Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.
Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.
AI workloads, however, demand far more power with greater energy density.
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Senior Director of Power Networks at Worley.
Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.
Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.
In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.
For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.
Balancing site trade-offs to unlock faster delivery
Site selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.
In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.
Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.
While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.
Combining power solutions can accelerate bringing capacity online more efficiently
As AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.
In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.
As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.
These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.
Rethinking cooling can support high-density AI and optimize when energy is used
With this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.
One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.
At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.
Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.
Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.
This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.
Early efforts on permitting can identify the fastest development route and avoid delays
Permitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.
For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.
Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.
This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.
Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.
Turning AI demand into operational capacity at the speed and scale the market requires
The importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.
The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.
Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.
Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.
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