The author argues that heavy reliance on AI coding agents produces fragile codebases unsuited for scalable SaaS applications, identifying four structural failure modes. First, AI writes shallow tests that prioritize trivial getters/setters and surface-level end-to-end paths, creating invisible coverage gaps not reflected in line-level metrics. Second, AI over-engineers solutions by rigidly applying principles like DRY, introducing unnecessary helper methods and complexity that impede human debugging. Third, sustained AI usage erodes developer comprehension of data structures and algorithms, preventing effective review of new abstractions against existing architecture. Fourth, accelerated PR velocity forces cursory reviews, allowing testing gaps and poor naming to accumulate into spaghetti code. The author correlates these patterns with observable symptoms: rising incident frequency (from monthly P0s to weekly outages), prolonged mitigation times due to shallow system knowledge, errors visible only to production users because AI omits comprehensive instrumentation, and degraded UX as software becomes optimized for machine generation rather than human use. The piece concludes that resilient, high-quality products require human-directed development to maintain architectural coherence and user-centric design, positioning AI-only workflows as viable only for low-end, price-competitive products.