Safety Overreach in the AI Era:  An LMT Analysis of Guardrail Hypersensitivity and the Structural Cost of Excessive Safety Optimization

Recent updates to large language models, particularly Google’s Gemini series, have led to increased guardrail hypersensitivity, where even low-risk or casual interactions trigger safety restrictions. This paper analyzes this phenomenon through the lens of Load Minimization Theory (LMT), formalized as L = U + F + E − ℛ. We argue that the observed “L explosion” — sudden spikes in friction (F) and uncertainty (U) — stems from the complex interplay between stringent regulatory pressures (especially the EU AI Act), benchmark-driven safety optimization, and lowered activation thresholds. While companies aim to enhance safety, the resulting over-sensitization has unintentionally increased user-side load and reduced creative exploration. Drawing on the Observation Quality Model (O_quality), we show that high-quality, gentle observation can partially mitigate these effects. This study integrates previous work on context rot and stability loss, offering a structural understanding of the trade-offs between safety and conversational fluidity in the AI era. Implications for model design, user strategies, and regulatory balance are discussed.