Construction and Extraction · United States (national)
Painters, Construction and Maintenance
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is highly exposed to automation, and the bigger shift looks near-term.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 14.9
Physical / roboticsrange 56.0 to 80.0
When the change is likely
Near-term, highlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Act within the next 1 to 2 years
Displacement for this role is near-term. Start a concrete reskilling plan now, while you still have income and leverage.
2
Move up from the task to the system
Robotics targets the physical work. Shift toward supervising, maintaining, or programming the automation instead of competing with it.
3
Pivot toward Painting, Coating, and Decorating Workers
It is skill-adjacent and less exposed (39.0 vs 68). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
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Which of my tasks are exposed
Fill cracks, holes, or joints with caulk, putty, plaster, or other fillers, using caulking guns or putty knives.
68.0
Cover surfaces with dropcloths or masking tape and paper to protect surfaces during painting.
68.0
Smooth surfaces, using sandpaper, scrapers, brushes, steel wool, or sanding machines.
68.0
Read work orders or receive instructions from supervisors or homeowners to determine work requirements.
68.0
Apply primers or sealers to prepare new surfaces, such as bare wood or metal, for finish coats.
68.0
Apply paint, stain, varnish, enamel, or other finishes to equipment, buildings, bridges, or other structures, using brushes, spray guns, or rollers.
68.0
Erect scaffolding or swing gates, or set up ladders, to work above ground level.
68.0
Mix and match colors of paint, stain, or varnish with oil or thinning and drying additives to obtain desired colors and consistencies.
68.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Fill cracks, holes, or joints with caulk, putty, plaster, or other fillers, using caulking guns or putty knives.
Task: Cover surfaces with dropcloths or masking tape and paper to protect surfaces during painting.
Lower-risk adjacent roles
Painting, Coating, and Decorating Workers
39.0
Furniture Finishers
57.2
Coating, Painting, and Spraying Machine Setters, Operators, and Tenders
62.0
Insulation Workers, Floor, Ceiling, and Wall
62.0
Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons
64.0
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Painters, Construction and Maintenance to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 2.9, robotics 68.0.
The timeline comes from the higher axis (68.0), shifted for local demand (stable), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.