Automation across diverse geographies.
A department of transportation owns one of the largest distributed physical estates in public hands, and the recurring work of keeping it open is the largest line in its budget. The question this topic asks is not whether Physical AI can take that work on. It is where on the trajectory each application sits, and which constraint is currently binding it, given that the answer changes with the ground the estate is built on.
The review is organised by terrain and climate class rather than by state, because the physical environment is what decides what the work actually is. An archipelago separates its assets with water. An Appalachian county carries steep grade, narrow valleys and, after Helene, a rebuild backlog. An Alaskan corridor closes for part of the year and the construction season is what bounds it. A tribal route network in the arid southwest runs very low route density over an area larger than ten states, much of it beyond grid power and cellular coverage.
The claim under test is that the binding constraint changes class with the terrain, which would make a single national automation case the wrong unit of analysis. Each hypothesis resolves to a position on the trajectory stated in degrees, a constraint named from the five technical classes or from regulatory and workforce where those bind, and the measured change that would move the boundary. Every figure carries a verification grade, and quantities this review could not locate are recorded as not located rather than asserted absent.
TR-2026-34 · in preparation · The Hiner Lab with The Charlot Lab
Michael Hiner, Ronnie Gomez and Grant Markhart, Industrial Research Fellows.