Publication archive
Browse recent AI infrastructure intelligence, briefs, and source-linked analysis. Source-linked records current through Aug 19, 2026. Long-form publishing last authorized Aug 12, 2026. Pipeline last successful update Aug 19, 2026.
Publication archive
EIA's high-demand case shows why near-term data-center growth is first a dispatch, fuel, and regional price problem, even before new plants can be built.
Capacity plans in ERCOT and PJM now depend on when large loads arrive, which generators can run harder, and whether grid supply can respond during the same hours.
EIA's long-term outlook separates server electricity from the cooling and ventilation that support it, exposing a wider facilities load than a server-only forecast captures.
Data-center capacity models need parallel assumptions for installed server stock, operational power draw, cooling intensity, weather, and efficiency rather than one headline demand curve.
Commercial electricity sales show the scale of Virginia's data-center growth, but PJM's Dominion peak forecast is the sharper test for reserves, infrastructure, and hourly reliability.
Developers and grid utilities must reconcile annual energy growth with the hours that set generation, transmission, demand-response, and storage requirements.
Dedicated nuclear agreements match the steady load of large data centers, but a contract for generation is still separate from interconnection, restart, transmission, and campus commissioning.
The value of a nuclear deal depends on the sequence that converts a long-term power commitment into electricity delivered at the site when computing capacity is ready.
ERCOT's large-flexible-load process separates applications, approved capacity, actual utilization, and curtailment, giving data-center planners a better model than the headline queue.
Generation and price forecasts depend on how much large-load capacity is operating, how steadily it consumes electricity, and whether it reduces demand during tight hours.
EIA data show that Virginia, Texas, and other states with rapid computing-facility development account for a disproportionate share of commercial electricity growth.
Regional grid and site selection decisions should follow where new commercial load is concentrating, not a national average that masks local pressure.
EIA projects commercial computing electricity use to outgrow efficiency gains and add cooling and ventilation demand across data-center floorspace.
Utilities and facility designers need to model computing and its supporting building systems as a growing base load rather than a small office end use.
U.S. generation set another annual record as commercial demand, including data centers, helped end the long period of nearly flat electricity use.
Capacity planning must now account for sustained load growth instead of assuming that efficiency will keep national electricity demand level.
EIA expects rising commercial load in Texas and Virginia to increase the call on natural gas generation as data centers add operating and cooling demand.
Gas supply, plant utilization, and regional summer conditions remain part of the near-term power case for new computing capacity.
EIA's forecast pairs renewed electricity consumption growth from data centers and manufacturing with a large wave of new solar generation.
The planning question is whether variable new supply, firm generation, storage, and grid delivery can match the hours when large computing loads run.
EIA expects the strongest regional electricity growth in Texas and the mid-Atlantic, where proposed data centers are concentrated and timing remains uncertain.
Interconnection, generation, and transmission decisions in these two markets will shape which announced campuses reach usable capacity first.
EIA expects solar generation and battery capacity to expand while data-center demand raises natural gas generation in ERCOT and PJM.
Capacity teams need an hourly supply mix that connects renewable additions, dispatchable generation, storage, and the regional timing of large computing loads.
New Lower 48 peak-demand records combine hot-weather cooling load with an underlying increase in electricity use around large data-center markets.
A campus power plan must hold during the same extreme hours that test regional generation, transmission, and reserve margins.
A new EIA pilot will collect data on energy sources, electricity use, site characteristics, server metrics, and cooling systems across major U.S. data-center regions.
Better measured load and cooling data can reduce the gap between announced capacity, utility forecasts, and actual operating demand.
EIA building data show that offices with data centers use more electricity for computing and cooling than comparable offices without server facilities.
Distributed server rooms can add meaningful facilities load outside the large standalone campuses that dominate utility forecasts and market headlines.