Geologists and geophysicists

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Geologists and geophysicists (ISCO-08 2114) score an average of 0.36 on a 0–1 exposure scale — more exposed than about 67% 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 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Charting the Earth's magnetic field and applying this and other collected data for broadcasting, navigation and other purposes;”

Scores 0.51 on the 2025 scale. The task of "Charting the Earth's magnetic field and applying this and other collected data for broadcasting, navigation, and other purposes" involves a combination of technical expertise, data analysis, and potentially some manual component in setting up or adjusting equipment to measure the Earth's magnetic field. Semantically similar tasks such as using electronic geodetic instruments (0.435), performing meteorological measurements (0.65), and conducting environmental monitoring (0.535) demonstrate varying levels of automation potential depending on the balance of manual versus analytical aspects. Given the nature of this task, AI can significantly assist in processing magnetic field data, analyzing patterns, and even generating preliminary maps or reports. However, the requirement for human oversight to interpret complex data, address anomalies, and make adjustments based on environmental factors remains significant. Additionally, the task's application areas, like broadcasting and navigation, necessitate a high degree of precision that currently depends on human expertise. Therefore, considering both the automation potential of Generative AI in data analysis tasks and the necessity of human intuition for accurate interpretation, an adjusted score of 0.57 reflects this balance in a technologically advanced nation like Poland.

Moving fastest, 2023 → 2025

“Studying and measuring physical properties of seas and the atmosphere and their inter-relationship, such as the exchange of thermal energy;”

Model capability on this task changed by +0.30 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 2114, 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.

• Physical Scientists, All Other
• Remote Sensing Scientists and Technologists
• Geoscientists, Except Hydrologists and Geographers
• Hydrologists

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Geologists and geophysicists sit at the 67th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Geologists and geophysicists rank in the 67th 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: "Charting the Earth's magnetic field and applying this and other collected data for broadcasting, navigation and other purposes;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Geologists and geophysicists". https://singulariki.com/gradient/2114-geologists-and-geophysicists.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.