Miners and Quarriers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Miners and Quarriers (ISCO-08 8111) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 23% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Completing records detailing operations completed during shifts;”

Scores 0.46 on the 2025 scale. The task of completing records detailing operations completed during shifts, similar to maintaining process progress records and maintaining production documentation, heavily involves data entry, organization, and standard reporting procedures. Generative AI can significantly assist in automating the routine aspects of this task, such as data entry, consistency checks, and initial report generation. Tasks like maintaining required records of process progress have seen adjusted scores of around 0.45, suggesting a strong potential for automation. However, human oversight remains crucial to ensure the accuracy and context-specific details are correctly captured, particularly in industrial settings where variability and human judgment are often required. Given the task's resemblance to documenting completed mining works (score 0.46) and the structured nature of the task, a slightly increased automation score of 0.47 is justified. This reflects confidence in Generative AI's capabilities while acknowledging the ongoing need for human verification and contextual accuracy in shift operation records, particularly in a high-income country like Poland.

Moving fastest, 2023 → 2025

“Operating machinery to open new shafts, drives, air vents and rises;”

Model capability on this task changed by +0.06 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 8111, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Continuous Mining Machine Operators
• Underground Mining Machine Operators, All Other
• Rock Splitters, Quarry

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Miners and Quarriers sit at the 23rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Miners and Quarriers rank in the 23rd percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.07 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Completing records detailing operations completed during shifts;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Miners and Quarriers". https://singulariki.com/gradient/8111-miners-and-quarriers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.