critical dynamic trade-off between short-term gains and long-term skill degradation, requiring strategic management of AI integration.
- · AI-managed service providers
- · Education and retraining platforms
- · Organizations with robust human-AI teaming strategies
- · AI ethicists and organizational psychologists
- · Individuals who over-rely on AI tools without skill maintenance
- · Organizations that implement AI without considering skill erosion
- · Traditional human-centric training programs
- · Workers in tasks highly susceptible to cognitive offloading
Companies will need to develop sophisticated strategies for AI integration that balance productivity with critical skill maintenance.
This could lead to a 're-skilling imperative' where continuous human learning or AI-guided re-skilling becomes a crucial part of employment.
Long-term, society might face a paradox of increased overall productivity coupled with a decline in deep human expertise in certain domains, creating new vulnerabilities or dependence on AI systems.
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Read at arXiv cs.AI