Subsistence Livestock Farmers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Subsistence Livestock Farmers (ISCO-08 6320) 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 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Buying, bartering and selling animals and some products.”

Scores 0.21 on the 2025 scale. The task of "Buying, bartering and selling animals and some products" involves a mix of negotiation, market understanding, regulatory compliance, and interpersonal interaction, which are only partially automatable using Generative AI. The given tasks with similar context such as "Organizing the sale of live animals..." (score 0.37) and "Sale of breeding and utility materials" (score 0.215) highlight the role of human judgment in sales and negotiation tasks. The adjusted scores for similar tasks suggest some roles AI can play in supporting sales through data analysis and customer interactions, but not in completely replacing the nuanced and context-specific human elements. The scores in the semantic context range from 0.05 to 0.39, indicating minimal to moderate automation potential. Generative AI can assist with data-driven insights, market prediction, and transactional automation but is limited by the need for human oversight in negotiations and customer relations, even in a technologically advanced environment like Poland. Thus, considering the supportive but constrained role of AI in this task, the adjusted score is set at 0.18, reflecting modest potential for automation while acknowledging the need for human expertise and decision-making.

Moving fastest, 2023 → 2025

“Herding or leading livestock to pastures, grazing land and water supplies;”

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 6320, 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.

• Farmworkers, Farm, Ranch, and Aquacultural Animals

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.

Subsistence Livestock Farmers sit at the 10th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Subsistence Livestock Farmers 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 rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Buying, bartering and selling animals and some products.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Subsistence Livestock Farmers". https://singulariki.com/gradient/6320-subsistence-livestock-farmers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.