AI is reshaping data center design faster than any previous workload. Training and inference clusters demand unprecedented power density, sustained utilization and ultra-high bandwidth connectivity. At the same time, electricity prices remain volatile, grid connections are increasingly difficult to secure and environmental scrutiny is intensifying across regions.

Efficiency, once treated as a cost-optimization exercise, has become the primary way operators unlock usable power for growth. The challenge is no longer how to reduce energy use, but how to make enough power available to support continued AI expansion.

Why efficiency matters more than ever

Power, not space or server count, is now the limiting factor in today’s data centers. AI accelerates this problem by concentrating consumption into dense clusters that push electrical and thermal limits simultaneously.

Cooling technologies, liquid loops and advanced airflow design all play a role. But even with best-in-class facility engineering, inefficiencies elsewhere can undermine progress, leaving power effectively stranded by issues such as:

• Under-utilized compute, where capacity exists but can’t be fully exploited
• Imbalanced workload placement, leading to localized power and cooling constraints
• Network bottlenecks, which limit effective cluster scaling

Every wasted watt of power reduces the data center’s effective output, inflates the cost per computation, and limits ROI, while constraining how much growth existing sites can support.

The operators who win will be the ones who cut power consumption across the entire AI chain.

Regulatory pressure adds another dimension. Energy reporting requirements, carbon accounting and regional sustainability mandates increasingly force operators to quantify not just total consumption, but efficiency per workload.

This exposes a less obvious issue: energy inefficiency doesn’t only raise operating costs, it increases exposure to regulatory risk by driving higher emissions per unit of useful compute. Inefficient architectures require more capacity to deliver the same output, amplifying emissions, grid impact and compliance overhead.

As a result, efficiency improvements that once delivered incremental savings now directly influence where and how data centers can be built, expanded or interconnected.

The rise of distributed AI data centers

To work around power and land constraints, many operators are moving away from single, monolithic campuses and distributing AI workloads across multiple facilities, often separated by tens or hundreds of kilometers, to tap into diverse grid connections and regional energy availability.

This shift fundamentally changes the role of data center interconnect (DCI). Inter-facility links are now part of the compute fabric itself, carrying latency-sensitive traffic that enables clusters to behave as one logical system.

In this model, transport efficiency becomes critical. Poorly optimized DCI can introduce hidden penalties through:

• Power-hungry optics, which scale energy use with bandwidth
• Limited reach or capacity, forcing additional intermediate sites
• Operational complexity, increasing overhead as networks expand

Energy efficiency doesn’t stop at the data center wall

As AI clusters scale horizontally, transport networks must deliver massive bandwidth without proportionally increasing energy consumption. Otherwise, the power constraint simply shifts from the facility to the network.

This is where open, high-capacity optical transport plays a critical role. Efficient line systems, modern modulation techniques and coherent optics designed for dense DCI allow operators to move more data using less power, while simplifying scaling and operations.

Platforms such as our FSP 3000 IP OLS and energy-efficient coherent pluggable transceivers are designed to address this challenge directly. By automating operations and eliminating unnecessary opto-electronics – therefore reducing watts per transported bit – these technologies help ensure that networking interconnect solutions support, rather than constrain, AI growth.

Instead of treating DCI as a necessary overhead, forward-looking operators design it as an efficiency multiplier in their so-called “scale-across” architecture, enabling distributed architectures that extract more value from constrained power budgets.

Scaling AI efficiently, end to end

While advances inside the data center remain essential, the ability to interconnect facilities efficiently is becoming just as critical to sustainable growth. For many operators, energy efficiency is already the limiting factor in AI growth, not within individual sites, but across the networks that connect them. 

As AI continues to drive demand, the operators that succeed will be those who reduce power consumption across the networks that tie them together.

To learn how our open optical transport solutions support more energy-efficient DCI for scalable AI workloads, visit adtran.com.