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Will AI replace Tile and Stone Setters?

Most of the work in Tile and Stone Setters still leans on things AI struggles with — research rates its theoretical AI reach at only ~9%, and real-world use lower still.

The Human Moat Work that's hard for AI to cross — for now.

O*NET-SOC 47-2044

How your 15 core tasks split

13% within AI's reach
0 AI can do this now
2 AI speeds this up
13 Still on you
AI could do · GPT-4 study
9%
9-pt gap
AI actually does · 2026 report
0%

Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.

⚡ The short answer

Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~9%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.

Where this job sits among 738 jobs

Being automatedTicking (can, but unused)Relatively safeQuietly happeningYOU0%50%100%0%40%75% → How much AI could do (theory) → How much AI is actually used (late 2025)

Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.

Stableconfidence

The signals here line up

Theoretical reach (~9%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.

See all 15 tasks, ratedBased on real task-level AI scores — click to collapse
AI can already do this0 of 15
  • None — AI cannot fully do any core task alone yet.
AI speeds this up2 of 15
  • Determine and implement the best layout to achieve a desired pattern.
  • Measure and mark surfaces to be tiled, following blueprints.
Still on you13 of 15
  • Cut and shape tile to fit around obstacles and into odd spaces and corners, using hand and power cutting tools.
  • Lay and set mosaic tiles to create decorative wall, mural, and floor designs.
  • Align and straighten tile using levels, squares, and straightedges.
  • Finish and dress the joints and wipe excess grout from between tiles, using damp sponge.
  • Cut, surface, polish, and install marble and granite or install pre-cast terrazzo, granite or marble units.
  • 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.
  • Apply mortar to tile back, position the tile, and press or tap with trowel handle to affix tile to base.
  • Level concrete and allow to dry.
  • Prepare surfaces for tiling by attaching lath or waterproof paper, or by applying a cement mortar coat to a metal screen.
  • Mix and apply mortar or cement to edges and ends of drain tiles to seal halves and joints.
  • Remove and replace cracked or damaged tile.
  • Apply a sealer to make grout stain- and water-resistant.
  • Remove any old tile, grout and adhesive using chisels and scrapers and clean the surface carefully.

My job is a Human Moat 😌

Turns out being human is still the hard part to copy.

Theoretical estimate · not a prediction · gistgarden.com

How we measured this — and how fresh it is

AI's theoretical reach data: 2023

From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.

Real-world AI use 2026 report

From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).

Task list & ratings O*NET 30.3

Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.

Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.

This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.