The distributed layer between the hyperscale core and the device.

Inference dominates compute. It is now the majority of AI compute and growing faster than training. The shift from building models to deploying them is structural.

Real time cannot round-trip. Voice, agentic sessions, inline security, and physical AI need local, sub-20 ms response. A distant cloud cannot deliver it.

Hyperscale is the wrong shape. It was engineered for centralized, batch-oriented training. Repurposing it for distributed inference is a rebuild, not a retrofit.

And it is stalled. Power and water scarcity, permitting backlogs, and community opposition hold large builds for years. The distributed inference layer remains unowned.

EC500 and EC1000. Pre-engineered 500 kW and 1 MW blocks on relocatable precast foundations. A facility starts with one and expands as demand is contracted. Capital follows commitment.

Sited on existing power. Co-ops, municipals, cell sites, investor-owned utilities, and RTOs where capacity already exists. Rates locked and interconnect expedited before a rack ships.

Nothing the neighbors object to. No community water draw. Minimal noise. Permitting expedited because the structure is relocatable.

Carrier-adjacent. Siting next to the carrier removes the backhaul hop. Bonded fiber with Starlink as diverse backup.