…through Salesman Spotting Lens …
Gartner famously predicted that by 2025, 80% of enterprises will have shut down their traditional in-house data centers. I got fascinated by a recent study on global data centre market which is projected to reach $1 trillion in next 3 years, largely driven by AI and digitalization. McKinsey projects that global data centre capacity needs could grow 19-22% annually through 2030, potentially tripling total capacity. By 2030, 70% of data centre capacity demand may be for hosting advanced AI workloads. #GenerativeAI alone could account for 40% of total demand by 2030 and we are yet to unleash the power of #AgenticAI.
1. Let us understand 1st how is #datacentercapacity measured?
Data center capacity is primarily measured in megawatts (MW), representing the total power available for IT equipment and infrastructure. Other factors like physical space (square footage), cooling efficiency, and network connectivity also contribute to overall capacity, but power consumption remains the key metric for determining a data center’s capability.
2. How is this growth expected ?
Simply put more gears in AI data center – Higher Density and Power – Average rack power density is rising, with a growing number of operators deploying >15 kW racks. This is driven by AI and HPC gear. Sustainability & Efficiency are Competitive Metrics to track.
Surging demands and evolution from traditional data center to AI data center is bringing this growth
3. Now let us understand what is AI Data Center ?
AI data center is a type of data center specifically designed to handle the massive computational and storage demands of artificial intelligence workloads with use cases ranging from Chatbots, computer vision, big data analytics. It’s a high-performance facility optimized to run AI models, process big data, and support machine learning operations at scale. These centers are packed with specialized hardware like GPUs, TPUs, and high-bandwidth interconnects—far beyond the standard CPUs used in traditional data centers.
- Main Workloads – Training and running AI/ML models as against Web hosting, storage, apps in traditional data centers.
- Power Needs –Extremely high due to GPU-heavy workloads.
- Advanced – Liquid cooling and immersion etc.
- Network – Ultra-low latency, high throughput.
Despite rapid expansion, uptime remains paramount and a #Tier4 data center means only 26.3 minutes of downtime per year. Can you imagine ? How many times we have failed
- #Tier4: Fully redundant (2N), fault-tolerant design, 99.995% uptime.
- #Tier3: Multiple distribution paths, allowing maintenance without downtime, 99.982% uptime (Only about 1.6 hours of downtime per year).
- #Tier2: Some redundancy (N+1) but a single distribution path, 99.741% uptime (about 22 hours of downtime per year).
- #Tier1: Basic infrastructure with no redundancy, 99.671% uptime (about 28.8 hours of downtime per year).
Question to answer – How do we execute or who will grab a pie here?Will we see traditional consulting and IT companies moving into as not many players are there but strategic partnerships with hyperscalers, NVIDIA, and local infrastructure players and having access to barren land with cheap power and cooling techniques with differentiation via ESG leadership (e.g. using Geo-Thermal and Arctic Air), AI readiness, and smart city integration with a focused expansion into India, SEA, Japan, Europe, and Middle East will be the winner here.
#Timewilltell.
