The Buildings That Eat Electricity

2026-07-19 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Intermediate, short

Summary

Data centers are large facilities housing servers, storage, and networking equipment, with GPUs being critical for AI workloads. The United States leads globally with over 4,400 data centers projected by early 2026, while Germany and the UK follow with around 500-550 each. Key builders include AECOM, Turner Construction, and DPR Construction. Networking, supplied by Nvidia, Cisco, and Arista Networks, facilitates data movement within and out of these centers. Storage, provided by Dell, NetApp, and Pure Storage, balances speed and capacity with flash and hard drives. Data centers are energy-intensive, consuming about 415 terawatt hours in 2024 (1.5% of global electricity), projected to double to 945 terawatt hours by 2030 (3%). Cooling, managed by Vertiv, Schneider Electric, and Rittal, is crucial due to heat generated by chips, evolving from air conditioning to liquid and immersion cooling methods.

Key takeaway

For AI Architects or MLOps Engineers planning future infrastructure, recognize that data center energy and cooling requirements are rapidly escalating. Your infrastructure designs must account for a projected doubling of electricity consumption by 2030 and the necessity of advanced cooling solutions like liquid or immersion systems. Prioritize energy-efficient hardware and scalable cooling strategies to manage operational costs and ensure system longevity.

Key insights

Data centers, critical for AI, face escalating energy and cooling demands due to increasing chip power.

Principles

• AI workloads drive significant data center growth.
• Diverse storage types optimize cost and access speed.
• Cooling methods adapt to increasing chip heat density.

In practice

• Deploy GPUs for parallel AI computations.
• Implement liquid cooling for dense server racks.
• Mix flash and hard drive storage for varied workloads.

Topics

• Data Centers
• AI Infrastructure
• GPU Computing
• Energy Consumption
• Data Center Cooling
• Networking Equipment
• Storage Systems

Best for: Investor, CTO, VP of Engineering/Data, AI Architect, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.