Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering
TLDR
AI in software engineering creates "Epistemological Debt," eroding human understanding and leading to systemic fragility, requiring human-in-the-loop standards.
Key contributions
- Introduces "Epistemological Debt" where AI verification replaces human derivation, degrading engineers' mental models.
- Warns that recursive training on synthetic code homogenizes software, reducing variance and increasing systemic fragility.
- Illustrates "mechanized convergence" and its risks using a 2026 Amazon outage case study.
- Proposes human-in-the-loop pedagogical standards to balance AI productivity with epistemic sovereignty.
Why it matters
This paper highlights critical, often overlooked risks of over-reliance on AI in software development, such as the erosion of human expertise and systemic fragility. It offers a timely warning and a framework for maintaining robust, resilient software ecosystems in an AI-driven future.
Original Abstract
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.
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