Critical Load: What’s next for data centres and AI?
In the finale of the 'Critical Load' miniseries, leading infrastructure investors evaluate data center unit economics, return profiles, and platform exit mechanics across global markets. The panel examines the shift from AI model training to low-latency inference, rising construction execution risks from inexperienced market entrants, and innovative bridging power and cooling solutions to resolve grid and water bottlenecks.
- Andrew Thomas projects that AI inference compute demand will expand to several multiples the physical footprint and capacity of training clusters. Read →
- Alexey Teplikhin warns that generalist sponsors entering data center megaprojects lack the engineering and supply chain capabilities required for mission-critical builds. Read →
- Large hyperscale data center platforms have grown too massive for standard sponsor-to-sponsor sales, making public listings an eventual route despite friction. Read →
- Standard grid interconnection queues now stretch beyond the 12- to 36-month delivery windows demanded by major cloud and enterprise tenants. Read →
- Hyperscale campuses require 100 megawatts or more of capacity, with build costs running from $10 million to $15 million per megawatt in the United States. Read →
- Direct-to-chip liquid cooling is replacing hybrid adiabatic and air-cooled setups as rack densities increase, altering unit cost profiles for high-density compute assets. Read →