EXPOSED
Mixing mortar, hauling block and stone, setting up scaffolding, cutting tile, and cleaning excess grout are physical tasks in dusty, uneven, weather-exposed environments that no deployed robot handles at cost. The exposure here is not language AI — it's the absence of any credential, signature requirement, or client relationship, which leaves the role wage-exposed and dependent on the crew's demand rather than on the helper's own defensible skill. Score reflects the modal helper, not the mason they may become.
Part 2020 shock, part continued decline in the years since.
Median pay $35,410 → $47,550 +7.4% 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
-10.5% 16,100 → 14,400 on the projections basis
Shrinking, but not obviously because of AI
The BLS projects -10.5% by 2034, but at 48/100 this work is only moderately exposed — not the profile of a job current AI can simply do. Occupations shrink for many reasons, and the score does not point at automation as this one's cause.
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.
~1,400 openings a year on average, including replacing people who leave.
Hod CarrierMasontenderTile HelperBrick TenderBrick WasherMason HelperMason TenderBrick CarrierBrick CleanerTile FinisherPatcher HelperMarble FinisherBricklayer HelperBricklayer TenderTile Layer HelperAdobe Layer HelperBoat Joiner HelperBrick Mason HelperHot-Top-Liner HelperChimney Builder HelperFirebrick Layer HelperMarble Finisher HelperMonument Setter HelperRefractory Tile Helper
Holding it up: embodiment . Weakest point: liability shield .
Tasks largely resist digitisation Carrying 8-inch CMU up a ladder, mixing mortar to the mason's preferred consistency by feel, wet-cutting tile on a saw, and chipping out hardened thinset are unstructured manipulation tasks on a surface that changes every course — an 18 rather than a 14 because there is no scripted, repeatable subtask in the day that a fixed-position machine could take over.
Hands-on in uncontrolled environments Nearly every hour is spent on scaffold planks, in trenches, on unfinished floors, in silica dust and whatever weather the pour schedule demands, hauling material the mason cannot reach — a 19 leaves room only for the rare indoor tile job in a conditioned building.
No licence, no signature requirement No state licenses masonry helpers; you can be put to work with an OSHA 10 card at most, and the general contractor's or mason contractor's licence covers the work you touch, so nothing you do carries your name — the 2 rather than 0 reflects only that scaffold and silica training requirements exist on paper.
Executes defined procedures on defined inputs You decide how to stack a load so it doesn't tip and when a mix looks too stiff, but the mason sets the layout, the string line, the joint profile, and the grout choice — a 4 rather than a 10 because the consequential calls are made above you and corrected on the spot.
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 (18/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (5/20) is whether buyers specifically pay for a person. Judgment and accountability (4/20) 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 11 of this occupation's 48 points (23%).
Embodiment (19/20) 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 61/100, still EXPOSED.
Registered-apprenticeship mandates on public work — e.g. state prevailing-wage statutes and PLAs that require a fixed journeyman:apprentice ratio and count only registered apprentices toward it — convert 'helper' into a legally recognized apprentice slot with wage steps. Already happening on federally funded IIJA/CHIPS jobs; broader state adoption would extend it.
Helpers being pulled inside a credentialed structure: OSHA's scaffold rule already requires a trained 'competent person' for erection/alteration, and the respirable crystalline silica standard (29 CFR 1926.1153) requires a written exposure control plan with a designated competent person. If states or large GC insurers began requiring that every worker who erects scaffold or operates a wet saw hold a documented, individually-issued card (as NY City's Local Law 196 site-safety training did for all construction workers), the helper role acquires a named credential tied to a person rather than a crew.
Historic-preservation and restoration work where the Secretary of the Interior's Standards and NPS Preservation Briefs govern mortar matching and stone repointing: tax-credit-certified projects require documented hand methods, and owners pay for identifiable craft labor. Growth of this segment (or state historic tax credits requiring certified restoration crews) raises what a buyer is specifically paying a human for — but this touches a minority of helpers, not the modal residential tile job.
Silica and heat rules that place stop-work authority on the worker: Cal/OSHA's heat illness standard and proposed federal heat rule, plus union contract language giving any crew member the right to halt work for dust or scaffold conditions without retaliation, make the helper the person who owns a consequential call.
If semi-automated block-laying and mortar-dispensing rigs (SAM100-class, Construction Robotics) or tile-setting gantries actually deploy on large flat-wall jobs, the hauling/feeding tier goes first and what remains for the helper is edge work, cut-ins, corners, existing-building irregularity and machine tending — a genuine two-tier split. This raises the residual score only for those retained, and shrinks headcount.
The limit. No route to a personal signature-and-liability regime: masonry licensing attaches to the contractor entity, and no US state licenses masons individually, so liability_shield realistically caps in the mid-single digits. Trust premium is structurally capped because the buyer contracts with a GC or masonry sub and never selects the helper. The honest ceiling is roughly 55-60, and it comes from credentialing and apprenticeship wage floors, not from AI being unable to do the work — it already can't.
| Los Angeles-Long Beach-Anaheim, CA | 940 | $48,220 +1% |
| New York-Newark-Jersey City, NY-NJ | 690 | $70,080 +47% |
| Riverside-San Bernardino-Ontario, CA | 610 | $46,750 -2% |
| Dallas-Fort Worth-Arlington, TX | 460 | $39,700 -17% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 420 | $45,030 -5% |
| Las Vegas-Henderson-North Las Vegas, NV | 350 | $44,170 -7% |
| Sacramento-Roseville-Folsom, CA | 340 | $57,400 +21% |
| San Francisco-Oakland-Fremont, CA | 340 | $59,000 +24% |
| Seattle-Tacoma-Bellevue, WA | 70 | $77,990 +64% |
| New York-Newark-Jersey City, NY-NJ | 690 | $70,080 +47% |
| Boston-Cambridge-Newton, MA-NH | 190 | $65,870 +39% |
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 48. 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.