ETL開発者
コンピュータ・数学AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.288
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.53
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは14%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 9 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.15-1252 | 0.200057.1 | 0.340073.9 |
Prepare reports or correspondence concerning project specifications, activities, or status.15-1252 | 0.050014.3 | 0.02004.3 |
Consult with customers or other departments on project status, proposals, or technical issues, such as software system design or maintenance.15-1252 | 0.040011.4 | 0.02004.3 |
Analyze information to determine, recommend, and plan installation of a new system or modification of an existing system.15-1252 | 0.03008.6 | 0.03006.5 |
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.15-1252 | 0.01002.9 | 0.02004.3 |
Store, retrieve, and manipulate data for analysis of system capabilities and requirements.15-1252 | 0.01002.9 | 0.02004.3 |
Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design.15-1252 | 0.01002.9 | 0.00000.0 |
Obtain and evaluate information on factors such as reporting formats required, costs, or security needs to determine hardware configuration. | —0 | 0.01002.2 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Analyze information to determine, recommend, and plan installation of a new system or modification of an existing system.O*NET Task ID 21661 | 1.0 |
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.O*NET Task ID 21662 | 1.0 |
Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces.O*NET Task ID 21664 | 1.0 |
Coordinate installation of software system.O*NET Task ID 21666 | 1.0 |
Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design.O*NET Task ID 21667 | 1.0 |
Determine system performance standards.O*NET Task ID 21668 | 1.0 |
Develop or direct software system testing or validation procedures, programming, or documentation.O*NET Task ID 21669 | 1.0 |
Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.O*NET Task ID 21670 | 1.0 |
Monitor functioning of equipment to ensure system operates in conformance with specifications.O*NET Task ID 21671 | 1.0 |
Obtain and evaluate information on factors such as reporting formats required, costs, or security needs to determine hardware configuration.O*NET Task ID 21672 | 1.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page