AI Automation Index

Will AI Replace Materials Scientists?

Task-level automation exposure for Materials Scientists: 16 O*NET tasks scored against their work activities, plus the simulator.

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Automation Exposure

46% weighted task index

16 of 16 tasks scored

Median Salary

$117,790 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

8,470 people hold this job — size, not growth

Projected 2025–35

+8.3% employment change

600 openings a year · BLS line item

Pay by state

BLS OEWS publishes 30 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$69,010 – $177,030

Highest medians

California $177,030

Oregon $150,550

Wisconsin $137,700

District of Columbia $137,600

Massachusetts $132,600

Lowest medians

Tennessee $69,010

Montana $71,810

Texas $72,700

Iowa $88,320

Georgia $91,970

State figures come from the 19-2032 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 46%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 0%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 30%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 12%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 6%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 14/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

16 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 19%

3 tasks

Medium exposure (≥ 35%) 55%

9 tasks

Low exposure (below 35%) 26%

4 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

Revision 08/2024 · Occupational Expert

Research or design methods of processing, forming, and firing materials to develop products, such as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.

Replaced: Research methods of processing, forming, and firing materials to develop such products as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.
New 08/2024 · Occupational Expert

Review and select materials for products to meet product design and cost requirements.

Constituent Tasks

#9087 · Core · 42% automatable

Conduct research on the structures and properties of materials, such as metals, alloys, polymers, and ceramics, to obtain information that could be used to develop new products or enhance existing ones.

data analysis — Product research aided; physical testing human

Conduct research to gain information about products or processes.

#9098 · Core · 28% automatable

Test metals to determine conformance to specifications of mechanical strength, strength-weight ratio, ductility, magnetic and electrical properties, and resistance to abrasion, corrosion, heat, and cold.

visual inspection — Quality testing of materials is hands-on inspection

Test quality of materials or finished products.

#9097 · Core · 28% automatable

Test material samples for tolerance under tension, compression, and shear to determine the cause of metal failures.

visual inspection — Quality testing of materials is hands-on inspection

Test quality of materials or finished products.

#9090 · Core · 50% automatable

Determine ways to strengthen or combine materials or develop new materials with new or specific properties for use in a variety of products and applications.

decision support — AI aids product development; prototyping and testing human

Develop new or advanced products or production methods.

#20214 · Core · 72% automatable

Prepare reports, manuscripts, proposals, and technical manuals for use by other scientists and requestors, such as sponsors and customers.

text generation — Reports drafted; presentations need human delivery

Prepare scientific or technical reports or presentations.

#9085 · Core · 55% automatable

Plan laboratory experiments to confirm feasibility of processes and techniques used in the production of materials with special characteristics.

decision support — Study designs aided; scientific accountability human

Design research studies to obtain scientific information.

#9091 · Core · 65% automatable

Recommend materials for reliable performance in various environments.

decision support — New-tech advice needs organizational context judgment

Advise others on the development or use of new technologies.

#20215 · Core · 45% automatable

Supervise and monitor production processes to ensure efficient use of equipment, timely changes to specifications, and project completion within time frame and budget.

decision support — Conformance checking aided by AI, but on-site verification is human

Monitor operational procedures in technical environments to ensure conformance to standards.

#9094 · Core · 42% automatable Revision

Research methods of processing, forming, and firing materials to develop such products as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.

data analysis — Product research aided; physical testing human

Conduct research to gain information about products or processes.

#20213 · Core · 40% automatable

Perform experiments and computer modeling to study the nature, structure, and physical and chemical properties of metals and their alloys, and their responses to applied forces.

decision support — Physical theory needs expert scientific judgment

Develop theories or models of physical phenomena.

#9089 · Core · 60% automatable

Devise testing methods to evaluate the effects of various conditions on particular materials.

decision support — Protocols drafted; scientific validity human-owned

Devise research or testing protocols.

#9092 · Core · 28% automatable

Test individual parts and products to ensure that manufacturer and governmental quality and safety standards are met.

visual inspection — Quality testing of materials is hands-on inspection

Test quality of materials or finished products.

#9086 · Core · 50% automatable

Confer with customers to determine how to tailor materials to their needs.

interpersonal — Client information exchange is live relational work

Confer with clients to exchange information.

#9093 · Core · 30% automatable

Visit suppliers of materials or users of products to gather specific information.

interpersonal — Observation and interviews are inherently human

Collect information from people through observation, interviews, or surveys.

#21175 · Core · 55% automatable

Write research papers for publication in scientific journals.

text generation — AI drafts; original expertise and authorship are human

Write articles, books or other original materials in area of expertise.

#9099 · Supplemental · 55% automatable

Teach in colleges and universities.

text generation — Science instruction content AI-assisted; delivery human

Instruct college students in physical or life sciences.

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Community Sentiment

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Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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