Aquaculture Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Aquaculture Workers (ISCO-08 6221) score an average of 0.22 on a 0–1 exposure scale — more exposed than about 41% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Delivering or marketing products;”

Scores 0.56 on the 2025 scale. The task of "Delivering or marketing products" involves elements of logistics, customer engagement, and potential promotional activities. Given the context and the capabilities of Generative AI, automation potential is present but limited, especially for the physical delivery aspect, which AI cannot perform. Generative AI can aid in optimizing delivery routes, handling promotional content creation, and generating marketing strategies, similar to the task of "Organizing promotional events" (0.365) and "Presenting company product offers" (0.3775), which require creative and strategic inputs. On the marketing side, AI excels in data-driven strategies and creating marketing content, similar to tasks like "Promoting new culinary products" (0.275) and "Marketing principles" (0.375). However, human involvement is essential for creativity, strategic decision-making, and interpersonal interactions which AI cannot replace. Given the upper range of similar tasks related to data processing and promotion, and considering the task's execution in an advanced technological environment like Poland, an adjusted score of 0.475 reflects a balance between automation capabilities and the necessity of human contribution.

Moving fastest, 2023 → 2025

“Delivering or marketing products;”

Model capability on this task changed by +0.21 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 6221, 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 Farming, Fishing, and Forestry Workers
• Animal Breeders

In context

Part of the 6 - Skilled agricultural, forestry and fishery workers major group. Return to the full gradient to see how the whole group sits.

Aquaculture Workers sit at the 41st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Aquaculture Workers rank in the 41st 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Delivering or marketing products;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Aquaculture Workers". https://singulariki.com/gradient/6221-aquaculture-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.