Published: April 13, 2026

The defense manufacturing workforce conversation has focused, understandably, on filling empty seats.  But there is a parallel problem that gets far less attention: what happens to the knowledge inside the heads of the workers who are leaving.

The workers closest to retirement are also the workers who know the most about how things actually get built.  They carry decades of accumulated judgment about tolerances, tooling sequences, material behavior, and workarounds that exist nowhere in a manual.  When they leave, that knowledge leaves with them.  And the programs designed to replace them are focused almost entirely on teaching technical skills, not on capturing institutional memory.

The Demographic Setup

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The numbers are not subtle.  Bureau of Labor Statistics (BLS) data show that across all production occupations, about 1.9 million workers out of 7.9 million—24%—are age 55 or older.  In occupations critical to defense, the concentration is far higher.  Among tool and die makers, 13,000 out of 29,000—nearly 45%—are 55 or older.  The median age for machinists is 45.7 years.

U.S. Census Bureau research published in December 2025 adds a firm-level dimension.  Using Business Dynamics Statistics matched to administrative records, Census found that the share of manufacturing employment at firms where at least a quarter of workers are over age 55 rose from 14% in 2006 to over 40% in 2022 (see note, below).  That is a structural transformation in the age profile of the manufacturing workforce, and it happened in less than two decades.

The BLS projects roughly 34,200 machinist openings per year through 2034, all driven by replacement demand.  The occupation is not adding jobs.  It is replacing workers who leave faster than the system can backfill them.

What Gets Lost

There is a useful distinction between explicit knowledge—the kind that can be written down in standard operating procedures, work instructions, and training curricula—and tacit knowledge, the kind that lives in the judgment and muscle memory of experienced workers.  Training programs are reasonably good at transferring explicit knowledge.  They are almost entirely incapable of transferring tacit knowledge.

In defense manufacturing, tacit knowledge matters enormously.  A veteran machinist who has spent 20 years working titanium forgings for aircraft landing gear knows what the cut sounds like when the tool is about to fail, how the material behaves differently in summer humidity versus winter dryness, which dimensions on the drawing are genuinely critical and which carry tolerance that was never tightened after the prototype phase.  None of that is in a work instruction.

The submarine industrial base offers a vivid illustration.  The Navy needs between 100,000 and 140,000 skilled trade workers over the next decade to meet production and maintenance targets.  Some shipyard workforces now report that a majority of their employees have fewer than five years of experience, resulting in reduced efficiency and increased quality oversight.  These workers are not unqualified.  They simply have not had enough time to develop the judgment that comes from repetition and mentorship, and the senior workers who could accelerate that development are retiring faster than knowledge transfer programs can capture what they know.

Why Current Programs Miss This

Federal workforce programs, including those administered by the Department of War (DoW), have invested significantly in training pipelines.  But most are designed around a training-to-placement model: recruit, train, place.  The model assumes that once a worker is employed, the employer will handle the rest.

For many employers, especially the small and mid-size subcontractors that form the backbone of the defense supply chain, “handling the rest” means throwing new hires into production and hoping they learn from whoever is on the next machine.  Structured mentorship and succession planning are rare at firms with 50 to 200 employees.  These companies do not have training departments.  They have production supervisors who are also doing quality, scheduling, and trying to keep their most experienced workers from retiring.

The result is a predictable cycle.  A senior worker retires.  The replacement has technical fundamentals but lacks the production-specific judgment that only comes from time on that particular line, with that particular equipment, making that particular product.  If the replacement leaves before reaching full proficiency—and, as we noted in an earlier article titled The Retention Problem Nobody’s Talking About, tenure data suggests many do—the cycle restarts and the knowledge gap widens.

What Would Help

The argument is not that existing programs are failing.  It is that they are solving one problem while a different one grows unchecked beside it.  Three interventions would help.

First, fund overlap, not just replacement.  The most effective knowledge transfer happens when an experienced worker and a newer worker operate side by side for an extended period.  Small defense suppliers cannot afford to carry two workers on the same job for six months.  Federal workforce grants could subsidize overlap periods explicitly, covering a portion of the senior worker’s wages during a structured transition window.  Some primes already do this internally.  The supply chain does not.

Second, integrate knowledge capture into Smart Manufacturing initiatives.  DoW has invested heavily in digital manufacturing and Smart Manufacturing through the Manufacturing Innovation Institutes.  These programs focus on process data, machine data, and digital twins.  What they do not capture is the human decision layer: why a veteran machinist chose a particular approach, what they looked for when inspecting a part, what experience taught them that the textbook did not.  Adding knowledge capture to these investments does not require a new program.  It requires expanding the scope of existing ones.

Third, support phased retirement.  Many experienced workers would transition gradually to part-time mentorship if the economics allowed it.  But pension structures, health insurance eligibility, and scheduling practices create a binary choice: full-time or gone.  Policy changes that make reduced-hour mentorship roles viable would extend the window for knowledge transfer without requiring senior workers to stay on the production floor indefinitely.

None of these ideas are new.  The challenge is that the urgent problem—filling empty seats today—crowds out the important one—preserving the knowledge that makes those seats productive.  The defense industrial base can survive a hiring drought.  It will have a much harder time surviving a knowledge drought.

Note: The Census Bureau’s 14% to 40% finding refers specifically to the share of manufacturing employment at firms where at least 25% of workers are over 55, not the share of firms.  This is a firm-weighted employment measure, which means large firms with aging workforces drive the number.  The distinction matters because many small manufacturing startups have younger workforces, but they employ fewer total workers.

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