AI expansion drives energy demand and memory shortages

The rapid expansion of artificial intelligence is creating significant infrastructure challenges, specifically regarding energy resilience and component availability. According to the International Energy Agency (IEA), global electricity consumption by data centers is projected to reach approximately 945 TWh by 2030, nearly doubling the 2024 levels. The shift toward AI-driven workloads requires more dynamic and reactive electrical systems to manage the rapid fluctuations in power demand caused by high-performance GPUs.

Simultaneously, the industry is facing a severe memory shortage driven by the high demand for high-performance components in data centers. This scarcity has led to dramatic price increases for DRAM, HBM, and NAND memory. Reports indicate that memory prices rose between 80% and 90% in the first quarter of the year, with some segments seeing costs multiply significantly. This supply bottleneck is impacting consumer electronics and enterprise infrastructure, with major companies like Microsoft, Apple, Amazon, Dell, HP, and Cisco adjusting prices to reflect these rising component costs. The shortage threatens to slow the mass adoption of AI technology by increasing the cost of the very hardware required to train and run large-scale models.