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On October 22, 2026, Infosys released an analysis arguing that data centers can free up near-term computing capacity for AI by improving energy efficiency and scheduling flexible loads. The analysis also notes that such demand-side measures are more likely to delay rather than eliminate the need for new generation, transmission, and grid upgrades. The report distinguishes between dispatchable and non-dispatchable loads: model training, retraining, and batch processing can be scheduled around grid conditions, while real-time inference and customer-facing applications typically cannot. The report recommends phased, modular construction, combined with quantization, pruning, and knowledge distillation to reduce computing demand; utilities, meanwhile, need verifiable evidence such as historical performance, real-time monitoring, and contractual commitments. The analysis is currently reported by Data Center Knowledge, and there are no subsequent developments of fact.
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Infosys: Data Centers Can Delay but Not Avoid Power ShortagesDevelopments
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Infosys analysis suggests demand-side measures can free up AI computing capacity in the short term
Source:Data Center Knowledge
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Oct 22
Data Center Knowledge
Infosys: Data Centers Can Delay but Not Avoid Power Shortages
Infosys analysis suggests that data centers can free up near-term capacity for AI by improving energy efficiency and scheduling flexible loads, but this is more likely to delay rather than eliminate the need for new generation, transmission, and grid upgrades. Model training, retraining, and batch processing can be scheduled around grid conditions, while real-time inference and customer-facing applications typically cannot. The report recommends phased modular construction, combined with quantization, pruning, and knowledge distillation to reduce computing demand; utilities, meanwhile, need verifiable evidence such as historical performance, real-time monitoring, and contractual commitments.