The interconnect queue choking our grid

The race for AI dominance and the expansion of mission-critical computing have hit a physical wall. Developing software takes weeks, but developing the physical infrastructure required to run software often takes years. Facing a dual crisis, organizations are stuck between (1) a legacy utility grid that cannot scale to meet high density power demands and (2) a desperate need to be able to monitor energy and compute systems at scale, as both such systems include hundreds of thousands of devices that rely on real-time observability.

As of 2026, the American power grid is oversubscribed. Requiring impact studies and regulatory approval before they can be built, electric transmission system operators flounder in a years-long waitlist to connect to the US energy grid (hence interconnect queue). The situation has gotten so bad that some utility providers have stopped accepting new interconnect applications. This leaves data center operators in an unacceptable multi-year waiting list for electricity. When energy delivery is delayed and the system’s operational health is obscured by fragmented data, innovation stalls. Working together to eliminate these barriers, Voxel and Sift are powering next generation grids to unblock compute.

Unblocking AI’s energy constraints with Voxel’s energy infrastructure

Relying on traditional utility interconnect queue timelines can delay a project by five years on average (seven if you’re in California). Voxel Energy bypasses this bottleneck by delivering grid independent, direct current (DC) native energy infrastructure purpose-built for the specific demands of AI data centers.

Traditional power systems are trapped in a cycle of inefficiency. When solar, batteries, and computers (all DC components) are connected via an alternating current (AC) grid, they require at least five to six conversion stages. Each conversion stage generates heat and wastes energy, which increases energy use and total costs. Building a localized, DC native system from first principles, Voxel eliminates this conversion tax entirely. Whereas legacy approaches could lose as much as 30 percent of total energy flows due to conversions, Voxel’s technology only loses roughly 4 percent. If the entire American commercial sector adopted Voxel’s electricity approach, cost savings could run to almost $70 billion. By removing redundant components and monolithic failure points, Voxel delivers a more sustainable, reliable, and efficient energy supply.

Transforming Voxel’s grid with Sift’s analysis and predictive maintenance

But reliable power is only half of the equation. As AI data centers scale the number of devices involved, the volume of operational data they produce becomes overwhelming. AI workloads are notoriously volatile and power demand can surge by over 50 percent in a matter of seconds. Sift augments the inherent stability offered by Voxel's novel energy system by helping engineers gauge battery resilience characteristics based on real world data.

With features like automated data review and stateful analysis, Sift transforms raw telemetry into actionable insights. Unifying Voxel’s data from every layer of the stack, all the way down to individual battery cell modules, Sift creates a single source of truth. No more siloed data streams. This allows Voxel operators to validate the efficiency of the DC architecture and detect microscopic anomalies, such as subtle voltage drops or thermal drift. Like never before, teams like Voxel can service their physical hardware with the same agility as their software, all because of Sift.