From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
For over a century, the electric grid has relied on a single statistical assumption: \emph{load diversity}, the principle that the uncorrelated demands of millions of small consumers produce a smooth, predictable aggregate. AI training data centers break that assumption. A single hyperscale training campus can draw power comparable to a mid-sized city, driven by one tightly synchronized job whose demand swings by hundreds of megawatts in seconds. This paper argues that the resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co-development between the historically decoupled data center and electric power industries. We introduce...
| Authors | Noman Bashir, Rob Sherwood, Le Xie et al. |
|---|---|
| Primary category | cs.DC |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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