5 data center trends to watch in 2025

Rise in AI and energy demand

The soaring popularity of AI tools, like ChatGPT, and cryptocurrencies is projected to continue to increase data center energy consumption. The creation of digital currency and chatbots requires space to store their capabilities. AI is only in an infant stage, with more expansion to come.

Although AI tools are a driving factor in the construction of hyperscale data centers, they are widely used to monitor energy, resources and operation use within facilities. Companies such as Meta, Microsoft and Nokia use AI tools to monitor real-time statistics, like energy consumption data, to actively reduce carbon footprints.

Advancements in AI tools reduce the need for manual work from data center operators. Since AI tools can predict errors and perform management tasks, data center operators have more time to spend on strategic plans for advancements in areas such as sustainability and equipment management.

According to Berkeley Lab's "2024 United States Data Center Energy Usage Report," data centers consumed about 4.4% of total U.S. electricity in 2023. By 2028, they are expected to consume 6.7% to 12% of total U.S. electricity, partly due to future advancements in AI.

Hyperscale data centers

Hyperscale data centers are becoming even more popular due to the ever-rising demand for storage. They are the largest and most efficient data storage facilities used by tech-centric companies, such as AWS and Google, that house over 5,000 servers. They have more physical space and use specialized high-density server racks to maximize server capacity.

A hyperscale data center can occupy hundreds of acres of land, while a standard -- 40-plus megawatt -- data center occupies about 10 acres. Existing standard data centers can undergo hardware upgrades to house more power, minimize land use and conserve building materials. Upgrades to high-power AI chips, servers and high-density racks maximize storage space and performance and keep standard data centers in use for longer.

Companies are also recycling older chips and hardware to assist in the production of new technology and servers. These upgrades significantly transform existing data centers to enhance their capacity and efficiency.

Although hyperscale data centers consume an incredible amount of power, companies that own these facilities use more efficient energy sources and recycling systems than most traditional data centers. Lithium-ion uninterruptible power supply batteries, nuclear energy, solar power, wind power and natural gas are available resources to reduce fossil fuel consumption.

Energy sustainability

To decrease the degree and pace at which global temperatures are rising, the United Nations created a net-zero commitment plan that must be met by 2050. This plan pressures countries worldwide to develop and follow guidelines to cut out all greenhouse gas emissions by the deadline to stop the 1.5 degrees Celsius increase in global temperature. This plan encourages the use of solar and wind resources to replace coal, gas and oil.

The 2023 United Nations Climate Change Conference also announced the Global Cooling Pledge. This pledge aims to decrease cooling emission rates by 68% by 2050. Strategies to meet this goal include improving cooling system efficiencies and increasing the use of natural refrigerants, such as water and air. Building data centers in cold environments can also reduce the need for cooling equipment.

A shift from the linear economy to the circular economy has also started. In this economy, companies recycle, reuse and refurbish older technology rather than throw material away. If parts are not useful to one data center, they can be sold to another. This creates a cycle of nonrenewable resources, like gold, silver and copper, to be recycled into new technologies, extending the material's lifespan with technological advancements.

Liquid cooling advancements

AI chip advancements have led to a manufacturing boom within AI chip companies. More advanced chips are usually released yearly, and each one can handle more than the last. Servers that use AI chips have a greater chance of overheating since more computational power is required to run increased workloads. Cooling technologies are changing to decrease energy use with more efficient methods, like direct-to-chip and immersion cooling.

Direct-to-chip liquid cooling decreases heat production at the source: the AI chips within the server. This cooling method uses liquids, such as water, dielectric fluids or propylene glycol-based fluids, to pass through a plate on top of the hardware chip. The heat from the AI chip quickly dissipates when in direct contact with a cooled surface.

The immersion cooling method takes a larger-scale approach and uses dielectric liquid to surround entire servers or racks. With proper handling, hardware is put into a sealed tank full of circulating dielectric liquid where heat is absorbed and transferred to a connecting cooling system.

Direct-to-chip and immersion cooling methods are more confined than traditional cooling systems that cool the entire facility. The direct cooling contact AI chips and hardware have with these newer methods enables faster and more efficient heat control. However, these methods do not entirely replace the need for traditional cooling methods.

Edge computing

Edge computing is gaining popularity as it has faster data speed responses and reduces latency compared to cloud computing. The rising popularity of edge computing is also due to lower costs for companies as cloud computing prices and subscriptions start to soar. With edge computing, data is processed in real time and does not connect to a cloud server.

Since data is stored closer to the original source of its generation, it has less distance to travel to connect with company servers than if stored in a data center hundreds of miles away. With shorter travel distances to the network, edge computing uses less energy than a permanent data center to transfer data and maintain the environment.

The foreseeable growth of AI and IoT is evidence for analysts to predict a rise in edge computing through 2030. By 2030, the edge computing market is expected to reach over $155.9 billion, from a value in 2023 of $16.45 billion, according to Grand View Research.

Kelly Richardson is site editor for Informa TechTarget's Data Center site.