The accelerating adoption of artificial intelligence has created the most energy-intensive computing cycle in modern history. Training foundation models and running large-scale inference systems requires dense clusters of graphics processing units (GPUs), often operating continuously across multiple geographies. This is not a one-time trend or a temporary infrastructure cycle. It is the foundation of a generational energy demand shift that is already underway.
Goldman Sachs estimates that power consumption from data centers will rise by one hundred sixty percent by 2030. This demand is largely driven by artificial intelligence training and inference workloads, which use exponentially more power than traditional cloud computing. Unlike earlier tech buildouts, the scale of electricity required to run artificial intelligence models cannot be deferred, throttled, or reduced through software efficiencies. This new workload is physical. It is thermally intensive, geographically dispersed, and entirely dependent on baseload power that must be stable and scalable.
