Among the predictions about the impact of artificial intelligence on data centers are a few salient statistics. Sanjay Bhutani, AdaniConneX chief business officer, notes that AI will drive data centers from 54 gigawatts to perhaps 90 Gwatts.
Likewise, Gautham Gnanajothi, Frost and Sullivan global VP estimates data center investment will grow from about $300 billion to $775 billion over the next 10 years, of which the largest-eight hyperscalers currently represent about $110 billion in annual investment out of the total of $300 billion globally.
Capex estimates vary from firm to firm, depending on assumptions about growth rates. Generally speaking, earlier studies show lower expected capex in 2033, compared to more-recent studies that include assumptions about additional requirements to support AI.
If the same ratios hold in a decade, hyperscalers will be investing about $284 billion, while other data centers invest about $496 billion, using the Frost and Sullivan estimate made by Gnanajothi.
On the other hand, not all of the AI impact will necessarily involve “more” commitment of resources. Since AI model training does not require the same level of redundancy as do other operations, it is possible that AI training will represent less resource intensity than other types of operations, Gnanajothi suggests.
Also, the denser footprint AI represents might also mean less proportional demand for land and building space, compared to existing operations.
Also, AI training operations might not always be so latency dependent, though inference operations might often require edge computing, says Phillip Marangella, EdgeConnex chief marketing officer.
And data centers, just like any other enterprise or organization, should be able to use AI to improve the efficiency of its operations. In fact, says Bhutani, AdaniConnex already uses AI to improve safety operations when it is building or operating a facility.
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