Maintenance and Repair — The Invisible Majority

In The You On AI Field Guide

Andrew Russell and Lee Vinsel, building directly on Edgerton's framework, published the 2016 manifesto Hail the Maintainers and extended the argument into The Innovation Delusion (2020). Their case extends Edgerton's empirical findings into a broader cultural and policy critique: innovation culture systematically undervalues the work that keeps civilization running, and the undervaluation produces concrete failures — collapsing infrastructure, accumulated technical debt, the erosion of skilled trades, the loss of institutional knowledge.

Applied to AI, the framework generates a sharp prediction: AI dramatically accelerates creation and may simultaneously degrade the capacity for maintenance. The imagination-to-artifact ratio collapses, and artifacts proliferate. Each artifact requires maintenance. The maintenance burden grows faster than the maintenance capacity, because the skills that maintenance requires — patience, contextual understanding, the accumulated knowledge of how systems behave under real conditions over real time — are precisely the skills that the removal of friction atrophies.

The asymmetry is already visible in the software industry. Engineers who have used AI coding assistants for six months report declining ability to debug manually. The tolerance for the slow, painstaking work of diagnosis erodes as the tools provide faster alternatives. But the alternatives work only when the problem is common enough to be in the training data. When the problem is novel — when it arises from the specific, local, idiosyncratic conditions of this particular system's history — the maintainer's embodied knowledge is the only resource that can diagnose it.

Ruth Schwartz Cowan

Edgerton's framework intersects with tacit knowledge in important ways. Maintenance requires what Ruth Schwartz Cowan called embedded knowledge — understanding that lives not in documentation but in the specific, local, accumulated experience of the people who have been tending the system. The maintainer knows that this particular server crashes under load on the third Tuesday of the month because of an undocumented cron job. The maintainer knows that this particular function was written in a non-obvious way because the obvious way triggered a race condition that took three engineers six weeks to diagnose. This knowledge is precisely the kind that the engineer in You On AI Chapter 10 describes losing — the architectural intuition that erodes when Claude handles the plumbing.

Origin

Edgerton's documentation of maintenance as a category of analysis began with his work on twentieth-century British military history, where the ten-to-one ratio of maintenance personnel to combat personnel during the Second World War became impossible to ignore. The systematic generalization came in The Shock of the Old, particularly the chapter on maintenance, which has become one of the most-cited foundational texts in the contemporary maintainers literature.

Key Ideas

Seventy-thirty. Roughly seventy percent of all labor performed with technology is maintenance, not creation; the ratio is consistent across domains and eras.

Invisibility is structural. Maintenance is invisible because it is mundane and continuous, while invention is dramatic and discrete; the asymmetry of attention follows the asymmetry of narrative drama.

AI accelerates creation, not maintenance. The collapse of the imagination-to-artifact ratio produces more artifacts, each of which requires maintenance, while the skills required for maintenance atrophy under conditions of frictionless creation.

Embedded knowledge is the foundation. Maintenance requires specific, local, accumulated knowledge that does not transfer through documentation and that AI systems cannot reproduce.