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Tile and Stone Setters

Occupation · SOC 47-2044.00

Apply hard tile, stone, and comparable materials to walls, floors, ceilings, countertops, and roof decks.

Also called: Tile Installer · Tile Mechanic · Tile Setter · Tile and Marble Installer · Ceramic Tile Mechanic · Ceramic Tile Setter · Tile Finisher · Tile Man · Tile Mason · Tile and Marble Setter · Acoustical Carpenter · Acoustical Installer

Job family: Construction and Extraction Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-47-2044-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

13th-percentile task overlap — yet about 4,200 openings a year (+10.1% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 10th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 20th 0.2
AI assistant applicability (Microsoft) Low 19th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.8 · 61st percentile among occupations · Moderate

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Study blueprints and examine surface to be covered to determine amount of material needed. 0.8%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Growing fast · +10.1% by 2034
Projected annual openings 4,200
Employment 2024 → 2034 52,600 → 58,000

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

10% mean task exposure (2025)
3rd percentile of 427 placed occupations
+0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Floor Layers and Tile Setters · 7122 10% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 25 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Building and Construction 4.0
Mathematics 3.4
Customer and Personal Service 3.0
Design 3.0

Abilities

Visualization 3.8
Near Vision 3.6
Extent Flexibility 3.5
Problem Sensitivity 3.4
Trunk Strength 3.4
Information Ordering 3.3
Oral Comprehension 3.1
Category Flexibility 3.1
Arm-Hand Steadiness 3.1
Manual Dexterity 3.1
Finger Dexterity 3.1
Multilimb Coordination 3.1
Static Strength 3.1
Stamina 3.1
Far Vision 3.1
Oral Expression 3.0
Deductive Reasoning 3.0
Inductive Reasoning 3.0
Control Precision 3.0
Visual Color Discrimination 3.0
Speech Recognition 3.0
Speech Clarity 3.0
Written Comprehension 2.9
Selective Attention 2.9
Gross Body Equilibrium 2.9
Originality 2.8

Essential skills

Active Listening 3.1
Speaking 3.0
Mathematics 3.0
Critical Thinking 3.0
Reading Comprehension 2.9
Monitoring 2.8

Transferable skills

Coordination 3.0
Complex Problem Solving 3.0
Time Management 3.0
Judgment and Decision Making 2.8

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Word Word processing software Hot technology
Salesforce software Customer relationship management CRM software Hot technology
Aya Associates Comp-U-Floor Data base user interface and query software
EasyCAD Iris 2D Computer aided design CAD software
Measure Square FloorEstimate Pro Project management software
TileGem Computer aided design CAD software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.9
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.9
Face-to-Face Discussions with Individuals and Within Teams 4.5
Importance of Being Exact or Accurate 4.5
Spend Time Kneeling, Crouching, Stooping, or Crawling 4.5
Spend Time Making Repetitive Motions 4.4
Spend Time Bending or Twisting Your Body 4.3
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.3
Exposed to Contaminants 4.2
Spend Time Standing 4.0
Determine Tasks, Priorities and Goals 3.9
Contact With Others 3.8
Work With or Contribute to a Work Group or Team 3.8
Freedom to Make Decisions 3.6
Time Pressure 3.6
Impact of Decisions on Co-workers or Company Results 3.5
Telephone Conversations 3.5
Frequency of Decision Making 3.4
Physical Proximity 3.4
Indoors, Environmentally Controlled 3.3
Exposed to Hazardous Equipment 3.3
Work Outcomes and Results of Other Workers 3.2
Coordinate or Lead Others in Accomplishing Work Activities 3.1
Exposed to Very Hot or Cold Temperatures 3.1
Exposed to Extremely Bright or Inadequate Lighting Conditions 3.1
Consequence of Error 3.1
Health and Safety of Other Workers 3.1
Importance of Repeating Same Tasks 3.0
Exposed to Cramped Work Space, Awkward Positions 3.0
Level of Competition 2.9
Exposed to Minor Burns, Cuts, Bites, or Stings 2.9
Indoors, Not Environmentally Controlled 2.9
Outdoors, Exposed to All Weather Conditions 2.8
Spend Time Keeping or Regaining Balance 2.8
Deal With External Customers or the Public in General 2.6
Spend Time Walking or Running 2.5
Outdoors, Under Cover 2.4
Exposed to High Places 2.2
Spend Time Climbing Ladders, Scaffolds, or Poles 2.1
E-Mail 2.1

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
No formal educational credential · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Construction Trades . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 48.0%
Less than a High School Diploma 36.9%
Some College Courses 10.7%
Post-Secondary Certificate 4.4%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.7
Artistic 2.5
Investigative 1.5
Social 1.3

Interest areas

Physical/Manual Labor 6.3
Construction/Woodwork 2.7
Applied Arts and Design 2.5
Visual Arts 2.2
Engineering 1.8
Mathematics/Statistics 1.5
Mechanics/Electronics 1.4

Work styles

Attention to Detail 2.3
Dependability 2.1
Cautiousness 1.4
Perseverance 1.3

Wages & employment

U.S. · annual wages (BLS OEWS)

$36k10th$45k25th$52kMedian$65k75th$83k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
53k202458k2034 (proj.)+10.1% · Growing fast
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $36,140
25th percentile $44,540
Median (50th) $52,240
75th percentile $64,980
90th percentile $82,960
People employed 38,740

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Construction · Sector 31,210 $55,160
Manufacturing · Sector 5,420 $47,350
Retail Trade · Sector 1,480 $52,700
Masonry Contractors · National industry 1,090 $59,780
Drywall and Insulation Contractors · National industry 220 $55,690
Temporary Help Services · National industry 120 $27,040
Educational Services · Sector 40 $47,690
Plumbing, Heating, and Air-Conditioning Contractors · National industry $46,810
Wholesale Trade · Sector $48,540

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Masonry Contractors · National industry 30.21× 1,090
Construction · Sector 15.29× 31,210
Drywall and Insulation Contractors · National industry 3.56× 220
Manufacturing · Sector 1.69× 5,420
Retail Trade · Sector 0.38× 1,480
Temporary Help Services · National industry 0.18× 120

Part of the Construction career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Tile and Stone Setters sits at the 13th percentile of AI task-overlap and the 37th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Tile and Stone Setters Drywall and Ceiling Tile Installers Floor Sanders and Finishers Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters Brickmasons and Blockmasons AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Tile and Stone Setters — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 3rd percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Tile and Stone Setters show 13th-percentile AI task overlap — and about 4,200 annual U.S. openings

  • Tile and Stone Setters rank in the 13th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 4,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be growing fast (+10.1%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $52,240, across about 38,740 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Tile and Stone Setters show 13th-percentile AI task overlap — and about 4,200 annual U.S. openings

• Tile and Stone Setters rank in the 13th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 4,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be growing fast (+10.1%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $52,240, across about 38,740 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Tile and Stone Setters". https://singulariki.com/roles/role-47-2044-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Tile and Stone Setters." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-47-2044-00

APA

Singulariki. (2026). Tile and Stone Setters. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-47-2044-00

BibTeX
@misc{singulariki-role-47-2044-00,
  title  = {Tile and Stone Setters},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-47-2044-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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