Monday, October 26, 2026

Goldman Sachs Projects 165% Data Center Power Surge by 2030, Repositioning US Utility Stocks

Data center electricity demand will rise 165% by 2030, Goldman Sachs projects, creating a structural tailwind for grid-connected utilities and nuclear operators. The US grid was designed for 1-2% annual growth, giving existing capacity scarcity value. Energy infrastructure firms near major AI compute clusters are drawing renewed investor attention.

LM Salvado
LM Salvado

June 23, 2026

Goldman Sachs Projects 165% Data Center Power Surge by 2030, Repositioning US Utility Stocks
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Data center power demand will rise 165% by 2030, according to Goldman Sachs.1 That single figure is reshaping how markets value energy infrastructure companies.

The demand driver is clear. A single ChatGPT query consumes roughly 10x the energy of a Google search.2 Training next-generation large language models requires power equivalent to a small city.2 Microsoft, Amazon, and Alphabet are now among the largest customers on the US electricity grid.2

The supply side is the constraint. The US grid was engineered for 1-2% annual growth per year.2 It was not built to absorb an AI-driven demand spike of this scale. That mismatch creates scarcity value for existing grid-connected generation capacity — particularly assets physically proximate to data center clusters.

Utilities sitting near hyperscaler campuses are uniquely positioned. Power purchase agreements with AI operators are structurally price-inelastic: hyperscalers need guaranteed electrons to run inference workloads. They cannot absorb outages the way a factory can. That dynamic shifts pricing leverage toward the generator.

Nuclear operators carry an additional advantage. AI companies have flagged 24/7 carbon-free power as a procurement requirement, not a preference. Nuclear baseload meets that spec. Grid-scale solar and wind, intermittent by nature, require storage additions that raise effective costs.

Bitzero Holdings (AIBZ) is among the companies positioning for the AI energy infrastructure opportunity.2 Broader investor interest is tracking toward any operator with contracted capacity near major data center hubs.

The structural case is straightforward: AI energy demand is not discretionary. Hyperscalers have committed to multi-year capital programs — data center construction pipelines measured in tens of billions. The electricity to run those facilities must come from somewhere. Utilities with the right geography and grid interconnection are the path of least resistance.

For market participants, the question is no longer whether AI creates utility upside. It is which operators have the grid position, permitting status, and generation mix to capitalize before interconnection queues and transmission constraints tighten further.

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About this analysis

This is a Via News analysis. It synthesizes signals, events and patterns across our coverage rather than deriving from a single source document, so it carries no external source pointer. Via News is a conduit: where a claim traces to a specific document, we link it. How we source

LM Salvado
LM Salvado

LM Salvado is an AI possibilist — he takes the risks of AI seriously, and still sees the route through them. Founder of Via News Agency, an AI-native newsroom built on full source-traceability, he tracks how AI is reshaping markets, capital, and labor — the quiet shifts that happen before the headlines catch up.

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