From American Affairs Journal, September 10, 2026:
As an engineer, I ran maintenance for a Fortune 500 pulp and paper company. I was based at a plant in Tennessee and covered nine other facilities across the Southeast and Florida. For anyone unfamiliar with heavy industry, these plants are filled with massive, capital-intensive equipment: three-story machines stretching the length of a football field, motors running constantly, and forklifts moving across the floor to keep the plant fed with raw material. It is an environment built to run continuously. A single hour of unplanned downtime can destroy hundreds of thousands, sometimes millions, of dollars in lost production, depending on the line.
When critical systems failed, and the laws of entropy ensure they always fail, it was never a software engineer or a sleek, cloud-connected dashboard that saved the day. The facility relied entirely on technicians like “Craig,” a millwright with thirty years of accumulated tribal knowledge: the hyper-specific, undocumented expertise and nuanced understanding of legacy equipment that resides in the minds of veteran workers rather than in manuals or databases.
Craig was the kind of technician who could diagnose a failing bearing simply by the pitch of its squeal or the specific frequency of a vibration traveling through a steel walkway. He knew which parts had been replaced, which modifications had never made it into the drawings, and which repair in the manual no longer applied to the machine in front of him. Manuals capture only so much, and they capture it in two dimensions. A maintenance technician works in three, using sight, sound, touch, and years of trial and error. Maintenance is also time-sensitive. Craig could often diagnose a problem faster than a newer technician could locate the relevant drawing. The documentation existed, but during a breakdown it was frequently incomplete, outdated, or too slow to use. That way of working has a consequence. Because the knowledge lived in Craig’s head, recording it was never the priority. The plant did not feel an urgent need to document why a certain oil had been discontinued, when a control program had last been changed, or why a replacement belt differed from the one in the parts list. Craig already knew.
The difficulty of the equipment only compounds this problem. Much of America’s industrial base is brownfield infrastructure. The frames of some paper machines are a century old. Many machines have been modified so often that the original manual no longer describes what is on the floor. Like the ship of Theseus, the machine may retain its name even though nearly every important component has changed. But Craig is retiring. And nobody is taking his place. The disappearance of people like Craig is not just a “skills gap”; it is the central structural flaw in America’s current reindustrialization strategy.
Today, while I am removed from the immediate reality of that specific plant floor as an early-stage entrepreneur, I still speak regularly with factory managers and maintenance directors across the country. Beneath the macroeconomic optimism of the current manufacturing boom, I hear the same refrain in every conversation: “It is impossible to find quality guys nowadays.”
Simultaneously, Silicon Valley and Washington, D.C. find themselves unusually aligned on a singular narrative: America must reindustrialize. We must bring manufacturing home, secure critical supply chains, and rebuild the physical base of national power on American soil.1 The new consensus holds that this will require vast public subsidies and private capital expenditures, along with a new generation of “software-defined” factories.
The assumed solution to local labor shortages is straightforward: if we do not have enough frontline operators, then we should automate the production line. They are solving the obvious problem. Pushing more automation onto the floor can get the plant up and running without requiring a massive army of baseline operators to actively feed the machinery. Yet both the technocratic and venture narratives assume that automating production lines will solve our labor problem, as though the factory’s bottleneck were simply the number of hands on the assembly line rather than the number of people who can keep the machines alive.
Maintaining an industrial facility is an intensely labor-heavy endeavor. It requires highly skilled maintenance technicians—millwrights, industrial machinery mechanics, PLC programmers, electricians—who are in even shorter supply than the general blue-collar workforce because their roles demand technical schooling, apprenticeships, and years of hands-on experience.2
What policymakers and tech founders are missing is that their proposed solution is engineering a serious structural flaw into the reindustrialization roadmap. They are creating what we might call the Automation Paradox: the more you automate production to reduce your dependence on frontline labor, the more highly complex machinery you introduce into the physical environment. That machinery contains many more motors, proximity sensors, bearings, planetary gears, and pneumatic belts. In turn, one must maintain all these discrete components to keep the line running. Consequently, the automated factory requires highly skilled, specialized maintenance technicians, and the shortage of these specific mechanics is far more severe, and far more threatening, than the shortage of baseline assembly workers.
Automation so far has happened almost entirely on the production side of the factory, and there is a reason for that. Production work repeats itself and it is structured, which is what machines are good at. Maintenance is neither. It stayed manual while everything around it was automated. Production capacity can be expanded with capital; maintenance capacity still depends on how many experienced technicians live within driving distance of a plant and how many years they have spent learning the trade. The better we get at automating production, the more work lands on the one part of the plant that has no way to absorb it.
The Demographic Cliff and the Loss of Tribal Knowledge....
....MUCH MORE
