Cognitive Atrophy and Systemic Collapse in AI-Dependent Software Engineering

TL;DR

This paper discusses the risks of cognitive and systemic collapse in AI-driven software engineering, emphasizing the importance of human oversight to maintain system resilience.

Contribution

It introduces the concept of 'Epistemological Debt' and advocates for human-in-the-loop standards to prevent systemic fragility in AI-assisted SDLC.

Findings

AI verification can erode engineers' mental models.

Recursive synthetic code training reduces software diversity.

Case study of 2026 Amazon outages illustrates systemic fragility.

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.