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A/KAI Kills What?

Dossier #254 // career autopsy

Lower exposure Low confidence

Materials Scientists

Category: General · SOC 19-2032.00 · Industry: Cross-industry

AI Exposure

8/100
0.0

Very low exposure

Last updated

12 Oct 2026

16 hours ago · sources: BLS OES (approx) · O*NET

Evidence

0 sources

15 core tasks shown

Verdict

Materials Scientists Lower exposure: only 0 of 15 core tasks show high task automation potential : exposure 8/100 (very low exposure).

No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.

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Score breakdown

Why this score?

Every component is stored with the score row and can be recomputed from the evidence table.

AI Exposure (composite)8/100

Methodology 2026-09-a · score v1.0 · input hash edcb6adbcb92dde7

AI capability exposure0/100

Strength of current models on the capabilities this job's tasks require.

Task automation potential25/100

Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.

Observed AI adoption0/100

Evidence that AI is actually being used for this work.

Labour-market signal0/100

Observed changes in postings, employment or earnings.

Evidence strength0/100

Quantity and source quality of the evidence behind the score.

0 evidence item(s) contribute. Calculated 16 hours ago. 12 Oct 2026 UTC.

Overview

Research and study the structures and chemical properties of various natural and synthetic or composite materials, including metals, alloys, rubber, ceramics, semiconductors, polymers, and glass. 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. Includes glass scientists, ceramic scientists, metallurgical scientists, and polymer scientists.

Employment

-

approx. BLS OES

Median wage

-

approx. BLS OES

Tasks tracked

15

linked to this occupation

Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.

Tasks

Task-level automation exposure

Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.

Evidence

Evidence trail

0 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.

No occupation-specific evidence has been mapped yet. This occupation is scored from generic task assessment only - a genuinely weaker basis, and the reason its score is marked low-confidence.

Historical trend

Exposure over time

Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.

Materials Scientists AI exposure backcast, quarterly from 2021Reconstructed exposure series with 9 checkpoints. Current value 8 out of 100. 9 observed snapshots recorded since tracking began.1000502026-10-042026-10-12888
Score · 9 points · 2026-10-04 to 2026-10-12

Reconstruction, not measurement. 9 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.

Recent events

Signals affecting this occupation

No events match this filter yet. New verified events appear here as analysis completes.

Related

Other occupations in this industry