Will AI replace Materials Engineers?
Work in Materials Engineers sits in the in-between: AI reaches some of it (~49% in theory) but is only measured doing about 0% today — part human, part machine.
O*NET-SOC 17-2131
How your 20 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 (~49%). By late 2025, real-world AI use had reached about 0% 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.
The signals here line up
Theoretical reach (~49%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 20 tasks, ratedBased on real task-level AI scores — click to collapse
- Perform managerial functions, such as preparing proposals and budgets, analyzing labor costs, and writing reports.
- Replicate the characteristics of materials and their components, using computers.
- Write for technical magazines, journals, and trade association publications.
- Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.
- Design and direct the testing or control of processing procedures.
- Monitor material performance, and evaluate its deterioration.
- Conduct or supervise tests on raw materials or finished products to ensure their quality.
- Evaluate technical specifications and economic factors relating to process or product design objectives.
- Determine appropriate methods for fabricating and joining materials.
- Guide technical staff in developing materials for specific uses in projected products or devices.
- Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical conductivity, and cost.
- Supervise the work of technologists, technicians, and other engineers and scientists.
- Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.
- Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.
- Solve problems in a number of engineering fields, such as mechanical, chemical, electrical, civil, nuclear, and aerospace.
- Present technical information at conferences.
- Design processing plants and equipment.
- Modify properties of metal alloys, using thermal and mechanical treatments.
- Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic materials production operations.
- Conduct training sessions on new material products, applications, or manufacturing methods for customers and their employees.
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.