Why your data layer is AI’s most critical climate technology
Executive Take
CIOs should treat data-layer fragmentation, not model selection, as the primary lever for controlling AI's cost and energy footprint, and should audit retrieval paths and duplication before approving further infrastructure spend.
Executive Summary
This opinion piece argues that enterprise AI's energy demand is driven less by model size than by fragmented data architecture. It cites IEA warnings, Goldman Sachs' projection of 165% data center power demand growth by 2030, current data center consumption of ~415 TWh (1.5% of global electricity), and claims engineers spend up to 40% of time on data prep, with ~60% of AI projects abandoned due to data readiness gaps.
Why It Matters
Technology and AI leaders face growing pressure to justify AI infrastructure costs and energy use, and this piece reframes the conversation from token pricing to data architecture as the actionable fix; GCC leaders managing shared infrastructure across regions should also note the sovereignty/governance angle.