Construction and Extraction · United States (national)
Tile and Stone Setters
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 meaningful change looks likely over the next 5 to 10 years.
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 15.8
Physical / roboticsrange 54.6 to 78.6
When the change is likely
Partial, 5 to 10 yearslocal demand growing
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Use your 5 to 10 year runway
Change here is gradual. Build resilient skills steadily and watch the signals rather than waiting for a shock.
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 Floor Layers, Except Carpet, Wood, and Hard Tiles
It is skill-adjacent and less exposed (50.5 vs 67). 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
Free shows where you stand today. JobBreak Monitor writes your full AI-researched plan and keeps watching, so you know the moment your outlook shifts.
Free
$0
See exactly where you stand.
Full job-risk score & timeline
Task-by-task exposure breakdown
One starter strategy
AI-researched personal plan
Ongoing monitoring & alerts
Current plan
Keep ahead
JobBreak Monitor
$3.99/mo
$29/yr
or $29/yr save 39%
that's about $2.42/mo, billed yearly
Your full researched plan, plus a watch on your job.
Everything in Free
Full AI-researched, cited action plan
Resume-aware personalisation
Re-run your plan any time
Alerts when your outlook shifts
Start monitoring
Secure checkout by Stripe. Cancel anytime.
Which of my tasks are exposed
Align and straighten tile using levels, squares, and straightedges.
66.6
Finish and dress the joints and wipe excess grout from between tiles, using damp sponge.
66.6
Cut and shape tile to fit around obstacles and into odd spaces and corners, using hand and power cutting tools.
66.6
Determine and implement the best layout to achieve a desired pattern.
66.6
Mix, apply, and spread plaster, concrete, mortar, cement, mastic, glue or other adhesives to form a bed for the tiles, using brush, trowel and screed.
66.6
Study blueprints and examine surface to be covered to determine amount of material needed.
66.6
Measure and mark surfaces to be tiled, following blueprints.
100.0
Lay and set mosaic tiles to create decorative wall, mural, and floor designs.
66.6
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Measure and mark surfaces to be tiled, following blueprints.
Task: Align and straighten tile using levels, squares, and straightedges.
Local demand is growing
Lower-risk adjacent roles
Floor Layers, Except Carpet, Wood, and Hard Tiles
50.5
Carpet Installers
51.8
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Tile and Stone Setters 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 3.8, robotics 66.6.
The timeline comes from the higher axis (66.6), shifted for local demand (growing), 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.