Community Sentiment
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AI Automation Index
Will AI Replace Refractory Materials Repairers, Except Brickmasons?
Task-level automation exposure for Refractory Materials Repairers, Except Brickmasons: 10 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
10%
weighted task index
10 of 10 tasks scored
Median Salary
$61,290 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
1,080 people hold this job — size, not growth
Projected 2025–35
-13.7%
employment change
100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 6 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$37,870 – $77,220
Highest medians
California $77,220
Indiana $74,600
Texas $67,330
Ohio $58,470
Alabama $47,650
Lowest medians
South Carolina $37,870
Alabama $47,650
Ohio $58,470
Texas $67,330
Indiana $74,600
State figures come from the 49-9045 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 10%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 100%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 0%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 22/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
10 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 0%
0 tasks
Medium exposure (≥ 35%) 0%
0 tasks
Low exposure (below 35%) 100%
10 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 06/2007 · Incumbent
Reline furnaces using ramming material.
Constituent Tasks
#13819 · Core · 10% automatable
Reline or repair ladles and pouring spouts with refractory clay, using trowels.
physical manipulation — Manual repair of worn mechanical parts
Repair worn, damaged, or defective mechanical parts.
#13820 · Core · 10% automatable
Chip slag from linings of ladles or remove linings when beyond repair, using hammers and chisels.
physical manipulation — Cleaning parts for repair needs hands-on dexterity
Clean equipment, parts, or tools to repair or maintain them in good working order.
#13821 · Core · 15% automatable
Mix specified amounts of sand, clay, mortar powder, and water to form refractory clay or mortar, using shovels or mixing machines.
physical manipulation — Hands-on mixing of compounds in physical workspace
Prepare compounds or solutions to be used for repairs.
#13822 · Supplemental · 17% automatable
Measure furnace walls to determine dimensions and cut required number of sheets from plastic block, using saws.
physical manipulation — Physical distance measurement in field
Measure distances or dimensions. Cut materials according to specifications or needs.
#13824 · Supplemental · 10% automatable
Dry and bake new linings by placing inverted linings over burners, building fires in ladles, or by using blowtorches.
physical manipulation — Part fabrication requires physical machining and handling
Fabricate parts or components.
#13825 · Supplemental · 10% automatable
Remove worn or damaged plastic block refractory linings of furnaces, using hand tools.
physical manipulation — Structural repair is hands-on physical work
Repair structural components.
#13827 · Supplemental · 3% automatable
Climb scaffolding, carrying hoses, and spray surfaces of cupolas with refractory mixtures, using spray equipment.
situated mobility — Climbing structures requires physical mobility
Climb equipment or structures to access work areas.
#13829 · Supplemental · 5% automatable
Spread mortar on stopper heads and rods, using trowels, and slide brick sleeves over rods to form refractory jackets.
physical manipulation — Sealing gaps requires hands-on application
Seal gaps or cracks to prevent leakage or moisture intrusion.
#13830 · Supplemental · 4% automatable
Dump and tamp clay in molds, using tamping tools.
physical manipulation — Manual placement of materials into molds
Place materials into molds.
#13832 · Supplemental · 10% automatable
Transfer clay structures to curing ovens, melting tanks, and drawing kilns, using forklifts.
physical manipulation — Operating heavy equipment by hand in field
Move large objects using heavy equipment.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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