Will AI replace Atmospheric and Space Scientists?
In theory, AI could do about 57% of the work in Atmospheric and Space Scientists. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 19-2021
How your 19 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a moderate share of this job's tasks (~57%). By late 2025, real-world AI use had reached about 4% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Don't trust a single AI-risk score here
For this job, the signals disagree sharply. AI's theoretical reach looks moderate (~57%), but real-world use is only ~4%, and how much AI "can" do shifts wildly by model — one 2026 study found the share of "high-risk" jobs swung 2.7% to 51.5% just by changing which AI did the rating. This page shows the spread instead of pretending there's one number.
See all 19 tasks, ratedBased on real task-level AI scores — click to collapse
- Develop or use mathematical or computer models for weather forecasting.
- Prepare scientific atmospheric or climate reports, articles, or texts.
- Speak to the public to discuss weather topics or answer questions.
- Develop computer programs to collect meteorological data or to present meteorological information.
- Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media.
- Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts.
- Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics.
- Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics.
- Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information.
- Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups.
- Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends.
- Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling.
- Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns.
- Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate.
- Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings.
- Develop and deliver training on weather topics.
- Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion.
- Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices.
- Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications.
- ⚠️ None — every core task is at least partly within AI's reach. The job won't vanish, but almost all of it changes.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.