From microservices to AI factories: cognitive debt in GPU-accelerated distributed systems

Abstract

In recent years, GPU-accelerated inference supporting generative workloads has begun to destabilize the operational assumptions that underpinned cloud-native software systems. Engineers who once reasoned about reliability, capacity, and performance through statelessness, horizontal scalability, and elastic compute increasingly confront stateful, hardware-constrained workloads shaped by finite accelerator capacity and tightly coupled execution domains. This study examines how these structural conditions reorganize the operational mental models through which distributed production systems are regulated. The study treats operational mental models as cognitive infrastructure within socio-technical control loops. Inherited cloud-native abstractions structure how practitioners interpret telemetry, coordinate action, and select interventions. Under sustained accelerator constraint, these abstractions increasingly diverge from system dynamics. This divergence is conceptualized as cognitive debt: a form of model–constraint misalignment that produces recurring patterns of intervention mismatch, metric misinterpretation, and cross-role coordination friction. Drawing on literature analysis, qualitative coding of technical artifacts, and reflexive practitioner observation, the study identifies a coordinated reorganization of operational reasoning. A recurring condition of operational double vision emerges, in which practitioners alternate between elasticity-oriented microservice logic and a constraint-oriented regulatory frame termed AI-factory cognition. Within this emerging frame, concepts such as model residency, execution-domain coupling, goodput, and cost-per-token become central to system regulation. The findings suggest that GPU-accelerated inference does not merely extend cloud-native infrastructure but reconfigures the control logic through which distributed systems are governed. By articulating cognitive debt as model–constraint divergence and identifying mechanisms of regulatory realignment, this study contributes a conceptual framework for understanding how operational control structures reorganize under sustained hardware constraint in contemporary AI-driven production environments.