Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering
For software engineering leaders and practitioners, this paper highlights a critical but overlooked risk of AI integration that could undermine long-term system resilience.
The paper identifies 'Cognitive-Systemic Collapse' in AI-dependent software engineering, where reliance on LLMs erodes engineers' mental models and homogenizes code, leading to systemic fragility. Using the 2026 Amazon outages as a case study, it argues that this 'mechanized convergence' increases risk of large-scale failures.
The integration of Large Language Models (LLMs) into the software development lifecycle (SDLC) masks a critical socio-technical failure: Cognitive-Systemic Collapse. This paper introduces "Epistemological Debt," the hidden carrying cost incurred when engineers substitute logical derivation with passive AI verification. This debt erodes the mental models essential for root-cause analysis, widening the gap between system complexity and human comprehension. Furthermore, recursive training on synthetic code threatens to homogenize the global software reservoir, diminishing the variance required for robust engineering. Using the 2026 Amazon outages as a case study, this research illustrates how "mechanized convergence" leads to systemic fragility. To preserve long-term resilience, engineering leaders must move beyond prompt-based development to implement rigorous human-in-the-loop pedagogical standards. This framework balances AI-driven productivity with the epistemic sovereignty necessary to manage increasingly opaque software ecosystems.