Community Sentiment
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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Segmental Pavers?
Task-level automation exposure for Segmental Pavers: 12 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
12%
weighted task index
12 of 12 tasks scored
Median Salary
$49,910 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
28,380 people hold this job — size, not growth
Projected 2025–35
+3.9%
employment change
2,400 openings a year · BLS roll-up 47-4090
Pay by state
BLS OEWS publishes 46 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$30,090 – $75,340
Highest medians
Maine $75,340
Massachusetts $70,540
Washington $69,380
North Dakota $66,450
Idaho $65,420
Lowest medians
Puerto Rico $30,090
Kentucky $35,420
Mississippi $36,260
Tennessee $41,600
Arkansas $42,670
OEWS has no state row for this exact code, so figures come from the 47-4090 roll-up. 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 12%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 91%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 8%
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 6/100
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
12 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%) 9%
1 tasks
Low exposure (below 35%) 91%
11 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 →Constituent Tasks
#8285 · Core · 7% automatable
Prepare base for installation by removing unstable or unsuitable materials, compacting and grading the soil, draining or stabilizing weak or saturated soils and taking measures to prevent water penetration and migration of bedding sand.
physical manipulation — Operating compaction equipment by hand on site
Compact materials to create level bases.
#8284 · Core · 5% automatable
Supply and place base materials, edge restraints, bedding sand and jointing sand.
physical manipulation — Manual moving of construction materials on site
Spread sand, dirt or other loose materials onto surfaces. Move construction or extraction materials to locations where they are needed.
#8292 · Core · 52% automatable
Discuss the design with the client.
interpersonal — Client dialogue on products/policies is relational
Communicate with clients about products, procedures, and policies.
#8287 · Core · 4% automatable
Set pavers, aligning and spacing them correctly.
physical manipulation — Aligning masonry is skilled manual work
Align masonry materials.
#8288 · Core · 5% automatable
Sweep sand into the joints and compact pavement until the joints are full.
physical manipulation — Operating compaction equipment by hand on site
Spread sand, dirt or other loose materials onto surfaces. Compact materials to create level bases.
#8294 · Core · 7% automatable
Screed sand level to an even thickness, and recheck sand exposed to elements, raking and rescreeding if necessary.
physical manipulation — Operating compaction equipment by hand on site
Compact materials to create level bases.
#8293 · Core · 5% automatable
Cut paving stones to size and for edges, using a splitter and a masonry saw.
physical manipulation — Masonry cutting requires skilled manual work
Cut tile, stone, or other masonry materials.
#8289 · Core · 7% automatable
Compact bedding sand and pavers to finish the paved area, using a plate compactor.
physical manipulation — Operating compaction equipment by hand on site
Compact materials to create level bases.
#8290 · Core · 29% automatable
Design paver installation layout pattern and create markings for directional references of joints and stringlines.
text generation — Installation diagrams drafted; field accuracy human-owned
Create construction or installation diagrams. Mark reference points on construction materials.
#8286 · Core · 10% automatable
Sweep sand from the surface prior to opening to traffic.
physical manipulation — Work site cleaning is physical labor
Clean work sites.
#8291 · Core · 2% automatable
Resurface an outside area with cobblestones, terracotta tiles, concrete or other materials.
physical manipulation — Masonry requires skilled manual labor
Install masonry materials.
#8295 · Core · 3% automatable
Cement the edges of the paved area.
physical manipulation — Skilled hand finishing of concrete surfaces
Finish concrete surfaces.
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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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