Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility

2026-06-27

AI Summary

This study addresses the trade-off firms face between immediate productivity gains and long-term human capital accumulation when deploying autonomous AI. Building a two-period dynamic game model, it distinguishes between AI capability and reliability, incorporates mechanisms of skill acquisition and obsolescence, and integrates labor mobility to examine its impact on human–AI task allocation. The analysis reveals that in high-mobility labor markets, firms prefer investing in high-skill workers closer to the AI frontier, and that improvements in AI capability generally stimulate greater human capital investment. By reframing human–AI task design as a human capital investment problem, this work demonstrates how labor mobility can reverse firms’ relative preferences for high- versus low-skill workers, offering a novel perspective on organizational decision-making in human–AI collaboration.

Abstract

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.

Problem

• human-AI collaboration
• skill investment
• worker mobility
• AI reliability
• human capital

Innovation

• human-AI collaboration
• skill investment
• worker mobility
• AI capability and reliability
• human capital dynamics