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.

翻译：大语言模型（LLM）融入软件开发生命周期（SDLC）的过程，掩盖了一个关键的社会技术性失败：认知-系统性崩溃。本文提出"认知债务"这一概念，指工程师以被动AI验证替代逻辑推导所承担的隐性维护成本。这种债务侵蚀了根本原因分析所需的心智模型，加剧了系统复杂度与人类认知能力之间的鸿沟。此外，基于合成代码的递归训练可能使全球软件资源库同质化，削弱稳健工程所需的多样性。本研究以2026年亚马逊服务中断为案例，阐释"机械趋同"如何导致系统性脆弱性。为维持长期韧性，工程领导者必须超越基于提示的开发模式，实施严格的人机协同教学标准。该框架在AI驱动的生产力与应对日益不透明软件生态系统所必需的认识论自主权之间取得平衡。