Meeting AI’s Energy Demand Sustainably

AI’s rising electricity demand will create a broad investment opportunity across the power infrastructure value chain because reliability, affordability, and lower emissions must be addressed together.

• The IEA projects global data center electricity consumption to rise from about 415 terawatt-hours in 2024 to approximately 945 terawatt-hours by 2030.
• AI data centers have grown from typical 30-megawatt facilities a decade ago to 100-200 megawatts today, while hyperscaler campuses are planned at gigawatt scale.
• McKinsey estimates that the United States will require around 18 gigawatts of additional capacity for data centers by 2030, while Goldman Sachs and Morgan Stanley estimate requirements of up to 100 gigawatts.
• Nuclear power provides dependable, zero point-of-use emissions generation, natural gas offers dispatchable capacity with roughly half the carbon intensity of coal, and renewables with storage provide rapidly deployable capacity.
• Supply constraints extend across copper, uranium, transformers, switchgear, transmission connections, permitting, cooling systems, and skilled construction labor, with some interconnection and permitting processes taking almost a decade.
• Jennison expects bottlenecks to move through the value chain over time, broadening the opportunity from existing clean generation and electrical equipment to mature companies exposed to materials, grid infrastructure, efficiency, and power generation.

Takeaway: Investors seeking durable exposure to AI adoption should consider the companies enabling reliable, affordable, and lower-carbon power across the full energy value chain, while recognizing that buildout progress will be uneven and technology-specific bets carry concentration risk.