The advance of artificial intelligence is rapidly transforming the global digital infrastructure. Generative models, real-time inference systems and edge computing architectures are driving up the demand for computing capacity and, with it, the energy consumption of data centers. In this context, energy efficiency ceases to be an optional improvement and becomes a structural factor that determines the viability of AI growth.
According to the International Energy Agency, data centers consumed around 415 TWh of electricity in 2024 and could approach 945 TWh in 2030 if the current pace of deployment of AI-related loads is maintained. In countries like Spain, the consulting firm DNV estimates that forecasts point to a strong increase in the electrical consumption of these infrastructures in the coming years, coinciding with an increase in investment and the arrival of new projects, which reinforces the need to prioritize energy efficiency from the design phase.
This scenario raises a key question: can AI grow at the current rate without transforming the way energy is managed in data centers and without placing energy efficiency at the heart of the operation?
More power, more density and less margin of error
Unlike traditional data centers, AI-oriented facilities work with much higher energy densities. Where once a rack consumed between 5 and 10 kW, today it is common to exceed 30 kW, with even higher peaks in intensive training environments.
This increase in density reduces the tolerance margins for any electrical inefficiency. Losses, phase imbalances, micro-cuts or poor power quality not only affect the availability of the service, but also increase indirect consumption, accelerate the aging of equipment and penalize the PUE of the data center, directly impacting overall energy efficiency.
In this context, energy efficiency is no longer limited to “consuming less”, but rather better managing each available kilowatt, ensuring continuity, stability and control.
Measure to optimize: the basis of energy efficiency
The first step towards an efficient energy operation is visibility. Without accurate, real-time data, it is impossible to identify losses, optimize loads or anticipate incidents that compromise energy efficiency.
For this reason, advanced electrical monitoring systems have become a key tool in modern data centers. Solutions such as DIRIS Digiware, from Socomec, allow granular measurement of energy consumption and quality in both alternating and direct current, at the line level, sub-panel or even by critical areas of the installation, facilitating advanced energy efficiency strategies.
This analysis capability facilitates:
• Detect inefficiencies before they escalate into consumption or risk.
• Optimize load distribution.
• Improve decision making in environments with high energy variability, such as those associated with AI.
Efficiency and continuity: two sides of the same coin
Energy efficiency in data centers cannot be separated from electrical continuity. Each microoutage, sag, or transient forces restarts, reworks, and recoveries that consume additional power and impact overall performance.
In this sense, modular and scalable solutions such as MODULYS XM allow the UPS capacity to be adapted to real load variations, reducing losses in online mode and improving energy efficiency throughout the entire operating range. This approach minimizes the energy dissipated in conversion stages and reduces the need for investment in installed power that remains underutilized.
Reducing interruptions not only improves availability: it also reduces energy consumption associated with failures and recoveries, an aspect especially relevant in long-running AI processes.
Sustainability beyond the origin of energy
Although the integration of renewable energy is a key element in the energy transition, sustainability in data centers also depends on maximizing internal energy efficiency and extending the useful life of the equipment.
Sustainability in data centers also depends on maximizing internal energy efficiency and extending the useful life of the equipment.
Designing efficient, well-protected and monitored electrical infrastructure reduces thermal stress, limits premature wear and avoids unnecessary replacements. In the long term, this translates into less consumption, less waste and an operation more aligned with sustainability objectives.
Efficiency as a condition for scalable AI
Artificial intelligence will continue to grow, but its development will be conditioned by the capacity of data centers to manage energy efficiently, reliably and sustainably, with energy efficiency as a strategic criterion. Measuring accurately, ensuring continuity and reducing losses is no longer just a technical issue, but a strategic decision.
In this new scenario, electrical infrastructure stops being a support element and becomes one of the pillars that support the digital future.
