
A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.
Will the US Congress introduce federal legislation on AI data center grid reliability by October 31, 2026?
Resolves by Oct 31, 2026
A power line failure outside Washington, DC revealed how large concentrations of data centers can destabilize the electrical grid. When the power line went down, more than 3 gigawatts of data centers switched to backup power nearly simultaneously, causing their load to drop from the grid within seconds and creating voltage spikes across a wide region that made lights flicker. The grid requires near-perfect balance between power supply and demand, and when many data centers disconnect at the same time, they create dangerous imbalances that can trigger failsafes. Experts warn these events will become more frequent as data centers account for a growing share of grid demand, with projections showing they could represent 24 percent of demand by 2040, compared to about 6 percent currently.

In this tutorial, we explore TileLang as a high-level Python domain-specific language for designing and compiling performance-oriented GPU kernels through TVM. We begin by validating the CUDA environment and establishing reusable benchmarking and numerical-verification utilities, then progressively implement vector addition, tiled tensor-core matrix multiplication, schedule exploration, fused GEMM epilogues, row-wise softmax, and FlashAttention. Throughout the tutorial, we work directly with Ti

As AI infrastructure grows more complex, companies are rethinking how they acquire, own, and finance assets that operate on dramatically different economic timelines.
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Dominion says data centers transferred to backup power after a transmission line fault, creating one of the largest sudden load changes publicly reported on the US grid.
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