DESIGN & BUILD
In 2015, there were 259 hyperscale data centres worldwide. Today, there are over 1,136 facilities, and the market has grown into a US $ 167bn industry expected to reach US $ 600bn by 2030. But the real story isn’ t in the numbers – it’ s in how AI is forcing a complete rethink of data centre design.
Traditional enterprise data centres, built for 5-10kW rack densities, are hitting a wall. AI workloads need 50kW + power densities to run GPU clusters, and that changes everything. The cooling systems, the power distribution, the network architecture – none of it was designed for this kind of heat and computational load.
Building at a different scale Hyperscale facilities operate on a completely different level than typical data centers. Where enterprise facilities might house hundreds of servers consuming 1-5 MW of power, hyperscale operations deploy millions of servers across campuses that consume 20-100 + MW. Individual facilities exceed 10,000 square feet with at least 5,000 servers, though modern hyperscale campuses often span multiple buildings.
“ When we go through a curve like this, the risk of under-investing is dramatically greater than the risk of over-investing,” Google CEO Sundar Pichai said when announcing the company’ s US $ 75bn commitment to AI infrastructure in 2025 at the company’ s 2025 Next conference.“ Even if it turns out that we are overinvesting, these are infrastructure which are widely useful for us.”
Meta data centre in Clonee, Ireland
The cooling crisis and liquid solutions AI has created a thermal problem that traditional cooling can’ t solve. The heat generated by thousands of GPUs running AI training workloads requires completely different approaches to heat removal. Air cooling, which has dominated data centres for decades, becomes inadequate when individual racks consume 50kW or more – roughly equivalent to powering 40 typical homes.
This shift has transformed liquid cooling from a niche technology into a rapidly growing market projected
146 September 2025