Bicycle and Related Repairers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Bicycle and Related Repairers (ISCO-08 7234) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 11% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Examining, servicing and repairing bicycles and other non-motorized transport equipment;”

Scores 0.15 on the 2025 scale. The task of examining, servicing, and repairing bicycles and non-motorized transport equipment involves a substantial amount of manual labor, tool use, and mechanical knowledge, which Generative AI cannot currently automate. This task is comparable to other tasks in the context that involve physical repair and inspection, such as "Removing faults in assemblies and systems of a single-track vehicle" (adjusted score: 0.15) and "Conducting technical inspections, carrying out current, medium and major repairs of machines and equipment" (adjusted score: 0.15), which require human dexterity and judgment. While AI might assist by providing diagnostic information or repair guides, the essential aspects of the work—like tactile assessment and physical manipulation—remain outside AI's capability to automate. Given the similar nature of these tasks and considering the technological infrastructure in a high-income country like Poland, the adjusted score of 0.16 reflects the limited role of AI, focused on providing supplementary support rather than replacing human involvement.

Moving fastest, 2023 → 2025

“Examining, servicing and repairing bicycles and other non-motorized transport equipment;”

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 7234, 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.

• First-Line Supervisors of Mechanics, Installers, and Repairers
• Bicycle Repairers

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.

Bicycle and Related Repairers sit at the 11th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Bicycle and Related Repairers rank in the 11th 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 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Examining, servicing and repairing bicycles and other non-motorized transport equipment;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Bicycle and Related Repairers". https://singulariki.com/gradient/7234-bicycle-and-related-repairers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.