
A draft federal study concludes that AI data centers are now the dominant driver of electricity demand growth, compelling planners to reconsider where and how the grid expands.
The Department of Energy's draft 2026 National Transmission Needs Study concludes that AI data centers have become the primary driver of electricity demand growth in the United States, fundamentally changing how grid planners design the nation's power transmission system. Historically, transmission projects were driven by reliability concerns and aging infrastructure, but rapid load growth from AI data centers now compels planners to reconsider where and how the grid expands. Projections show US data center electricity consumption could exceed 10 percent of total US electricity consumption by 2030, with data center demand growing at compound annual rates between 13 and 27 percent through 2028. For developers building AI campuses measured in hundreds of megawatts, transmission availability has become a critical site-selection factor, as having generation capacity in a region is no longer sufficient without the ability to deliver that power to the required location within required timelines.

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

A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.

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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