How exposed to AI is the job of Rock Splitters, Quarry?

1.6% of the weighted task load of Rock Splitters, Quarry is exposed, meaning work current AI systems can produce with little structural friction. A further 1.4% is assisted work and 97.0% is untouched. That places it 2th out of 923 occupations, rank 906, in release v2026.Q3 of the Task Exposure Index.

Will AI replace Rock Splitters, Quarry?

The index does not measure displacement. It measures what current AI systems can produce and what stands in the way. Whether exposed work becomes a lost job depends on firm economics, regulation and organisational inertia, none of which this index models.

Which tasks of a Rock Splitters, Quarry are most exposed to AI?

Of 9 tasks O*NET records for this occupation, 0 are exposed, 0 assisted and 9 untouched. The full task-by-task breakdown is published on this page.

Occupation · SOC 47-5051.00 · Job Zone 2

AI exposure: Rock Splitters, Quarry

Separate blocks of rough dimension stone from quarry mass using jackhammers, wedges, or chop saws.

Reading this score

computed

Rock Splitters, Quarry is among the least exposed occupations measured, at 1.6% of weighted task load, rank 906 of 923. 97.0% of this job is work current AI systems cannot produce at all. That is not a statement about skill or value. It is a statement about what these systems can and cannot do.

What holds the line here is embodiment. Across this occupation's 9 tasks it averages 2.94 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.

The most exposed thing this job does is Locate grain line patterns to determine how rocks will split when cut, at 13.3%. The least is Cut grooves along outlines, at 0.0%. A gap of 13.3% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within construction and extraction occupations, this one is less exposed than the median of 5.1% across the group's 61 roles, with 49 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.

What would move this score. Of 9 tasks, 0 are currently banded exposed, 0 assisted and 9 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.

Where the score comes from

judged

Every task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.

Dimension	Mean	Scale
Capability	0.11	0-4
Embodiment	2.94	0-3
Presence	0.72	0-3
Accountability	0.61	0-3
Context	0.78	0-3
Verification cost	1.00	0-3

Task by task

9 tasks, O*NET 31.0

Task	Exposed	Assisted	Untouched	Importance	Band
Locate grain line patterns to determine how rocks will split when cut.	13.3%	11.7%	75.0%	3.80	untouched
Remove pieces of stone from larger masses, using jackhammers, wedges, and other tools.	0.0%	0.0%	100.0%	3.70	untouched
Insert wedges and feathers into holes, and drive wedges with sledgehammers to split stone sections from masses.	0.0%	0.0%	100.0%	3.69	untouched
Mark dimensions or outlines on stone prior to cutting, using rules and chalk lines.	0.0%	0.0%	100.0%	3.40	untouched
Cut slabs of stone into sheets that will be used for floors or counters.	0.0%	0.0%	100.0%	3.92	untouched
Set charges of explosives to split rock.	0.0%	0.0%	100.0%	3.80	untouched
Drill holes along outlines, using jackhammers.	0.0%	0.0%	100.0%	3.78	untouched
Drill holes into sides of stones broken from masses, insert dogs or attach slings, and direct removal of stones.	0.0%	0.0%	100.0%	3.74	untouched
Cut grooves along outlines, using chisels.	0.0%	0.0%	100.0%	3.28	untouched

Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.

Occupations either side of this one

The four closest scores in the same occupational family, then the four closest anywhere in the index.

Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.

What this means in practice

Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.