Goldman Sachs, the US investment bank, has published a projection that electricity demand from US data centers will double by 2027, according to a report circulated on May 19, 2026. The forecast frames the artificial-intelligence computing buildout not as a niche technology story but as one of the largest near-term drivers of US electricity consumption.
Executive Summary
The headline claim is simple and stark: the amount of power consumed by US data centers — the facilities that house the servers behind cloud services and AI models — is projected by Goldman Sachs to double by 2027. A doubling over such a short horizon is extraordinary for electricity demand, a category that in the US grew slowly or stayed flat for most of the two decades before the AI boom.
Why it matters: power, not land or chips, has become the binding constraint on data-center expansion. If a major financial institution’s base case is a doubling within roughly a year and a half of the report’s publication, then utilities, grid operators, regulators, and data-center developers are all planning against a demand curve steeper than anything the sector has seen. Forecasts like this one shape capital allocation — transmission projects, generation buildouts, and multi-year power purchase agreements are being underwritten on the strength of exactly this kind of projection.
Power Is Now the Product
For most of the industry’s history, data-center capacity was measured in square feet; today it is measured in megawatts. The Goldman Sachs projection captures that shift: the constraint on AI infrastructure growth is no longer how fast servers can be manufactured, but how fast electricity can be generated and delivered. AI training and inference clusters draw far more power per rack than traditional enterprise computing, which is why demand can double even if the number of buildings grows much more slowly.
A doubling forecast, if it holds, effectively converts every data-center siting decision into an energy-procurement decision. Markets with available grid interconnection — the formal process of connecting a large load to the transmission system — gain a decisive advantage over markets with cheaper land or better fiber routes. That reorders the competitive map for developers and colocation providers alike.
Who Absorbs the Demand — and Who Profits
Utilities and independent power producers are the most direct beneficiaries of a demand doubling: large, creditworthy, around-the-clock loads are the customers grid operators dream of. Transmission builders, transformer and switchgear manufacturers, and backup-power suppliers sit next in line, since delivering twice the load requires physical equipment that is already supply-constrained industry-wide.
The cost side is less comfortable. Rapid demand growth tends to push up wholesale power prices and interconnection wait times, which raises operating costs for every data-center operator — including those serving ordinary cloud and enterprise workloads rather than AI. Residential and industrial ratepayers in data-center-heavy regions may also bear part of the grid-upgrade cost, a tension that is already a live regulatory debate in several US states.
Reading a Bank Forecast Critically
It is worth being precise about what this is: a projection by an investment bank, not a measurement. Demand forecasts for AI infrastructure have varied widely across analysts, and they are sensitive to assumptions about chip efficiency, model sizes, and how much announced capacity actually gets energized on schedule. Goldman Sachs has a research franchise in this area, but banks also have commercial exposure to the energy and technology sectors they cover, so the appropriate posture is neither dismissal nor uncritical adoption.
The strongest reason to take the direction of the forecast seriously — even if the exact multiple proves off — is that it aligns with observable behavior: hyperscale operators signing long-dated power agreements, utilities revising load forecasts upward, and interconnection queues lengthening. Forecasts can be wrong on timing and still be right about the trend that planners must build for.
Background
US data centers spent two decades as a quiet, efficient corner of the electricity system: demand grew, but efficiency gains in servers and facility design largely kept national consumption in check. The generative-AI boom that began in late 2022 broke that equilibrium. AI clusters concentrate enormous electrical loads in single campuses, and cloud providers and specialized developers have been racing to build capacity, turning power availability into the industry’s defining constraint.
Goldman Sachs is one of several major financial institutions now publishing recurring research on data-center energy demand, reflecting how central the topic has become to utility planning, energy markets, and technology investment. Its projections are widely cited by developers, utilities, and policymakers — which is precisely why the assumptions behind them merit as much attention as the headlines.
