Abstract: Practitioners and scholars increasingly recognize that generative AI adoption in knowledge work does not always follow conventional automation patterns—where paid hours decline proportionally to task replacement. This article synthesizes emerging evidence with recent theoretical insights to explain a puzzle: workers may experience substantial productivity gains from AI tools while recorded wages and formal hours remain stable or even increase. Drawing on Gans (2026) and empirical studies across software development, consulting, and professional services, the article demonstrates that when workers derive intrinsic value from certain productive tasks, automation changes not only which tasks are replaced but also the boundary between paid responsibilities and voluntary work. Organizations face a containment motive—automating tasks to prevent uncapped voluntary expansion—alongside traditional replacement and scale effects. The practical implication is that payroll data alone cannot identify total work intensity, task composition, or well-being outcomes. Effective organizational responses require evidence-based interventions spanning communication, job redesign, capability building, and bundle-pricing compensation systems. Building long-term resilience demands psychological contract recalibration, distributed governance, and continuous learning infrastructure. These findings challenge the assumption that automation primarily substitutes paid labor with machines; in knowledge-intensive settings, it also redistributes effort between compensated and uncompensated time.
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