EXPOSED
The core work — disassembling motors, rewinding armatures and stator coils, replacing bearings and brushes, testing insulation with meggers, balancing rotors, reassembling and bench-testing power tools — is manual dexterity inside grimy, non-standard machines that no robot can handle economically. AI touches only the thin paperwork and diagnostic-lookup layer. The real threat to this occupation isn't AI, it's the replace-rather-than-repair economics of cheap imported motors and tools, which has already shrunk headcount to ~14,000.
Part 2020 shock, part continued decline in the years since.
Median pay $44,070 → $56,210 +2.0% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+3.4%
Percentage only. The projection counts a different population from the 14,450 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +3.4% more of these jobs by 2034, and at 60/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~1,700 openings a year on average, including replacing people who leave.
CalibratorTool MasterCell ChangerSaw RepairerTool ChangerCell RepairerDynamo TenderTool RepairerCell InstallerLathe MechanicMotor MechanicTool InspectorBattery ChargerDynamo RepairerElectric WinderMotor RebuilderTest TechnicianTool TechnicianBattery MechanicBattery RepairerElectro-MechanicMagneto RepairerSalvage RepairerStarter Mechanic
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Stripping burnt varnish off a 40-year-old stator, counting turns on a coil nobody has a winding diagram for, pressing bearings without scoring the shaft, and megger-testing insulation to ground are one-off physical judgments on machines that arrive with no two alike — hence 17 rather than a 20, because the growing share of your day spent on parts lookup, warranty forms, and standard brush/switch swaps on identical cordless tool models is genuinely scriptable.
Hands-on in uncontrolled environments You work bent over a bench or on the floor beside a 200 lb frame, hot dip-and-bake tanks, arbor presses, and live 480V test panels, plus field trips to pull a motor off a pump skid in a plant — the only thing keeping this off a 20 is that the majority of the actual rewinding and reassembly happens in your own shop rather than up a tower or in a trench.
No licence, no signature requirement No state licenses electric motor repair; you may hold an EASA rebuild qualification or a journeyman electrician card if you also do disconnect-and-reconnect work, and NEMA/UL rules govern the motor, not you — the 4 rather than 0 reflects that hazardous-location (Ex/explosion-proof) motor repair does carry named-shop certification and traceable repair records.
Meaningful discretion You make the rewind-versus-scrap call, decide whether a slight bearing-journal wear means sleeve it or reject the shaft, and choose insulation class and winding configuration when the nameplate is unreadable — real money and a production line ride on it, but a wrong call surfaces on the test stand or within weeks, not as an ambiguous irreversible decision, which is why this sits at 12.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (17/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (9/20) is whether buyers specifically pay for a person. Judgment and accountability (12/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 25 of this occupation's 60 points (42%).
Embodiment (18/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 82/100 — SAFE.
Right-to-repair statutes with actual coverage of tools and motors — Colorado's HB24-1121 (wheelchairs/ag equipment), Oregon SB 1596, Minnesota's Digital Fair Repair Act — creating a legally protected parts-and-schematics supply so independent repair is a purchasable alternative rather than blocked by OEM parts denial. Trust premium rises only where a customer can actually choose repair.
Task-mix shift as the trade concentrates on large custom industrial motors, traction and mining motors, and EV/e-mobility drive units — where the call of rewind-vs-scrap, root-cause failure analysis (bearing currents from VFDs, insulation contamination), and re-rating decisions carry six-figure downtime consequences. The commodity power-tool bench tier is the part disappearing to replacement economics.
Adoption/enforcement of NFPA 70B (elevated to a standard in 2023) and NFPA 70E arc-flash requirements pushing insurers and facility owners to demand that motor repairs on >600V or hazardous-location equipment be certified by an EASA-accredited shop (EASA AR100 / ANSI-EASA rebuild standard), with a named technician signing the repair record. Explosion-proof motor repair already requires UL/CSA-recognized rebuilders to maintain the hazardous-location listing — extending that named-signer requirement to a wider voltage/class band would put personal accountability into the job.
Tariff or supply-shock driven price increase on imported motors and cordless tools, plus corporate Scope-3 / circular-economy procurement mandates that credit rebuild over replacement, flipping the replace-rather-than-repair math for industrial buyers. This is the single largest determinant of headcount for this SOC and it is economic, not technical.
State/OSHA or insurer rules requiring documented recertification of lifting-magnet, crane, hoist, elevator and mine motors after rewind, with the rebuild technician's certification on file as the basis for continued equipment operation.
Growth of high-voltage EV traction motor and battery-pack drive unit repair, requiring a technician qualified to work energized HV systems (NFPA 70E qualified-person status, OEM HV certification) to authorize return-to-service on a vehicle that will carry passengers.
The limit. task_resistance (17) and embodiment (18) are already near ceiling and cannot meaningfully rise. The binding constraint on this occupation is not AI capability but replacement economics: even with every lever above pulled, the score would rise while headcount continues to shrink. A high resistance score on a 14,000-person and falling occupation describes a safe job that fewer people hold.
| Houston-Pasadena-The Woodlands, TX | 890 | $61,000 +9% |
| Chicago-Naperville-Elgin, IL-IN | 660 | $114,670 +104% |
| Dallas-Fort Worth-Arlington, TX | 350 | $61,610 +10% |
| Denver-Aurora-Centennial, CO | 320 | $63,750 +13% |
| Los Angeles-Long Beach-Anaheim, CA | 300 | $66,040 +17% |
| New York-Newark-Jersey City, NY-NJ | 280 | $73,210 +30% |
| Tampa-St. Petersburg-Clearwater, FL | 270 | $46,200 -18% |
| Charlotte-Concord-Gastonia, NC-SC | 260 | $46,690 -17% |
| Chicago-Naperville-Elgin, IL-IN | 660 | $114,670 +104% |
| Milwaukee-Waukesha, WI | 100 | $81,810 +46% |
| San Francisco-Oakland-Fremont, CA | 80 | $79,330 +41% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 60. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.