Masonry workers

AI Overlap Index
27.3 / 100
Insulated

Embodied skill, frontline presence, or deep institutional judgment. Most insulated.

SOC · Construction And Extraction

Bureau of Labor Statistics
Median pay
$56,600/yr
Hourly
$27/hr
Jobs 2024
294,300
Projected 2034
300,000
10-yr outlook
+2% · Slower than average
Employment change
5,600
Entry education
See How to Become One
SOC code

Signal composition

how the 0-100 score is assembled

Task Automation Impact weight 60%
19.6
contribution to AOI: 11.8
Automation Potential weight 10%
20.0
contribution to AOI: 2.0
Market Pressure weight 15%
55.0
contribution to AOI: 8.2
Entry Barrier Erosion weight 15%
35.0
contribution to AOI: 5.2

By seniority

multiplicative adjustment from category curve

Entry
32.2
mult 1.18x
Mid
27.3
mult 1.00x
Senior
23.2
mult 0.85x

Entry-level roles carry the brunt because they concentrate the most automatable subset of tasks. Senior work is insulated by judgment, relationships, and accountability.

Task-level analysis

scored 0-100 for current-generation AI feasibility, weighted by BLS-stated importance

11 tasks · model: claude-sonnet-4-5-20250929
Important t4

Read blueprints or drawings to calculate materials needed and plan layout

AI vision systems can parse construction drawings, perform quantity takeoffs, and generate material lists with high accuracy; this is primarily a computational and pattern recognition task that current AI handles well with human review of outputs.

BLS evidence: Masons typically read blueprints or drawings to calculate materials needed and lay out patterns, forms, or foundations according to plans.

72
automation
Supporting t11

Fill expansion joints with caulking materials

AI could potentially guide caulking patterns, but the physical application requires precise manual control in varied joint configurations and environmental conditions, with some potential for semi-automated assistance in standardized settings.

BLS evidence: Masons fill expansion joints with caulking materials.

24
automation
Important t7

Break or cut materials to required size and shape

AI can assist with measurement calculations, but the physical cutting of varied masonry materials (brick, stone, block) with different tools in field conditions requires manual dexterity and real-time judgment that robotics cannot yet replicate reliably.

BLS evidence: Masons break or cut materials to required size.

22
automation
Supporting t10

Clean and polish surfaces with handtools or power tools

Involves physical operation of hand and power tools on varied surfaces with judgment about pressure and technique to avoid damage; robotic systems cannot yet adapt to the range of materials and conditions encountered.

BLS evidence: Masons clean and polish surfaces with handtools or power tools.

20
automation
Important t6

Align structures using levels and plumbs to ensure proper positioning

Involves physical use of levels and plumb tools with continuous micro-adjustments during construction; while sensors exist, the integration with physical manipulation in unstructured environments remains beyond current automation capabilities.

BLS evidence: Masons align structures, using levels and plumbs.

18
automation
Supporting t9

Clean excess mortar with trowels and other handtools

Requires physical scraping and cleaning with trowels in tight spaces and irregular surfaces, with tactile feedback to avoid damaging finished work; current robotics lack the delicate touch control needed.

BLS evidence: Masons clean excess mortar with trowels and other handtools.

16
automation
Important t5

Mix mortar or grout and spread it onto slabs or foundations

Requires physical mixing and spreading with real-time adjustment for consistency, weather conditions, and substrate variations; robotic systems lack the adaptive manipulation needed for varied site conditions.

BLS evidence: Masons mix mortar or grout and spread it onto a slab or foundation.

15
automation
Important t8

Install decorative terrazzo finishes by blending marble chips into epoxy or cement

Highly specialized decorative work requiring artistic judgment, precise blending of materials, and fine finishing techniques in custom installations that demand human craftsmanship and aesthetic sensibility.

BLS evidence: Terrazzo workers create decorative finishes by blending fine marble chips into the epoxy, resin, or cement, which is often colored.

14
automation
Core t2

Place and finish concrete for foundations, floors, walkways, and other structures

Demands physical presence for pouring, finishing, and smoothing concrete in varied site conditions with real-time judgment about consistency and weather effects that AI-controlled robotics cannot yet handle autonomously.

BLS evidence: Cement masons and concrete finishers place and finish concrete. They may color concrete surfaces, expose small stones in walls and sidewalks, or make concrete beams, columns, and panels.

12
automation
Core t3

Cut and set natural or artificial stone for walls, exteriors, and floors

Involves cutting irregular natural materials and precisely setting them in non-standardized positions, requiring tactile feedback and spatial reasoning in dynamic outdoor environments beyond current robotic dexterity.

BLS evidence: Stonemasons build stone walls and set stone exteriors and floors. Using a special hammer or a diamond-blade saw, workers cut stone into various shapes and sizes.

10
automation
Core t1

Construct masonry walls, fireplaces, and structures using brick, block, or stone

Requires precise physical manipulation in unpredictable outdoor environments, real-time adaptation to material variations, and fine motor control that current robotics cannot reliably achieve across diverse construction sites.

BLS evidence: Brickmasons and blockmasons build and repair walls, fireplaces, and other structures with brick, terra cotta, precast masonry panels, concrete block, and other masonry materials.

8
automation

Task heatmap

automation score by task, sorted by weighted contribution

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External signals and sources

category-level priors and BLS fields that feed the four non-task signals

Automation Potential
20
karpathy 2/10
  • Karpathy/BLS Digital AI Exposure (0-10 scale rescaled to 0-100)
Market Pressure
55
outlook: Slower than average
  • BLS projected outlook: Slower than average (2%)
  • Indeed demand signal (monthly refresh pending)
Entry Barrier Erosion
35
ed: See How to Become One
  • BLS typical entry-level education: See How to Become One
  • Credential trend signal (annual refresh)

Related in Construction And Extraction

closest AOI neighbors in the same category