Paperhangers
Construction, Maintenance & RepairAI exposure
- Data source: BLSPublished: 2026-08
Low· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.13
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 97% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Exposed tasks only| Task | APIRaw / share % |
|---|---|
Measure surfaces or review work orders to estimate the quantities of materials needed.47-2142 | 0.0015100.0 |
| Not observed on any surface — 19 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | |
Smooth strips or sections of paper with brushes or rollers to remove wrinkles and bubbles and to smooth joints. | —0 |
Trim rough edges from strips, using straightedges and trimming knives. | —0 |
Trim excess material at ceilings or baseboards, using knives. | —0 |
Check finished wallcoverings for proper alignment, pattern matching, and neatness of seams. | —0 |
Mark vertical guidelines on walls to align strips, using plumb bobs and chalk lines. | —0 |
Cover interior walls and ceilings of rooms with decorative wallpaper or fabric, using hand tools. | —0 |
Apply adhesives to the backs of paper strips, using brushes, or dunk strips of prepasted wallcovering in water, wiping off any excess adhesive. | —0 |
Measure and cut strips from rolls of wallpaper or fabric, using shears or razors. | —0 |
Place strips or sections of paper on surfaces, aligning section edges and patterns. | —0 |
Fill holes, cracks, and other surface imperfections preparatory to covering surfaces. | —0 |
Apply sizing to seal surfaces and maximize adhesion of coverings to surfaces. | —0 |
Smooth rough spots on walls and ceilings, using sandpaper. | —0 |
Set up equipment, such as pasteboards and scaffolds. | —0 |
Remove old paper, using water, steam machines, or solvents and scrapers. | —0 |
Apply thinned glue to waterproof porous surfaces, using brushes, rollers, or pasting machines. | —0 |
Mix paste, using paste powder and water, and brush paste onto surfaces. | —0 |
Staple or tack advertising posters onto fences, walls, billboards, or poles. | —0 |
Remove paint, varnish, dirt, and grease from surfaces, using paint remover and water soda solutions. | —0 |
Apply acetic acid to damp plaster to prevent lime from bleeding through paper. | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Measure surfaces or review work orders to estimate the quantities of materials needed.O*NET Task ID 14916 | 0.5 |
Smooth strips or sections of paper with brushes or rollers to remove wrinkles and bubbles and to smooth joints.O*NET Task ID 14906 | 0.0 |
Trim rough edges from strips, using straightedges and trimming knives.O*NET Task ID 14907 | 0.0 |
Trim excess material at ceilings or baseboards, using knives.O*NET Task ID 14908 | 0.0 |
Check finished wallcoverings for proper alignment, pattern matching, and neatness of seams.O*NET Task ID 14909 | 0.0 |
Mark vertical guidelines on walls to align strips, using plumb bobs and chalk lines.O*NET Task ID 14910 | 0.0 |
Cover interior walls and ceilings of rooms with decorative wallpaper or fabric, using hand tools.O*NET Task ID 14911 | 0.0 |
Apply adhesives to the backs of paper strips, using brushes, or dunk strips of prepasted wallcovering in water, wiping off any excess adhesive.O*NET Task ID 14912 | 0.0 |
Measure and cut strips from rolls of wallpaper or fabric, using shears or razors.O*NET Task ID 14913 | 0.0 |
Place strips or sections of paper on surfaces, aligning section edges and patterns.O*NET Task ID 14914 | 0.0 |
Fill holes, cracks, and other surface imperfections preparatory to covering surfaces.O*NET Task ID 14915 | 0.0 |
Apply sizing to seal surfaces and maximize adhesion of coverings to surfaces.O*NET Task ID 14917 | 0.0 |
Smooth rough spots on walls and ceilings, using sandpaper.O*NET Task ID 14918 | 0.0 |
Set up equipment, such as pasteboards and scaffolds.O*NET Task ID 14919 | 0.0 |
Remove old paper, using water, steam machines, or solvents and scrapers.O*NET Task ID 14920 | 0.0 |
Apply thinned glue to waterproof porous surfaces, using brushes, rollers, or pasting machines.O*NET Task ID 14921 | 0.0 |
Mix paste, using paste powder and water, and brush paste onto surfaces.O*NET Task ID 14922 | 0.0 |
Staple or tack advertising posters onto fences, walls, billboards, or poles.O*NET Task ID 14923 | 0.0 |
Remove paint, varnish, dirt, and grease from surfaces, using paint remover and water soda solutions.O*NET Task ID 14924 | 0.0 |
Apply acetic acid to damp plaster to prevent lime from bleeding through paper.O*NET Task ID 14925 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page