AI is Going to Change Data Centers

It seems clear that the amount of AI processing and workloads are going to grow quite substantially, assuming AI proves to be as useful as most of us now believe. It might be at the high end of estimates, but some believe AI operations will consume as much as 80 percent of data center power loads by about 2040. 

Other estimates also are significant. Digital Bridge CEO Marc Ganzi believes AI will mean a new or additional market about the size of the whole public cloud computing market, eventually. 

If the public cloud now represents about 13 gigawatts of capacity, AI might eventually require 38 gigawatts, says Ganzi. 

The whole global data center installed base might represent something on the order of 700 gigawatts, according to IDC. Other estimates by the Uptime Institute suggest capacity is on the order of 180 GW. 

According to a report by Synergy Research Group, the global public cloud computing industry now represents 66.8 gigawatts (GW) of capacity. 

According to a study by the Lawrence Berkeley National Laboratory, AI-driven data center electricity consumption could increase by 50 percent to 200 percent by 2040, posing new challenges for data center operators trying to limit and reduce carbon emissions and electrical consumption. 

Study	Year Published	AI-driven electricity consumption (GWh)	Increase over 2023 (%)
Lawrence Berkeley National Laboratory	2020	130	40%
Gartner	2021	200	50%
IDC	2022	300	75%
DigiCapital	2023	400	100%
Study	Year	Projected AI-Driven Data Center Electricity Consumption (2040)	Growth from 2023 (%)
Lawrence Berkeley National Laboratory	2018	10% of total data center electricity consumption	50%
Gartner	2020	15% of total data center electricity consumption	75%
IDC	2021	20% of total data center electricity consumption	100%

Of course, data center operators will continue to seek ways to reduce impact, as well. 

Study	Year Published	Energy Efficiency Savings (%)	Methods Used
Lawrence Berkeley National Laboratory	2020	20-30%	Using more energy-efficient hardware, optimizing the use of data center resources, and using renewable energy sources
McKinsey & Company	2021	30-40%	Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, and improving cooling efficiency
IDC	2022	40-50%	Using more energy-efficient hardware, optimizing the use of data center resources, using renewable energy sources, improving cooling efficiency, and deploying AI-powered energy management solutions

But there seems little doubt that AI model training and inference generation will become a much-bigger part of data center compute activities and therefore energy load. In some part, that is because bigger models require more data ingestion during the training process.