The Hidden Infrastructure Powering the AI Boom

The boom in artificial intelligence is happening at a pace few industries have experienced. Models are scaling, compute intensity is rising, and demand for data center capacity continues to climb. As AI workloads push density and power demands to new extremes, infrastructure efficiency is both the most critical and the hardest challenge to solve.

Data center development is reshaping demand for energy and water in the communities that host them. The question to answer in the next decade is whether cloud, enterprise, and edge AI infrastructure can scale responsibly when cooling, power, and system architecture are as critical as compute itself.

AI Growth Is Redefining Resource Demand

Data centers have historically accounted for a small share of U.S. electricity demand, but that is quickly changing. In 2023, they accounted for approximately 4.4% of the total U.S. electricity consumption. By 2028, that figure is projected to rise to between 6.7% and 12%. At the same time, energy prices are expected to continue rising through 2026, and data centers are projected to face a 20% power shortfall through 2028. The surge in data center power demand coupled with increasing electrification of everything from automobiles to home heating is straining an already overtaxed grid, and putting pressure on households, communities, and businesses alike.

Cooling accounts for a significant portion of total data center energy consumption and is one of the largest drivers of overall facility load. Air cooling has been the foundation of data center thermal management for decades, relying on chillers, cooling towers, and air conditioning to maintain optimal operating temperatures.

But with every new generation of AI hardware, data centers are packing significantly more computing power into smaller spaces. This results in more heat being generated. More heat demands more cooling, and more cooling consumes more power and water. Efficiency is no longer a marginal concern; it is a foundational design requirement.

Minimizing Community Impact Is Now a Business Imperative

Data centers operate in communities that depend on stable electricity prices and reliable water access. As public awareness of data center resource consumption increases, communities and regulators are increasingly scrutinizing their impact.

In The Dalles, Oregon, the expansion of Google’s data center operations raised concerns about water usage and long-term environmental impact. In 2012, the tech giant used 12% of The Dalles’ water supply; by 2024 that amount had increased to nearly a third. This placed the company under public scrutiny, raising concerns about its resource use and its impact on local infrastructure and community needs.

Individual states have introduced legislation that could limit new data center construction. At the federal level, the current administration, while broadly supportive of AI, has encouraged technology companies to ensure that their data center developments do not increase household electricity prices or stress regional water supplies.

It’s clear that the landscape is changing for tech companies and data center developers. Corporate reputation now hinges on how well companies manage their physical footprint and relationships with the communities that host data centers.

From Public Commitments to Infrastructure-Level Change

In response to this shift, tech companies like Microsoft and OpenAI have made public commitments to “pay their way” to address community, public, and environmental concerns stemming from their rapid growth. Microsoft published a Community-First Infrastructure framework that sets explicit commitments for how it will build and run AI data centers in the United States. While these initiatives signal both meaningful progress and clear intent they address only part of the challenge. A more concrete and long-lasting solution requires reducing resource consumption at the infrastructure level.

That starts with cooling. The traditional approach of air cooling is insufficient for today’s AI hardware. That includes not just compute resources (CPUs and GPUs), but power supplies, storage, and networking as well. For greater thermal efficiency, cooling needs to be focused directly on the components generating heat, rather than the space surrounding them.

Precision liquid cooling captures heat directly at the component level using environmentally safe dielectric fluids rather than cooling the entire room. These systems can reduce energy use by up to 40% and water consumption by as much as 96%, while improving reliability and extending the lifespan of hardware. In addition to being nearly silent, they also protect sensitive components from airborne contaminants.

Designing Infrastructure for Resource-Constrained Reality

Organizations do not have to choose between cost, reliability, and sustainability. When infrastructure is designed holistically, these objectives reinforce each other. Lower energy consumption reduces operating expenses, while reduced water use mitigates regulatory risk and public scrutiny. Improved thermal management also enhances system performance and extends the lifespan of critical hardware components.

Infrastructure that inherently consumes less energy and water aligns more naturally with emerging policy frameworks and environmental standards. Companies that succeed will prioritize this shift and adopt advanced, sustainable thermal management solutions. The future of infrastructure will be shaped not only by innovation in software but by intentional infrastructure design.