As AI deployment scales up, especially for large models and data-intensive workloads, one of the **least-discussed bottlenecks isn’t compute power or algorithms — it’s the energy infrastructure needed to power it, particularly the interconnection queue required to hook data centres up to the electrical grid. New AI data centre campuses often demand hundreds of megawatts to gigawatts of power — amounts that power grids were never designed to deliver quickly. Before a facility can draw electricity, it must pass through a formal interconnection queue process with grid operators that evaluates and approves connections to transmission infrastructure. In many regions, this queue is now years — often 7–8 or more — creating a major delay before a data centre can even begin operating despite having land, capital and compute ready.
The interconnection queue isn’t a technical limit on power generation alone — it’s a regulatory and procedural backlog rooted in how grid planning and approvals historically worked. These systems were designed for gradual growth in load — like new neighbourhoods or renewable projects — not for sudden applications seeking hundreds of megawatts at once. As a result, even projects with signed power agreements and construction ready to go can sit idle for years while utilities and regional grid operators conduct studies and coordinate upgrades to substations and transmission lines. This mismatch between AI demand timelines and grid planning timescales has effectively made the queue a choke point for broader AI expansion.
The delay matters because AI growth isn’t constrained by capital or chips alone — it is now intimately tied to how quickly electricity can be delivered and sustained. Hyperscale AI clusters require continuous, high-quality power; delays in grid access push developers toward temporary solutions like on-site generation (e.g., gas turbines) or smaller, behind-the-meter power systems. These stopgaps can be expensive, emissions-intensive, and fundamentally misaligned with long-term infrastructure planning. Meanwhile, the backlogs in interconnection queues can slow AI deployments, inflate project costs, and contribute to regional inequality in where AI infrastructure can realistically be built.
Addressing this hidden bottleneck will require policy reform and infrastructure investment that modernizes grid approval processes, expands transmission capacity, and aligns energy planning with the pace of digital infrastructure demand. Industry analysts and infrastructure planners argue that simply building more AI chips and data centre racks won’t be enough without equivalent upgrades in how power is distributed and connected. Bridging this gap could determine which regions and companies lead in the AI era, making energy access — and not just algorithms — a central factor in global technological competitiveness.
