Stonemasons, Stone Cutters, Splitters and Carvers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Stonemasons, Stone Cutters, Splitters and Carvers (ISCO-08 7113) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 4% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Selecting and grading slabs and blocks of granite, marble, and other stone;”

Scores 0.14 on the 2025 scale. The task of selecting and grading slabs and blocks of granite, marble, and other stone involves significant manual dexterity, tactile feedback, and domain-specific expertise to assess material quality and suitability. Comparable tasks in the cluster, such as "Selecting tools, machines and equipment" and "Assessing the condition and preparing the substrate of walls," scored between 0.107 and 0.175, reflecting the manual nature and reliance on human judgment. Generative AI's potential role is limited to providing guidance on material specifications or criteria based on established data but cannot replace the on-site human assessment required for this task. The task's inherent manual and skill-based nature aligns it closely with lower automation potential observed in other physical tasks like repairing or manipulating stone and related materials. Therefore, in the context of a high-income country with good technological access, I assigned a slightly higher score than the most manual tasks due to potential AI-assisted inspection or classification support but tempered it below tasks involving more cognitive elements.

Moving fastest, 2023 → 2025

“Selecting and grading slabs and blocks of granite, marble, and other stone;”

Model capability on this task changed by +0.04 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 7113, 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.

• Stonemasons
• Segmental Pavers

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Stonemasons, Stone Cutters, Splitters and Carvers sit at the 4th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Stonemasons, Stone Cutters, Splitters and Carvers rank in the 4th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selecting and grading slabs and blocks of granite, marble, and other stone;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Stonemasons, Stone Cutters, Splitters and Carvers". https://singulariki.com/gradient/7113-stonemasons-stone-cutters-splitters-and-carvers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.