How artificial intelligence is reshaping electricity markets and redefining efficiency inside data centers and real estate portfolios
Across North America, electricity load forecasts are being revised upward as hyperscale campuses, colocation expansions, and AI clusters come online. Grid operators are responding to a new reality: computing growth is accelerating faster than generation and transmission capacity.
Global data center electricity consumption could reach 1,000 terawatt-hours annually by 2030, roughly equivalent to the annual consumption of Japan. In the United States, PJM Interconnection has reported a sharp increase in capacity auction prices as new large-load customers, including AI-driven data centers, enter the interconnection queue.
Goldman Sachs forecasts a 175% surge in global data center power demand by 2030, a significant upward revision driven by the rapid adoption of AI infrastructure and intensive GPU requirements. This surge is expected to raise data centers’ share of U.S. electricity consumption to approximately 8% and contribute to a 10–15% increase in European power demand over the next decade.
AI Workloads Are Structurally Different
Previous digital expansion cycles, including early cloud adoption, drove steady and predictable growth. AI changes the profile of demand.
Training large language models requires sustained, high-density compute over extended periods. Inference activity multiplies that demand across millions of daily interactions. Rack densities are climbing. Thermal loads are intensifying. Mechanical systems are operating closer to their performance limits for longer durations.
Higher density translates directly into greater airflow sensitivity and tighter thermal tolerances. Small inefficiencies that were once negligible now compound materially across 8,760 operating hours per year.
Cooling Is a Major Line Item, Not a Support Function
In many modern facilities, mechanical systems account for 30 to 40 percent of total site energy consumption. That includes chillers, pumps, CRAH and CRAC units, and most consistently, fans.
Fan energy is particularly sensitive to system resistance. Based on affinity laws, fan power scales approximately with the cube of airflow. As static pressure increases, required fan energy rises disproportionately. Even modest increases in resistance can translate into meaningful increases in kilowatt draw.
Filtration is one of the most persistent contributors to system resistance. As filters load or when they have a high baseline pressure drop, fans compensate continuously. This is not a temporary event. It is a compounding operational penalty.
For a 20-megawatt data center, cooling and mechanical systems often account for 30 to 40 percent of total facility energy use. That equates to roughly 6 to 8 megawatts dedicated to cooling infrastructure.
Even modest efficiency improvements at this scale are financially meaningful. A 5 percent reduction in cooling-related fan energy would reduce load by approximately 300 to 400 kilowatts. At an electricity price of $0.10 per kilowatt-hour, that translates to roughly $260,000 to $350,000 in annual energy cost savings, before considering demand charges or capacity pricing exposure.
In regions with constrained supply or rising capacity costs, the economic value of that reduction increases further.
Grid Stress Is Changing the Economics of Efficiency
Electricity markets are tightening in regions experiencing concentrated data center growth. Capacity auctions in PJM have reflected the strain of new large-load interconnection requests. Transmission upgrades require time. Generation additions face permitting and capital constraints.
As reserve margins narrow, price volatility increases. Capacity charges, peak demand pricing, and long-term power purchase agreements are becoming more complex and more expensive.
For data center operators, efficiency becomes a hedge against volatility. Each kilowatt not consumed reduces exposure to price swings and capacity premiums. For REITs with diversified portfolios, it strengthens net operating income resilience across properties facing similar market pressures.
Scope 2 emissions reporting adds another layer of accountability. Institutional investors increasingly evaluate carbon intensity alongside operating performance. Mechanical efficiency directly influences both.
Energy savings are no longer incremental improvements. They are risk mitigation tools.
Designing for AI Without Expanding Energy Budgets
The strategic response does not require speculative technology. It requires disciplined optimization of existing systems.
Three priorities stand out:
1. Reduce persistent static pressure
Airflow resistance accumulates silently over time. Low-pressure, high-efficiency filtration technologies reduce baseline resistance while maintaining particulate capture performance. This lowers continuous fan energy and protects downstream equipment.
2. Optimize thermal management architecture
Variable-speed fans, containment strategies, and calibrated ventilation align airflow with actual load rather than worst-case assumptions. This stabilizes thermal performance under higher rack densities.
3. Institutionalize monitoring-based commissioning
Continuous diagnostics prevent efficiency drift. Small degradations in airflow or heat exchange are corrected before they become embedded in long-term energy profiles.
The Competitive Advantage of Thermodynamic Discipline
Artificial intelligence will continue to expand electricity demand. That trajectory is supported by credible projections from global energy authorities and financial institutions. The constraint is not computing ambition. It is infrastructure capacity.
Facilities that treat thermodynamic efficiency as core infrastructure rather than as an ancillary optimization will outperform in this environment. Lower static pressure, calibrated airflow, and disciplined mechanical management translate directly into reduced exposure to grid stress and electricity price escalation.
The economics are clear. As AI reshapes the demand curve, cooling efficiency becomes one of the most controllable variables in an increasingly uncontrollable market.
Data centers are powering intelligence.
Efficiency will determine who powers it profitably.
