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：大规模语言模型（LLMs）融入软件开发生命周期（SDLC）掩盖了一个关键的社会技术性失效：认知-系统双重崩溃。本文提出"认知债务"概念，即当工程师用被动AI验证取代逻辑推导时产生的隐性持有成本。这种债务侵蚀了根本原因分析所需的心智模型，导致系统复杂性与人类理解之间的鸿沟持续扩大。此外，基于合成代码的递归训练威胁着全球软件库的同质化，削弱了稳健工程所需的关键方差。以2026年亚马逊服务中断事件为案例，本研究阐释了"机械趋同"如何引发系统性脆弱性。为维系长期韧性，工程领导者必须超越提示词驱动开发模式，建立严谨的"人类在环"教学标准。该框架在AI驱动生产力与应对日益不透明软件生态系统所需的认知自主权之间实现平衡。