By Data center electricity demand will double by 2030, jumping from 448 terawatt-hours to 980 TWh according to Gartner’s November 2025 forecast. AI-optimized servers alone will surge fivefold, from 93 TWh to 432 TWh, accounting for 44% of total data center power by decade’s end. For developers, this isn’t environmental news—it’s infrastructure reality hitting cloud bills and architecture decisions. Electricity has suddenly emerged as the limiting factor for AI development, not model architecture or training data.
The Scale of the Problem
Multiple authoritative sources converge on the same unsettling projection. The International Energy Agency’s December 2025 analysis forecasts data center demand reaching 945 TWh by 2030, slightly more than Japan’s entire electricity consumption. In the United States, data centers will jump from 4.4% of total electricity in 2023 to between 6.7% and 12% by 2028, according to Department of Energy estimates.
What makes this particularly concerning: data center electricity is growing at 15% annually, four times faster than all other sectors combined. AI is the primary driver, with the IEA projecting AI workloads could account for 35-50% of data center power by 2030, up from just 5-15% in recent years.
Your Cloud Bill is About to Get Worse
This electricity crisis translates directly to money. Starting June 2026, residential customers in the PJM region will see average bill increases of $18 per month in western Maryland and $16 per month in Ohio, driven largely by capacity market pricing for data centers. Carnegie Mellon’s Open Energy Outlook study projects an 8% national average increase by 2030, with high-demand markets like Northern Virginia facing increases exceeding 25%.
For developers working with cloud infrastructure, the math is brutal. A single NVIDIA A100 GPU instance on AWS costs $3-5 per hour—over $40,000 annually for continuous operation. Data transfer fees alone can account for up to 30% of cloud AI expenditures for data-intensive applications. And these costs aren’t temporary spikes; they’re the new baseline as electricity becomes scarcer relative to demand.
Why AI Workloads Devour Electricity
AI training clusters consume seven to eight times more energy than typical computing workloads. The technical explanation is straightforward: massive parallel processing across thousands of GPUs, adjusting billions of parameters through repeated computations. Power density tells the story—AI training pushes 100-160 kilowatts per rack, with next-generation GPUs expected to require 300+ kW per rack. Traditional compute operates at a fraction of that.
What surprises many developers: 80-90% of AI computing power is now used for inference, not training. A ChatGPT query consumes five times more electricity than a web search. Memory systems and cooling requirements compound the problem, as constant data movement between memory and processors generates heat that must be continuously managed.
Edge Computing Becomes Cost-Competitive
The electricity crisis is accelerating the shift to edge computing. Organizations implementing hybrid AI architectures report 15-30% cost savings compared to pure-cloud deployments. The edge AI market, valued at $25.7 billion in 2025, is on track to grow as part of a broader edge computing sector projected to hit $380 billion by 2028.
The hybrid model is emerging as the standard approach: use cloud infrastructure for the heavy computational lift of model training, then optimize and deploy to the edge for inference. This reduces unnecessary data transfer and keeps processing costs local. For developers planning AI infrastructure, edge inference is no longer a nice-to-have optimization—it’s becoming a cost-critical architecture decision.
The Grid Can’t Keep Up
BloombergNEF’s forecast revision illustrates the acceleration. In just seven months, their projection for U.S. data center power demand by 2035 jumped 36%, now reaching 106 gigawatts. The PJM region expects data center capacity to hit 31 GW by 2030, while the Energy Information Administration forecasts only 28.7 GW of new generation capacity over the same period. The math doesn’t work.
Massive investments reflect this constraint. The Stargate initiative plans to spend $500 billion building ten data centers, each requiring five gigawatts—more than New Hampshire’s total power demand. Google alone is spending $75 billion on AI infrastructure in 2025. Yet as the CSIS analysis puts it, “electricity supply has suddenly emerged as a binding constraint on data center expansion and AI progress.” The limiting factor isn’t demand for AI or willingness to pay for compute—it’s transformers, backup power generators, and grid capacity.
For developers, the implications are clear: cloud AI costs will continue rising, infrastructure optimization is no longer optional, and architecture decisions around edge deployment matter more than ever. The AI boom hit the power wall. Plan accordingly.
