The rapid expansion of AI has transformed the digital infrastructure landscape in just a few years. Training and operating advanced AI models now requires enormous computing clusters running continuously across hyperscale data centers. As companies race to deploy more powerful generative AI systems, the underlying infrastructure supporting them is expanding at an unprecedented pace. Behind the GPUs and servers that power this new generation of computing lies an equally critical challenge: providing stable, high-capacity energy to keep these systems running reliably around the clock.
While data centers have always been energy-intensive facilities, AI has changed the scale and nature of their electricity demand. The result is a convergence of digital and energy infrastructure, making batteries essential to modern data centre design.
Reshaping Global Data Center Expansion with AI Workloads
AI is driving a structural shift in the way computing infrastructure is built. Unlike traditional workloads, AI training and inference use dense clusters of specialized processors running at full capacity for extended periods. Training large language models requires thousands of GPUs running for weeks, while inference must stay available for millions of users.
These workloads significantly increase both energy consumption and power density within data centers. As such, global data center electricity consumption is expected to rise from 448 tWh in 2025 to around 980 tWh by 2030, effectively doubling within five years as AI adoption accelerates. AI-optimized servers are projected to account for 44% of total data center power consumption by decade’s end.
The rapid expansion of AI infrastructure is also changing how electrical systems are designed inside data centers. Power density at the rack level is rising dramatically, with modern AI racks already consuming 30-100 kW each, compared with 5-10 kW in traditional data centers, creating far greater demands on power delivery and resilience across the facility. This growth is already visible in current infrastructure investment, with facilities expanding into multi-gigawatt campuses designed specifically for AI clusters.
Power Availability Challenges for Modern AI Data Centers
As AI infrastructure grows, access to electricity is becoming one of the main constraints on new data center construction. In many regions, power grids were not originally designed to support large clusters of facilities each consuming hundreds of megawatts. Indeed, global data center power demand from AI operations is expected to rise sharply. Furthermore, overall electricity consumption is projected to reach about 945 tWh by 2030, driven largely by AI workloads. This surge places significant pressure on utilities to expand generation capacity, transmission infrastructure, and grid resilience.
Despite strong demand for AI infrastructure, power supply limitations are already affecting data center expansion in multiple regions. In Europe, new data center capacity added in 2025 was 11% lower than in 2024 because power availability constraints delayed project completions, even as demand for AI‑driven infrastructure continues to rise. Similarly, the largest utilities in the U.S. are planning to add up to 30 GW of new power generation capacity by 2035 to support data centre loads.
Ensuring Continuous and Reliable Power for AI Data Centers
Beyond power consumption, AI data centers also introduce new challenges related to power stability and reliability. High-performance computing clusters cannot tolerate interruptions or fluctuations in electricity supply without risking data loss, hardware damage, or service outages. AI workloads are particularly sensitive to these disruptions as training processes often run continuously for extended periods. Even short outages can force entire training runs to restart, wasting significant computing resources and energy.
As a result, modern data centers rely on complex electrical architectures designed to guarantee uninterrupted power delivery. Traditionally, this infrastructure has included diesel generators and uninterruptible power supply systems that provide backup power during grid disturbances. However, the growing scale of AI facilities is increasing the importance of battery-based energy storage systems.
These systems serve several functions simultaneously. Firstly, they provide immediate backup power in the event of grid outages, bridging the gap until backup generators start operating. Second, they stabilize voltage and frequency fluctuations that could otherwise affect sensitive computing equipment. Third, they enable operators to manage large and sudden changes in power demand created by dynamic AI workloads.
Integrating Battery Energy Storage into Core Data Center Infrastructure
As AI data centers grow in size and complexity, battery energy storage systems are being integrated directly into facility design rather than treated as auxiliary components. Many large data centers already deploy battery-based uninterruptible power supply systems, but the scale of these installations is expanding rapidly. Modern facilities may use thousands of battery modules arranged in large energy storage banks capable of delivering megawatts of instantaneous power.
Lithium-ion batteries have become the dominant technology in this space due to their high efficiency, compact footprint, and long cycle life. Within the lithium-ion family, LFP chemistry has emerged as a preferred solution for large stationary energy storage systems. These batteries offer several characteristics particularly suited to data center environments. They are inherently more thermally stable than many other lithium chemistries, reducing the risk of thermal runaway and improving operational safety. Moreover, they also support thousands of charge and discharge cycles, making them suitable for long-term infrastructure deployment.
Prismatic LFP cells are especially attractive for large installations. Their rigid rectangular format allows for dense packing within battery racks while maintaining consistent thermal management and structural stability. These characteristics help data center operators deploy large battery arrays that remain reliable over many years of continuous operation.
Balancing Increasingly Dynamic AI Loads
Another important function of batteries in AI infrastructure is managing fluctuations in electricity demand. AI workloads can generate sudden spikes in power consumption as training jobs start or inference workloads rise. This can create stress both inside the data center and across the surrounding power grid. Battery energy storage systems can absorb these fluctuations by rapidly charging or discharging to maintain stable power flows.
Hence, some AI data centers can dynamically adjust their electricity consumption in response to grid conditions, temporarily reducing their load when needed to support overall grid stability. This kind of flexible energy management becomes far more effective when combined with large-scale battery storage systems capable of responding within seconds. By integrating batteries with advanced energy management systems, data centers can behave like active participants in energy markets rather than passive electricity consumers.
Energy Innovation as a Foundation for Future AI Infrastructure
The rapid expansion of AI infrastructure is redefining the relationship between computing and energy systems. Data centers are evolving into large industrial energy consumers whose reliability depends not only on computing hardware but also on sophisticated power management architectures. Meeting the energy needs of AI will require continued investment across the electricity ecosystem. Batteries are expected to play a central role in this transformation by providing the flexibility and resilience needed to support highly dynamic computing workloads. As AI continues to expand across industries, the infrastructure supporting it will increasingly resemble a hybrid of digital and energy systems. Ensuring that these systems remain reliable, efficient, and scalable will be essential to sustaining the next phase of AI development.
Advancing Next-Generation Energy Storage with Addionics
As battery systems become central to AI infrastructure, improving their performance, durability, and efficiency is more important. Large-scale energy storage must operate reliably under heavy cycling conditions while maintaining strong conductivity, effective thermal management, and long operational lifetimes.
Addionics approaches this by rethinking battery architecture at the system level rather than focusing on a single component. The Smart Porous 3D Current Collector replaces traditional flat metal foils with a porous conductive structure. This significantly increases internal surface area, improving electron transport and enabling more efficient charge and discharge behavior. The innovation extends beyond the collector, with Addionics combining AI-driven cell architecture design, advanced metals engineering, and scalable manufacturing integration to develop batteries optimized for the specific systems they power. This approach is now being applied in collaboration with PNT Materials to produce cost-effective LFP prismatic cells, directly addressing the high energy demands of AI data centers, renewable energy, and grid-scale storage.
This architecture-driven approach creates a continuous feedback loop from design to operation and ultimately to end-of-life performance data, enabling ongoing improvements over time. For large energy storage systems supporting AI infrastructure, these capabilities translate into more efficient power delivery, stronger thermal stability, and longer operational lifetimes. By redesigning how battery systems are engineered and integrated, Addionics is helping build the energy architecture required to support the next generation of computing infrastructure and other demanding technologies where reliable, scalable energy is fundamental. Through this partnership, Addionics leverages industrial-scale manufacturing to deliver high-performance LFP cells, translating its breakthrough 3D Current Collector technology into real-world energy solutions.
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