The AI Geography Era
Why the transmission medium became an architectural decision, and why the speed of light now defines the geography of AI.
Power, not compute, now determines where AI factories can be built. Operators deploy where power can be secured, so what once would have been a single giga-facility is becoming several interconnected campuses.
The training architecture does not change simply because the compute is farther apart. In tightly synchronized distributed training, each step requires results to be exchanged and reduced before the system can advance. The slowest contribution sets the pace.
When the system spans campuses, the time it takes light to travel between them becomes part of the critical path.
This paper sets out the architecture of that constraint: the synchronization budget a distributed design has to spend, the Propagation Tax every kilometer charges against it, and why only the medium can change the price.
- Why power availability is forcing AI infrastructure across geography
- The Propagation Tax, defined and quantified against a fixed synchronization budget
- How lower propagation latency converts directly into greater viable build territory
- Where Scale Up, Scale Out, and Scale Across begin and end
- Why laboratory latency is not enough: what it takes to build a deployable, carrier-grade route
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