Where the methodology applies.
These are reference points, not a claim that any domain is solved. Implementation happens in Atlas.
Eight domains the architecture is designed to generalize across.
Extraction, processing, and refining capacity for materials used across batteries, electronics, and defense.
Generation, storage, and grid infrastructure supporting industrial and compute load growth.
Compute, power, and thermal systems underlying large-scale model training and inference.
Fabrication, packaging, and substrate capacity across the chip supply chain.
Routing, logistics, and chokepoint dependencies across global trade networks.
Capacity, tooling, and process constraints across industrial production.
Input supply, processing, and distribution systems underlying food production.
Availability and infrastructure constraints relevant to industrial and agricultural siting.
Where the methodology has been implemented.
Battery-grade graphite supply — source regions, processing bottlenecks, and siting questions relevant to Southern California / Imperial Valley — implemented in Atlas as the first reference deployment of the DT4I methodology.
Open in Atlas ↗Not yet implemented
- AI Infrastructure
- Battery Materials
- Industrial Logistics
- Data Centers
Constraints the methodology is built to map.
China refines the large majority of battery-grade anode graphite. Any disruption in processing capacity, not just mining, propagates into cell production.
Substrate and interposer capacity has not scaled at the same rate as accelerator demand, creating a packaging-side bottleneck independent of wafer output.
Multi-year lead times on large power transformers are a known constraint on new data center interconnection timelines.
Concentrated refining and magnet-manufacturing capacity outside the mining stage itself is the more binding constraint.