[Submitted on 29 Apr 2026 (v1), last revised 18 Aug 2026 (this version, v3)]

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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.

Submission history

From: Frank Ginac [view email]
[v1] Wed, 29 Apr 2026 16:20:25 UTC (132 KB)
[v2] Sun, 3 May 2026 21:34:35 UTC (288 KB)
[v3] Tue, 18 Aug 2026 01:38:21 UTC (370 KB)

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