Underwater Divers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Underwater Divers (ISCO-08 7541) score an average of 0.16 on a 0–1 exposure scale — more exposed than about 19% 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 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Obtaining information about diving tasks and environmental conditions.”

Scores 0.36 on the 2025 scale. The task of obtaining information about diving tasks and environmental conditions involves refining and synthesizing diverse data sources, interpreting contextual factors, and potentially requiring a level of human expertise to ensure accuracy and relevancy in dynamic and often unpredictable environments. Semantically similar tasks, such as monitoring environmental conditions or ensuring compliance with safety regulations, reflect a low automation potential due to their reliance on human oversight, situational awareness, and manual input (e.g., checking the level of gas concentration, monitoring the environment). Generative AI can support data aggregation and preliminary analysis, providing insights and recommendations, but it lacks the capability for full automation, particularly in nuanced or context-specific scenarios. The adjusted automation score reflects AI's potential to assist in information processing while acknowledging the dependence on human skills for precision and interpretation in a high-income setting like Poland, where the digital infrastructure supports AI-assisted automation.

Moving fastest, 2023 → 2025

“Taking safety precautions, such as monitoring dive lengths and depths, and registering with authorities before diving expeditions begin;”

Model capability on this task changed by +0.09 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 7541, 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.

• Commercial Divers

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.

Underwater Divers sit at the 19th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Underwater Divers rank in the 19th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Obtaining information about diving tasks and environmental conditions.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Underwater Divers". https://singulariki.com/gradient/7541-underwater-divers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.