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The AI Resilience Report helps you understand how AI is likely to impact your current or future career. Drawing on data from over 1,500 occupations, it provides a clear snapshot to support informed career decisions.
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Last Update: 4/23/2026
Your role’s AI Resilience Score is
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Med
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Low
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
Med
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
There are a reasonable number of sources for this result, but there is some disagreement between them.
Contributing sources
Rock Splitters, Quarry are somewhat less resilient to AI impacts than most occupations, according to our analysis of 6 sources.
The career of a Rock Splitter in a quarry is labeled as "Somewhat Resilient" because while some tasks like drilling are becoming automated, the core work of splitting rock still relies heavily on human skill. Workers' ability to read grain lines and manually split rocks with tools remains crucial and isn't easily replaced by machines.
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Learn more about how you can thrive in this position
This role is somewhat resilient
The career of a Rock Splitter in a quarry is labeled as "Somewhat Resilient" because while some tasks like drilling are becoming automated, the core work of splitting rock still relies heavily on human skill. Workers' ability to read grain lines and manually split rocks with tools remains crucial and isn't easily replaced by machines.
Read full analysisAnalysis of Current AI Resilience
Rock Splitters, Quarry
Updated Quarterly • Last Update: 2/17/2026

In practice, most rock-splitting tasks still need human skill. Job guides note that workers mark outlines by hand (using chalk and rules), examine grain lines, and insert wedges and sledgehammers to split stone [1]. We did not find any off-the-shelf AI tools for those exact tasks.
Some advanced systems do assist quarry planning – for example, researchers use algorithms to identify natural block shapes and plan cuts to reduce waste [2] – but this is a planning aid, not live splitting. On the other hand, drilling holes (often done with jackhammers) is seeing automation: one industry report describes the first fully autonomous drill rig that can “drill entire patterns” without a human in the cab [3]. Experts note mining and drilling are slowly adding more robots and software for big operations [4] [3].
In summary, steps like blasting or heavy drilling may be automated, but the core quarry work (reading grain lines, chalking outlines, hand-splitting with wedges) remains manual today.

Big companies may adopt AI faster to solve real problems, while small quarries move slowly. For instance, one report explains that a major U.S. quarry firm used an autonomous drill rig to tackle skilled‐labor shortages and improve safety [3] [3]. That rig example shows the industry is curious about efficiency gains.
However, rock splitting employs only a few thousand workers nationwide and is growing slowly [1], so many operators can’t afford cutting-edge machines. Expensive automated rigs make sense for large sites but are hard to justify in smaller family quarries. Socially and legally, workers and unions often insist on safe rollout of new tech.
In the end, human judgment and care remain crucial – machines may lift heavy tasks, but quarry workers’ experience in spotting grain and handling tools is still valuable [3] [1]. The industry is cautiously optimistic: AI can help address challenges (like safety), but the unique skills of people on the ground will stay important for a long time.

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They break large rocks into smaller pieces using tools and machines, making it easier to transport and use the stone for construction and other purposes.
Median Wage
$47,460
Jobs (2024)
3,200
Growth (2024-34)
+4.4%
Annual Openings
400
Education
No formal educational credential
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
AI-generated estimates of task resilience over the next 3 years
Locate grain line patterns to determine how rocks will split when cut.
Insert wedges and feathers into holes, and drive wedges with sledgehammers to split stone sections from masses.
Cut grooves along outlines, using chisels.
Remove pieces of stone from larger masses, using jackhammers, wedges, and other tools.
Set charges of explosives to split rock.
Drill holes into sides of stones broken from masses, insert dogs or attach slings, and direct removal of stones.
Cut slabs of stone into sheets that will be used for floors or counters.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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