Astrologers, Fortune-tellers and Related Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Astrologers, Fortune-tellers and Related Workers (ISCO-08 5161) score an average of 0.30 on a 0–1 exposure scale — more exposed than about 56% 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 Minimal 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

“Forecasting future events on the basis of these interpretations;”

Scores 0.58 on the 2025 scale. The task of forecasting future events based on interpretations shares similarities with other data-driven analytical tasks within the provided context, such as "Analyzing climate changes" and "Creating strategic and operational action plans." These tasks received adjusted scores of 0.57 and 0.535, respectively, reflecting the potential of Generative AI to assist significantly in data processing and trend identification while acknowledging the need for human expertise in nuanced interpretation and decision-making. Given Generative AI's capabilities in automating the analysis of large datasets, suggesting predictive patterns, and generating preliminary forecasts, this task's automation potential in a high-income country is considerable. However, the necessity for human judgment in interpreting complex scenarios and understanding context-specific factors remains critical. Thus, the adjusted score reflects a high potential for automation, emphasizing AI's supportive role while recognizing the indispensable human element in refining and contextualizing forecasts.

Moving fastest, 2023 → 2025

“Forecasting future events on the basis of these interpretations;”

Model capability on this task changed by −0.02 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 5161, 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.

• Entertainers and Performers, Sports and Related Workers, All Other

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Astrologers, Fortune-tellers and Related Workers sit at the 56th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Astrologers, Fortune-tellers and Related Workers rank in the 56th 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 0.24 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Forecasting future events on the basis of these interpretations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Astrologers, Fortune-tellers and Related Workers". https://singulariki.com/gradient/5161-astrologers-fortune-tellers-and-related-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.