Will AI replace Cement Masons and Concrete Finishers?
Most of the work in Cement Masons and Concrete Finishers still leans on things AI struggles with — research rates its theoretical AI reach at only ~0%, and real-world use lower still.
O*NET-SOC 47-2051
How your 19 core tasks split
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.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~0%). By late 2025, real-world AI use had caught up to 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
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~0%), 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 19 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- No tasks in this middle tier.
- Check the forms that hold the concrete to see that they are properly constructed.
- Set the forms that hold concrete to the desired pitch and depth, and align them.
- Spread, level, and smooth concrete, using rake, shovel, hand or power trowel, hand or power screed, and float.
- Monitor how the wind, heat, or cold affect the curing of the concrete throughout the entire process.
- Mold expansion joints and edges, using edging tools, jointers, and straightedge.
- Signal truck driver to position truck to facilitate pouring concrete, and move chute to direct concrete on forms.
- Direct the casting of the concrete and supervise laborers who use shovels or special tools to spread it.
- Produce rough concrete surface, using broom.
- Apply hardening and sealing compounds to cure surface of concrete, and waterproof or restore surface.
- Operate power vibrator to compact concrete.
- Install anchor bolts, steel plates, door sills and other fixtures in freshly poured concrete or pattern or stamp the surface to provide a decorative finish.
- Wet surface to prepare for bonding, fill holes and cracks with grout or slurry, and smooth, using trowel.
- Waterproof or restore concrete surfaces, using appropriate compounds.
- Mix cement, sand, and water to produce concrete, grout, or slurry, using hoe, trowel, tamper, scraper, or concrete-mixing machine.
- Chip, scrape, and grind high spots, ridges, and rough projections to finish concrete, using pneumatic chisels, power grinders, or hand tools.
- Cut out damaged areas, drill holes for reinforcing rods, and position reinforcing rods to repair concrete, using power saw and drill.
- Wet concrete surface, and rub with stone to smooth surface and obtain specified finish.
- Clean chipped area, using wire brush, and feel and observe surface to determine if it is rough or uneven.
- Build wooden molds, and clamp molds around area to be repaired, using hand tools.
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.