Will AI replace Brickmasons and Blockmasons?
AI reaches little of this job’s work.
0/100
Low exposure
More exposed to AI than 2% of US workers.
- AI already used in
- 0% of the work
- As AI tools mature: up to
- 0% of the work
What the job involves
Lay and bind building materials, such as brick, structural tile, concrete block, cinder block, glass block, and terra-cotta block, with mortar and other substances, to construct or repair walls, partitions, arches, sewers, and other structures.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Out of reach(14)
- Measure distance from reference points and mark guidelines to lay out work, using plumb bobs and levels.
- Construct corners by fastening in plumb position a corner pole or building a corner pyramid of bricks, and filling in between the corners using a line from corner to corner to guide each course, or layer, of brick.
- Calculate angles and courses and determine vertical and horizontal alignment of courses.
- Break or cut bricks, tiles, or blocks to size, using trowel edge, hammer, or power saw.
- Remove excess mortar with trowels and hand tools, and finish mortar joints with jointing tools, for a sealed, uniform appearance.
and 9 more
Also called
Brick Mason · Bricklayer · Block Layer · Mason · Brick Layer · Cupola Patcher · Firebrick and Refractory Tile Bricklayer · Block Mason
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics