Blacksmiths, Hammersmiths and Forging Press Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Blacksmiths, Hammersmiths and Forging Press Workers (ISCO-08 7221) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 20% 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

“Measuring and inspecting machine parts to ensure conformance to product specifications.”

Scores 0.26 on the 2025 scale. The task of measuring and inspecting machine parts to ensure conformance to product specifications is a hands-on, precise task requiring physical dexterity and detailed inspection skills. This aligns with semantically similar tasks involving the inspection of machinery, quality control, and the use of measuring and control equipment, which generally scored low for automation potential within the provided context, such as "Detecting and removing defective work and leaks in precision devices" (0.285) and "Controlling the quality of orthopedic products" (0.2195). These tasks require human sensory input and manual intervention, which Generative AI cannot yet replicate. While AI could assist with data collection and analysis, such as flagging potential deviations from specifications, the real-time, tactile and nuanced judgment necessary for this task remains reliant on human expertise. Given the average capabilities of Generative AI, and considering the technological access in a high-income country like Poland, the adjusted score reflects AI's supportive but limited role in automating this task.

Moving fastest, 2023 → 2025

“Measuring and inspecting machine parts to ensure conformance to product specifications.”

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 7221, 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.

• Metal Workers and Plastic Workers, All Other
• Forging Machine Setters, Operators, and Tenders, Metal and Plastic

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.

Blacksmiths, Hammersmiths and Forging Press Workers sit at the 20th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Blacksmiths, Hammersmiths and Forging Press Workers rank in the 20th 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 rose by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Measuring and inspecting machine parts to ensure conformance to product specifications.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Blacksmiths, Hammersmiths and Forging Press Workers". https://singulariki.com/gradient/7221-blacksmiths-hammersmiths-and-forging-press-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.