AI Exchanges: Power Problems?

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Apr 2, 2026

Brian Singer, head of GSUSD and Goldman Sachs Research, gives research-driven analysis on data center power demand and energy sourcing. He breaks down hyperscaler spending, agentic AI’s impact on compute and energy, the six P framework for growth and constraints, labor bottlenecks in the energy workforce, and tradeoffs between behind-the-meter solutions and grid investments.

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INSIGHT

Hyperscaler Budgets Are Redrawing Power Demand

Hyperscaler capital and R&D budgets rose by over $300 billion for 2026–2027, driving downstream data center and power demand.
Brian Singer warns rising budgets plus continued productivity gains likely keep demand surprising to the upside, not causing immediate oversupply.

INSIGHT

Agentic Systems Become Token Demand Engines

Agentic machine-to-machine systems will generate far more token traffic than human-driven inference, becoming a runaway demand driver.
Brian Singer sees these agentic systems as 'furnaces for tokens' that materially raise energy intensity of inference.

INSIGHT

2030 Power Need Equals A New Top 10 Country

Goldman Sachs raised its global data center power demand forecast to ~220% growth in 2030 vs 2023, up from 175%.
That increase equates to adding a top-10 electricity-consuming country (roughly sixth-largest) in global demand terms.

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As the demand for AI-powered data centers continues to grow, Goldman Sachs Research’s Brian Singer explains the potential sources—and constraints—of power demand growth.

This episode was recorded on March 3rd, 2026.

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