AI Job Risk in Science
Science produces enormous amounts of text and data, and AI is already changing how much of that gets processed by hand. Literature review, data cleaning, and first-pass comparison of models or methods can now be done far faster than a researcher working alone. The tension is that generating a summary of prior work is not the same as knowing which question is worth asking, and running an analysis is not the same as knowing whether its result actually means what it appears to mean. Those judgment calls sit with a scientist's training and reputation, not with the tool that produced the draft.
Industry Average Risk Score
32.33
Jobs Analyzed
9
How to read this page in practice
The notes below explain how to interpret the score, where automation pressure tends to show up first, and where human-led value is more likely to remain inside this industry.
How to Read This Industry
Read AI's effect in science by separating information processing from the scientific judgment around it. Searching and summarizing literature, cleaning and preprocessing datasets, running standard statistical or model comparisons, and drafting routine lab records move much faster with current tools. Formulating a genuinely novel question, designing an experiment that actually tests it, and interpreting an ambiguous or surprising result do not move at the same pace, because they depend on domain judgment that a tool cannot originate.
What Automation Hits First
AI moves first through literature search and summarization across large numbers of papers, through data cleaning and preprocessing pipelines, through drafting and formatting of lab notebooks and routine reports, and through rapid first-pass comparison of models, parameters, or analysis approaches. Some experimental workflows already use automated instruments for repetitive sample processing. It stalls on the core of research: deciding which question is worth pursuing, designing an experiment or study that isolates the right variable, judging whether a surprising result reflects reality or an artifact, and taking responsibility for a claim published under a researcher's name.
What Still Depends on People
What stays durably human in science is originating and judging, not processing. Principal investigators and researchers framing which questions matter, experimentalists designing studies that control for the right variables, scientists interpreting ambiguous or conflicting results in context, and peer reviewers and mentors exercising judgment about what counts as a valid claim all depend on domain expertise and accountability that a model can assist but not replace.
How to Use the Gap
For science roles, weigh how much of the work is literature search, data processing, or routine analysis versus posing questions and interpreting results. Roles concentrated in literature review, data cleaning, or standard model comparisons should expect a higher score, since those tasks are already being compressed by current tools. Roles centered on original question framing, experimental design, or interpreting ambiguous findings should read a lower score as reflecting judgment that remains squarely human.
Jobs Most At Risk from AI
This table is a current snapshot of jobs in this industry that sit on the higher-risk side. Read it together with the fixed commentary above rather than as a permanent list of examples.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Research Assistant | 51 |
| 2 | Meteorologist | 46 |
| 3 | Chemist | 35 |
| 4 | Biologist | 30 |
| 5 | Sociologist | 29 |
| 6 | Anthropologist | 27 |
| 7 | Geologist | 26 |
| 8 | Physicist | 25 |
| 9 | Astronomer | 22 |
Jobs Safest from AI
This table shows the jobs in this industry that currently sit on the lower-risk side. Use it as a comparison of task structure, not as a promise that these roles will never change.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Astronomer | 22 |
| 2 | Physicist | 25 |
| 3 | Geologist | 26 |
| 4 | Anthropologist | 27 |
| 5 | Sociologist | 29 |
| 6 | Biologist | 30 |
| 7 | Chemist | 35 |
| 8 | Meteorologist | 46 |
| 9 | Research Assistant | 51 |
Frequently asked questions
Q.Which jobs in Science are most exposed to AI?
In Science, the jobs with the highest AI risk scores include Research Assistant. The full ranking of the most and least exposed Science jobs is shown above.
Q.Which Science jobs are safest from AI?
The Science roles least exposed to AI automation include Astronomer. These tend to depend on judgment, physical presence, or accountability that current AI cannot take on.
Q.Is Science safe from AI?
No industry is uniformly safe or at risk. Within Science, routine information-handling roles are far more exposed than roles built on judgment and responsibility, so the score is best read as a task-exposure signal rather than a prediction of job loss.
Q.How is the Science AI risk score calculated?
It is the average AI risk across the Science jobs we track, refreshed weekly. See the methodology page for how the underlying scores are produced and how to interpret them.