Will AI replace Chemists?
Work in Chemists sits in the in-between: AI reaches some of it (~38% in theory) but is only measured doing about 26% today — part human, part machine.
O*NET-SOC 19-2031
How your 12 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 (~38%). By late 2025, real-world AI use had reached about 26% of its task activity (already common). 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 (~38%), real-world use (~26%) 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 12 tasks, ratedBased on real task-level AI scores — click to collapse
- Write technical papers or reports or prepare standards and specifications for processes, facilities, products, or tests.
- Analyze organic or inorganic compounds to determine chemical or physical properties, composition, structure, relationships, or reactions, using chromatography, spectroscopy, or spectrophotometry techniques.
- Evaluate laboratory safety procedures to ensure compliance with standards or to make improvements as needed.
- Compile and analyze test information to determine process or equipment operating efficiency or to diagnose malfunctions.
- Confer with scientists or engineers to conduct analyses of research projects, interpret test results, or develop nonstandard tests.
- Develop, improve, or customize products, equipment, formulas, processes, or analytical methods.
- Direct, coordinate, or advise personnel in test procedures for analyzing components or physical properties of materials.
- Purchase laboratory supplies, such as chemicals, when supplies are low or near their expiration date.
- Conduct quality control tests.
- Maintain laboratory instruments to ensure proper working order and troubleshoot malfunctions when needed.
- Prepare test solutions, compounds, or reagents for laboratory personnel to conduct tests.
- Induce changes in composition of substances by introducing heat, light, energy, or chemical catalysts for quantitative or qualitative analysis.
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.