Meta engineered a custom CXL 2.0 memory controller chip that bridges DDR4 and DDR5 server architectures, allowing existing DDR4 DRAM inventory to function within DDR5-only infrastructure. This approach defers the need for wholesale memory replacement during hardware refresh cycles. The move addresses a core cost driver in AI training infrastructure expansion. DDR5 server deployments typically require parallel investment in new memory modules, creating capital inefficiency when existing DDR4 stock remains viable. CXL 2.0 provides the protocol layer to extend hardware asset lifecycles without performance degradation. For operators managing AI infrastructure at hyperscale, this shifts capital allocation from memory inventory replacement toward compute and interconnect upgrades. Organizations can now phase DDR4 retirement gradually rather than synchronously with server generation upgrades. Second-order effect: reduced pressure on DRAM supply chains and pricing, which historically constrains deployment velocity during infrastructure scaling phases. The model becomes replicable—other cloud operators face identical cost pressures.