
Jefferies says Palm Beach County's rejection of a proposed 600 MW AI campus shows local permitting could determine whether AI load materializes.
Palm Beach County rejected a proposed 600 MW AI data center campus despite the developer making numerous redesigns and obtaining approval from planning staff, suggesting that local permitting could prevent announced AI projects from actually materializing into real electricity demand. Investment analysts argue this denial exposes a significant risk for utilities and Wall Street investors: the biggest uncertainty for AI infrastructure may no longer be securing available electricity but obtaining community and local government approval. Several other communities in different regions have also challenged large data center proposals over concerns including noise, water use, and land compatibility, though many projects have moved forward after modifications. The implications are substantial because utilities have historically focused on whether the grid can support new demand, but now must also assess whether proposed projects can secure sufficient local support to reach the grid at all.

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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