COOKED
The core of the job — looking up part numbers by VIN or model, checking fitment, quoting prices, taking orders, and processing returns — is exactly the catalog-lookup and classification work that software already does well, and e-commerce parts platforms have been eating it for a decade. What survives is physical: pulling stock from bins, receiving and shelving freight, inspecting cores and warranty returns, and standing at a counter helping a mechanic or DIYer figure out what actually broke. Median workers sit in retail and dealership counters where headcount per store keeps falling as online ordering absorbs the lookup volume.
Headcount grew steadily across the period.
Median pay $31,710 → $38,630 -2.5% 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
+3.1% 272,100 → 280,600 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3.1% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~30,200 openings a year on average, including replacing people who leave.
Parts ClerkSalespersonParts PersonParts PullerParts RunnerParts AdvisorParts SalesmanParts AssociateParts ConsultantParts CountermanParts SpecialistSales SpecialistParts CoordinatorParts SalespersonParts ProfessionalParts Counter ClerkParts CounterpersonParts Counter PersonParts Back Counter ManMerchandising AssistantParts Counter AssociateParts Counter SalespersonRetail Parts ProfessionalWholesale Parts Salesperson
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier A 7 rather than a 4 because VIN decoding, fitment checks, and price quoting are already handled by Epicor/PartsTech-style catalogs, but the residual — diagnosing what a customer means when they bring in a broken bracket with no part number, cross-referencing a superseded OEM number against three aftermarket brands, and pulling and staging the stock — still needs a person at the counter; a 12 would require most shifts to be non-catalog work, and they aren't.
Some physical or field component A 10 fits a job spent walking bins and back rooms, unloading hotshot deliveries, lifting rotors and batteries onto the counter, and physically inspecting cores for cracked housings or leaked fluid — real bodily work, but inside a lit, shelved, climate-adjacent store, not a roof or a trench, which is what separates this from 15+.
No licence, no signature requirement A 1 because no state licenses parts counter work; if you sell the wrong brake caliper, the store eats the return and the installing shop or manufacturer carries the failure exposure, and nothing about you personally is on file with a regulator.
Executes defined procedures on defined inputs A 5 because most calls are bounded by the catalog, the warranty policy, and a return window with a manager override above a dollar threshold — the discretion you do exercise, like accepting a marginal core or advising a customer to buy the OEM instead of the economy line, is real but low-stakes and reversible.
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 (7/20 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 (1/20) is whether the law requires a licensed human to sign. Trust premium (7/20) is whether buyers specifically pay for a person. Judgment and accountability (5/20) 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 13 of this occupation's 30 points (43%).
Embodiment (10/20) 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 43/100 — EXPOSED.
Counter work consolidating into hub-and-spoke distribution: if store counters shrink but regional DC pick/pack, core inspection, and hotshot delivery roles absorb the headcount, the surviving job is physical bin-pulling, freight receiving, and hands-on core/warranty inspection in cluttered non-standard stockrooms that pick robots don't handle at auto-parts SKU breadth (millions of part numbers, irregular packaging).
Task-mix shift to the diagnostic tier: the job genuinely has two tiers — catalog lookup, and the counter conversation where a DIYer describes a noise or brings in a broken part with no number. If e-commerce absorbs all clean-lookup volume, what remains at the counter is identify-from-fragment, supersession/interchange disputes, and fitment failures the catalog got wrong — work that requires holding the part.
Warranty and core adjudication authority: if manufacturers push more first-line warranty denial/approval and core credit decisions onto the counter person (as dealership parts departments already do for OEM warranty claims subject to manufacturer audit chargeback), the role owns money decisions under ambiguous evidence of abuse vs. defect.
Commercial account relationships: independent repair shops paying a premium to a specific jobber counter because the counterperson knows their bays, catches wrong-fitment orders before the car is on the lift, and eats the cost of a bad call — the existing basis of the commercial-vs-retail split at NAPA/CarQuest jobbers. This is a real but narrow premium and it does not extend to DIY retail.
The limit. No realistic liability_shield route — there is no license to sell parts and no statutory signature requirement anywhere in the US; that dimension stays near zero. The embodiment and task-mix gains are gains per surviving worker, not gains in total headcount: the occupation can become harder to automate while shrinking sharply. Realistic ceiling is low-to-mid 40s.
| Los Angeles-Long Beach-Anaheim, CA | 8,690 | $41,600 +8% |
| Dallas-Fort Worth-Arlington, TX | 7,160 | $36,010 -7% |
| Houston-Pasadena-The Woodlands, TX | 6,200 | $34,010 -12% |
| New York-Newark-Jersey City, NY-NJ | 6,050 | $46,930 +21% |
| Atlanta-Sandy Springs-Roswell, GA | 5,450 | $36,940 -4% |
| Chicago-Naperville-Elgin, IL-IN | 5,270 | $44,260 +15% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 4,300 | $40,860 +6% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 3,910 | $37,860 -2% |
| San Jose-Sunnyvale-Santa Clara, CA | 940 | $50,200 +30% |
| San Francisco-Oakland-Fremont, CA | 2,170 | $50,030 +30% |
| Greeley, CO | 280 | $49,080 +27% |
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 30. 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.