Your job under AI
About this app
Explore 20 occupations as task cubes with ILO exposure scores — and discover why exposure is not a job-loss forecast.
What AI exposure means
An occupation is a bundle of different tasks. In a 2025 working paper (Gmyrek et al.), the International Labour Organization (ILO) estimated how exposed tasks in the ISCO-08 occupational classification are to generative AI, as an index from 0 to 1. Exposure describes technical potential for change. It is not a probability of job loss and says nothing about time actually saved. Whether an occupation changes also depends on institutions, adoption costs and human responsibility.
What the app shows
The app covers 20 occupations, from university teachers and software developers to nurses, general practitioners, cooks and electricians, each with 5 to 11 tasks. Every task is a cube of the same size. When you unpack an occupation, the cubes line up by score from left (low) to right (high); colour and position encode the same value. Scores are taken unchanged from the study’s dataset, and the English task texts follow the source.
Things to try
Choose your occupation and a second one to compare. The slider “From occupation to task” bundles or unpacks the cubes. Tap a cube to read the task and its score; every task can also be selected from the list. Notice how widely scores spread within a single occupation. Cube size does not represent time shares, and height and depth only separate the cubes visually.
Frequently asked questions
- Which jobs are most exposed to generative AI?
- According to the 2025 ILO study, clerical and administrative tasks and text-heavy work are the most exposed. Manual and physical tasks usually score low. Scores vary considerably within one occupation, which is why looking at individual tasks is useful.
- Does high AI exposure mean a job will disappear?
- No. Exposure measures how strongly a task could technically be changed by generative AI. Whether that leads to automation, assistance or new tasks depends on costs, regulation, organisation and responsibility. The study makes no job-loss forecasts.
- Where do the exposure scores come from?
- The scores come from ILO Working Paper 140 (Gmyrek et al., 2025) and the authors’ accompanying dataset. They refer to tasks in the international occupational classification ISCO-08. The data are licensed under CC BY 4.0.
- What can I do in this app?
- You choose one of 20 occupations, compare it with a second one and unpack both into their tasks. Tap any task to read it with its score. Source and licence are listed in the info panel.
Subject: Neural networks
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