Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility
Quick Answer
This study presents a two-period model analyzing how firms should balance AI deployment and worker engagement, revealing that engaging less-skilled workers is cost-effective for fallback purposes, while worker mobility shifts investment towards higher-skilled workers.
Quick Take
This study presents a two-period model analyzing how firms should balance AI deployment and worker engagement, revealing that engaging less-skilled workers is cost-effective for fallback purposes, while worker mobility shifts investment towards higher-skilled workers. The model highlights the dual dimensions of AI progress—capability and reliability—and their impact on future skill development.
Key Points
- Firms must decide on AI deployment versus worker engagement to optimize output.
- Engaging less-skilled workers is cheaper and enhances fallback capabilities.
- Worker mobility influences labor-market sorting towards higher-skilled roles.
- AI capability increases engagement value, while reliability can have mixed effects.
- Future skill development is shaped by current work allocation decisions.
Paper Resources
📖 Reader Mode
~2 min readAbstract:When firms deploy autonomous AI, they must decide how much work to leave to the system and how much to keep workers engaged. This decision affects current output and future human capital. We develop a parsimonious two-period model in which AI may outperform the worker when it functions, but may fail with positive probability. A firm chooses worker engagement; engagement lowers current output for below-benchmark workers, but changes future skill through learning and erosion. We distinguish two dimensions of AI progress: capability, the system's output when it works, and reliability, the probability that it works. In a single-firm benchmark, engagement is valuable only as fallback investment. The firm engages the least-skilled workers most, because they have the largest skill gaps and are least costly to bring toward a useful fallback level. With worker mobility, engagement also affects labor-market sorting: workers prefer jobs that build more valuable skill trajectories. This sorting motive targets higher-skill workers near the AI frontier, where skill gains are more valuable and engagement is less costly. Mobility can therefore reverse the engagement pattern, shifting investment from the least-skilled toward the most-skilled workers below the AI benchmark. Mobility also reshapes how AI progress affects engagement: greater capability raises engagement by increasing the value of the skill trajectory a firm offers, whereas greater reliability can raise or lower it because it reduces fallback need while also changing learning opportunities. Under worker mobility, human-AI work design becomes a problem of human-capital investment, in which allocating work today shapes future skill.
| Comments: | 32 pages, 5 figures, 31-page appendix |
| Subjects: | Artificial Intelligence (cs.AI); General Economics (econ.GN); Theoretical Economics (econ.TH) |
| MSC classes: | 90B50, 90B70, 90B30, 91B38, 91B39, 91A10 |
| ACM classes: | J.4; K.4.3; I.2.0 |
| Cite as: | arXiv:2606.29111 [cs.AI] |
| (or arXiv:2606.29111v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29111 arXiv-issued DOI via DataCite |
Submission history
From: Tinglong Dai [view email]
[v1]
Sat, 27 Jun 2026 23:34:23 UTC (245 KB)
— Originally published at arxiv.org
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