Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration

While industry discourse often emphasizes immediate productivity gains and frames GenAI primarily as a tool for automation, the integration of GenAI into system administration may involve deeper shifts in professional practice that are not yet fully understood. Drawing on 14 semi-structured interviews with IT professionals, this paper explores the lived reality of embedding GenAI into daily routines of troubleshooting, scripting, and system verification. Through inductive thematic analysis, we uncover two unanticipated socio-technical findings. First, we describe a "compression of traditional expertise pathways" where GenAI appears to function as both a mentor-like tutor and a "ladder-shortening" tool. While the tool can support faster task performance in unfamiliar domains, our findings suggest it may also reduce a practitioner's exposure to the foundational, hands-on cycles of building, failing, and debugging that historically served as the training ground for technical expertise. Second, we describe a "performance perception shift," where the speed of AI-assisted work begins to reset organizational and self-expectations for productivity. This shift may create a "two-speed culture" within teams and introduce "productivity guilt," as necessary manual work, even when required for safety or validation, is increasingly perceived as slow or a failure of efficiency. Our results raise broader questions about how GenAI may influence expertise development, how professional value is assessed in high-stakes technical environments, and the role of human judgment in complex technical environments.