Carpenters and Joiners

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Carpenters and Joiners (ISCO-08 7115) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 10% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Constructing, assembling, altering and repairing wooden fixtures and fittings in train coaches, aircraft, ships, boats, floats and other vehicles.”

Scores 0.15 on the 2025 scale. The task of constructing, assembling, altering, and repairing wooden fixtures and fittings in train coaches, aircraft, ships, boats, floats, and other vehicles involves significant manual dexterity, physical manipulation, and craftsmanship, which Generative AI cannot currently automate. Similar tasks such as preparing carpentry elements for assembly (0.125) and making non-standardized parts (0.125) involve hands-on skill and are scored low due to their manual nature. Although AI can assist with design aids, providing guidelines, and optimizing certain processes, the core physical actions required remain firmly in the human domain. Given the high-income country context of Poland, where AI can enhance efficiency through planning and informational support, the adjusted score reflects the limited role AI can play in directly performing these tasks, aligning with the constraints observed in other semantically similar manual tasks in the context.

Moving fastest, 2023 → 2025

“Constructing, assembling, altering and repairing wooden fixtures and fittings in train coaches, aircraft, ships, boats, floats and other vehicles.”

Model capability on this task changed by +0.05 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 7115, 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.

• Carpenters

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.

Carpenters and Joiners sit at the 10th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Carpenters and Joiners rank in the 10th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Constructing, assembling, altering and repairing wooden fixtures and fittings in train coaches, aircraft, ships, boats, floats and other vehicles.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Carpenters and Joiners". https://singulariki.com/gradient/7115-carpenters-and-joiners.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.