Global semiconductor shortages beginning in 2020, combined with geopolitical supply disruptions extending through 2025, imposed material constraints on hyperscale cloud infrastructure expansion that could not be resolved through conventional procurement. Lead times for critical networking components stretched to 168-280 days; hard disk drive production faced component-level shortages with no viable short-term substitution paths. This paper presents a practitioner-grounded framework for sustaining hyperscale storage infrastructure growth under these conditions, drawn from operational experience managing exabyte-scale object storage deployments across commercial and government-classified regions. We describe five interdependent strategies: (1) logarithmic overhead reduction through schema governance and retention policy enforcement, achieving a 35-40% reduction in raw storage consumption per unit of customer data; (2) systematic elimination of orphaned and over-provisioned resources, recovering capacity equivalent to hardware additions without procurement; (3) workload-aware placement across heterogeneous storage media, reducing single-technology supply dependency; (4) end-to-end provisioning and capacity management automation, reducing planning cycle overhead by 66%; and (5) proactive constraint modeling for network topology and power infrastructure, with procurement horizons extended to 18-24 months. For each strategy, we describe the decision criteria that determined its scope and sequencing, the technical mechanisms of implementation, the measurement approach used to validate outcomes, and the failure modes encountered. The combined effect was 35-40% gains in effective storage capacity from existing infrastructure, sustained through periods when hardware lead times made conventional scaling infeasible. We generalize these findings into a constraint-driven efficiency framework applicable to any large-scale distributed storage deployment operating under material scarcity.