(untitled) (International scientific-electronic journal “Pioneering studies and theories”, 2026)
Information systems in the digital economy are increasingly replaced or abandoned within 2-5 years of deployment-far shorter than their intended lifespan. This article investigates premature system obsolescence as an economic problem rooted in algorithmic and data structure decisions made at the design stage. Through a systematic analysis of 24 industry reports and academic sources, we demonstrate that incorrect algorithm selection - particularly in Low-Code/NoCode (LC/NC) platforms and AI-Driven Development (AIDD) environments - is a primary structural cause of performance degradation, technical debt accumulation, and costly system replacement. Our empirical findings show that systems built with O(n²) sorting algorithms degrade to unacceptable performance thresholds within 2.8 years on average, compared to 12.5 years for systems using optimal O(log n) structures. The economic cost differential reaches 4.8M per incident for worstcase algorithm choices vs. 180K for optimal implementations. We argue that LC/NC and AIDD paradigms, while accelerating development speed, systematically eliminate the developer’s ability to make informed algorithmic decisions, thereby creating a structural vulnerability that manifests as premature obsolescence. Recommendations for algorithm-aware development frameworks are proposed.