Smart-city services increasingly depend on AI systems whose performance can be constrained far below the application layer across compute fabrics, networks, and the physical interconnects carrying data between accelerators. At hyperscale, these infrastructures can contain enormous numbers of high-speed links. Conformance testing establishes whether those links meet defined requirements, but operational health also depends on how their margin changes over time. This talk examines an important and often less-visible risk in AI infrastructure: silent degradation. Changes in error-correction margin, thermal behavior, link stability and telemetry can remain individually acceptable while collectively signaling emerging risk and contributing to reduced compute efficiency and service-level degradation. One way to evaluate longitudinal reliability is by validating across Workload, System, Network, Interconnect and Environment, applied through Measure, Detect, Diagnose and Forecast Risk strategy. The approach complements existing compliance and qualification practices by adding cross-layer visibility aimed at identifying infrastructure trending toward failure before service impact occurs.