电子游戏开发者
计算机与数学AI暴露度
- 数据来源: BLS发布时间: 2026-08
很高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: 2026-03
0.288
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
0.53
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 14%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
显示隐藏的 11 项工作| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Modify existing software to correct errors, allow it to adapt to new hardware, or to improve its performance.15-1132 | 5.974659.1 | 9.960757.1 |
Modify existing software to correct errors, to adapt it to new hardware, or to upgrade interfaces and improve performance.15-1133 | 2.566625.4 | 4.597226.3 |
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.15-1132 | 0.46444.6 | 1.46648.4 |
Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.15-1132 | 0.26692.6 | 0.21851.3 |
Store, retrieve, and manipulate data for analysis of system capabilities and requirements.15-1133 | 0.21512.1 | 0.50062.9 |
Analyze information to determine, recommend, and plan computer specifications and layouts, and peripheral equipment modifications.15-1132 | 0.11881.2 | 0.02810.2 |
Develop and direct software system testing and validation procedures, programming, and documentation.15-1132 | 0.08410.8 | 0.32071.8 |
Evaluate factors such as reporting formats required, cost constraints, or need for security restrictions to determine hardware configuration.15-1133 | 0.07380.7 | 0.00560.0 |
Direct software programming and development of documentation.15-1133 | 0.07100.7 | 0.05470.3 |
Consult with customers about software system design and maintenance.15-1132 | 0.05970.6 | 0.00840.0 |
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
职业信息
与本职业相关的近期变化
2026年7月: ADP/Stanford linked postings-payroll study of ~7,000 IT workers (2019-2025) prices tasks separately within IT jobs. Advising others on the design or use of technologies is among 8 higher-wage activities; five tasks lost compensation value in 2023-2025 vs 2019-2022, including "develop models of systems, processes, or products." Effect sizes were not published.
[来源: ADP Research / Stanford Digital Economy Lab, Unbundling Jobs (July 2026)]2026年3月: EIG (Mar 2026) argues the weakness in young adults' labor market is about age, not education: young workers of all education levels lag the rest of the labor market, which does not fit the media narrative of AI displacing computer science majors and entry-level graduates.
[来源: EIG: AI and Young-Adult Jobs (March 2026)]2026年3月: Brookings (Mar 2026) says research on AI and the labor market is still in its first inning, and studies disagree: ADP payroll data show employment fell more for young workers in high-AI-exposure occupations, while CPS data show unemployment rose less for workers in more exposed occupations.
[来源: Brookings Institution]These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.