AI exposure: Paperhangers
Cover interior walls or ceilings of rooms with decorative wallpaper or fabric, or attach advertising posters on surfaces such as walls and billboards. May remove old materials or prepare surfaces to be papered.
Reading this score
computedAt 8.1% of weighted task load, Paperhangers sits at the 10th percentile, below the point where a job's centre of gravity has moved. 86.8% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.
What holds the line here is embodiment. Across this occupation's 19 tasks it averages 2.81 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Staple or tack advertising posters onto fences, walls, billboards, or poles, at 30.0%. The least is Remove paint, varnish, dirt, and grease from surfaces, at 0.0%. A gap of 30.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within construction and extraction occupations, this one is more exposed than most. The median across the 61 roles in the group is 5.0%, and only 18 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 19 tasks, 1 are currently banded exposed, 0 assisted and 18 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 0.25 | 0-4 |
| Embodiment | 2.81 | 0-3 |
| Presence | 0.28 | 0-3 |
| Accountability | 0.29 | 0-3 |
| Context | 0.96 | 0-3 |
| Verification cost | 0.86 | 0-3 |
Task by task
19 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Staple or tack advertising posters onto fences, walls, billboards, or poles. | 30.0% | 20.0% | 50.0% | 4.22 | exposed |
| Measure surfaces or review work orders to estimate the quantities of materials needed. | 25.9% | 15.7% | 58.3% | 3.80 | untouched |
| Check finished wallcoverings for proper alignment, pattern matching, and neatness of seams. | 8.3% | 16.7% | 75.0% | – | untouched |
| Place strips or sections of paper on surfaces, aligning section edges and patterns. | 0.0% | 0.0% | 100.0% | 3.76 | untouched |
| Smooth strips or sections of paper with brushes or rollers to remove wrinkles and bubbles and to smooth joints. | 0.0% | 0.0% | 100.0% | 3.22 | untouched |
| Smooth rough spots on walls and ceilings, using sandpaper. | 0.0% | 0.0% | 100.0% | – | untouched |
| Cover interior walls and ceilings of rooms with decorative wallpaper or fabric, using hand tools. | 0.0% | 0.0% | 100.0% | – | untouched |
| Trim rough edges from strips, using straightedges and trimming knives. | 0.0% | 0.0% | 100.0% | – | untouched |
| Apply sizing to seal surfaces and maximize adhesion of coverings to surfaces. | 0.0% | 0.0% | 100.0% | 3.30 | untouched |
| Set up equipment, such as pasteboards and scaffolds. | 0.0% | 0.0% | 100.0% | 3.18 | untouched |
| Measure and cut strips from rolls of wallpaper or fabric, using shears or razors. | 0.0% | 0.0% | 100.0% | – | untouched |
| Trim excess material at ceilings or baseboards, using knives. | 0.0% | 0.0% | 100.0% | – | untouched |
| Mix paste, using paste powder and water, and brush paste onto surfaces. | 0.0% | 0.0% | 100.0% | 3.01 | untouched |
| Apply adhesives to the backs of paper strips, using brushes, or dunk strips of prepasted wallcovering in water, wiping off any excess adhesive. | 0.0% | 0.0% | 100.0% | – | untouched |
| Apply thinned glue to waterproof porous surfaces, using brushes, rollers, or pasting machines. | 0.0% | 0.0% | 100.0% | – | untouched |
| Fill holes, cracks, and other surface imperfections preparatory to covering surfaces. | 0.0% | 0.0% | 100.0% | – | untouched |
| Mark vertical guidelines on walls to align strips, using plumb bobs and chalk lines. | 0.0% | 0.0% | 100.0% | – | untouched |
| Remove old paper, using water, steam machines, or solvents and scrapers. | 0.0% | 0.0% | 100.0% | – | untouched |
| Remove paint, varnish, dirt, and grease from surfaces, using paint remover and water soda solutions. | 0.0% | 0.0% | 100.0% | – | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean. 12 task(s) lacked a usable O*NET weight and are shown but excluded from the weighting.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.