Data Centers, AI and the Evolving Energy Landscape

You can't just flip the switch. As artificial intelligence (AI) continues to reshape industries and redefine technological capability, it is also poised to become one of the most significant drivers of energy demand in the coming decade. 

In a Commonfund Forum 2025 session discussion, Ethan Levine, Managing Director and Head of Real Assets and Sustainability, and Dan Connell, Managing Director both at CF Private Equity, offered a detailed analysis of the evolving energy landscape—highlighting both the challenges and opportunities ahead for investors.

AI’s Expanding Energy Footprint

For over 15 years, U.S. energy demand has remained flat, largely due to gains in efficiency and the offshoring of industrial production. However, this trend is reversing as artificial intelligence and data center growth introduce unprecedented power requirements.

AI-related workloads are significantly more energy-intensive than traditional computing. Estimates indicate a single AI query may consume 20–30 times more electricity than a standard internet search. Some analysts project that the total additional energy required to support AI and data center growth could be the equivalent to adding the energy consumption of four to six New York Cities—within just the next five years. Recent analysis based on U.S. Energy Information Administration (EIA) and Goldman Sachs data forecasts a >2% increase in U.S. power demand by 2030, with nearly half of that growth attributable to data centers alone.

Grid Constraints and Infrastructure Headwinds

Meeting this demand is not simply a matter of building more capacity. The U.S. electric grid is already strained, and climate-related disruptions are becoming more frequent. Power outages are increasing in both duration and frequency, while hundreds of energy infrastructure projects remain stuck in interconnection queues.

Key constraints include:

• Permitting and regulatory delays: Time to bring new generation capacity online has doubled from under two years to nearly five.
• Aging grid infrastructure: The current system was not designed for the high-density loads that are emerging.
• Technological limitations: Efficient cooling systems, grid software, and storage solutions are emerging but are either not yet mature enough or lack the scale to fully meet near-term needs.

Notably, the energy sector remains one of the most heavily regulated segments of the economy, which presents both a risk and an opportunity for long-term investors seeking clarity and predictability.

Investment Implications: Where Capital Is Likely to Flow

Despite these challenges, several structural trends are creating a favorable environment for capital deployment:

• Renewable energy continues to scale rapidly and has achieved grid parity in terms of levelized cost of electricity (“LCOE”). Wind and solar now represent some of the most competitive sources of new generation on an LCOE basis.
• Battery storage is emerging as a critical enabling technology, particularly as it becomes more commercially viable and cost-effective. However, storage for longer durations remains an area for continued improvement.
• Grid optimization technologies—including AI-driven software, data analytics, and enhanced cooling solutions—represent an important category for private equity and venture capital investment.
• Energy efficiency is becoming a strategic necessity for data center operators and energy-intensive industries. As power prices rise, efficiency improvements will be essential not only for managing costs, but for unlocking project viability.

Strategic Considerations for Investors

From a capital markets perspective, the evolving energy landscape calls for a dual focus: supporting scalable, cost-effective generation, technology and infrastructure, while navigating regulatory constraints to enable faster deployment.

The tension between rising demand and the system’s current limitations suggests that pricing will play a central role in incentivizing new capacity. At the same time, investor attention must remain on the reliability and resilience of the energy system: while innovation and urgency are needed, rolling blackouts and intermittent supply are not acceptable trade-offs.

Conclusion

The intersection of AI and energy is one of the most dynamic frontiers in the current market environment. While challenges related to infrastructure, policy, and technology are substantial, so too are the investment opportunities. Long-term capital committed to real assets, digital infrastructure, and energy transition strategies will be well-positioned to benefit from the structural changes underway.