Goldman Sachs sees hyperscaler AI spending topping $1 trillion in 2027 as data center capacity doubles by 2030
Goldman Sachs expects hyperscaler AI spending to surpass $1 trillion in 2027 as workloads require more compute, memory, storage, and connectivity. Global data center capacity could nearly double by 2030 driven by surging AI demand and cloud expansion. Micron's earnings could serve as an indicator of data center infrastructure growth.
Goldman Sachs expects hyperscaler AI spending to surpass $1 trillion in 2027 as workloads require more compute, memory, storage, and connectivity [1]. Global data center capacity could nearly double by 2030, driven by surging AI demand and rapid cloud expansion [1].
Micron's earnings could serve as an indicator of data center growth, with some of the largest beneficiaries found inside the rack, including providers of chips, storage, and networking solutions [1]. The projection marks a sharp acceleration from current hyperscaler capital expenditure levels, reflecting the computational intensity of training and deploying large language models and other AI workloads at scale.
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