Fire Fighters

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Fire Fighters (ISCO-08 5411) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 29% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Informing the public about fire prevention.”

Scores 0.36 on the 2025 scale. The task of "Informing the public about fire prevention" primarily involves communication, education, and dissemination of safety information, which can be supported by Generative AI through content creation and customization to suit different audiences. However, like tasks that involve real-time human judgment and situational awareness, such as "Handling matters related to flood protection and fire protection" (score 0.26) and "Complying with applicable instructions and regulations when performing rescue and firefighting activities" (score 0.175), the task requires human intuition to effectively engage the public and address their concerns. The task also involves understanding human behavior, as seen in tasks like "Educating about the importance of avoiding road risk" (score 0.34), emphasizing the need for empathy and adaptability. AI can generate content and support in providing general information, but the nuanced real-time interaction and personalized engagement still require human involvement. Therefore, a score of 0.27 reflects the potential for AI to assist alongside the necessity for human oversight and interaction. The use of AI in this context could help automate the dissemination of generic information while human input tailors the message to effectively reach and influence the public, particularly in a well-connected setting like Poland.

Moving fastest, 2023 → 2025

“Preventing or limiting the spread of dangerous substances in case of fires or accidents;”

Model capability on this task changed by +0.13 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 5411, 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.

• Firefighters
• First-Line Supervisors of Firefighting and Prevention Workers

In context

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

Fire Fighters sit at the 29th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Fire Fighters rank in the 29th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Informing the public about fire prevention.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Fire Fighters". https://singulariki.com/gradient/5411-fire-fighters.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.