Software Developers
Computer and Mathematical Occupations- Código O*NET-SOC
- 15-1252.00
Research, design, and develop computer and network software or specialized utility programs. Analyze user needs and develop software solutions, applying principles and techniques of computer science, engineering, and mathematical analysis. Update software or enhance existing software capabilities. May work with computer hardware engineers to integrate hardware and software systems, and develop specifications and performance requirements. May maintain databases within an application area, working individually or coordinating database development as part of a team.
Exposición a la IA
- Fuente de datos: BLSPublicado: 2026-08
Muy alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: 2026-03
0.288
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
Publicado por ocupación SOC 2018; toda ocupación O*NET con el mismo código SOC 2018 recibe este valor
- Fuente de datos: ILOPublicado: 2025
0.53
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Publicado por grupo primario ISCO-08. Vinculado a esta ocupación, total o parcialmente, aplicando tal como se publicaron las tablas de correspondencia de la U.S. Bureau of Labor Statistics (ISCO-08 a SOC 2010, SOC 2010 a SOC 2018)
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
Ver 1 tarea oculta19 tareas evaluadas · 18 tareas con β ≥ 0,5 (94.7%)
β = exposición directa (E1) + 0,5 × exposición con herramientas disponibles (E2), según la definición del repositorio de origen.
- Unidad de origen: tareas de O*NET 27.2 → código de ocupación de O*NET 31.0
- 2 tareas puntuadas no figuran en la lista de tareas de O*NET 31.0; sus puntuaciones se conservan tal como se publicaron para O*NET 27.2.
- Fuente
- OpenAI "GPTs are GPTs" exposure rubric
- Versión
- gh-main-0471612
- Licencia
- MIT License, Copyright (c) 2024 OpenAI
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®, Eloundou et al. (2023): see full notices on the Credits page
Todas las tareas de O*NET 31.0 (17)
Todas las tareas de O*NET 31.0 de esta ocupación (en inglés, tal como se publicaron; tipo de tarea principal o complementaria). Los valores de exposición no figuran en esta lista; están en la tabla de arriba, junto a cada tarea con la redacción de la versión de O*NET que usó cada fuente.
| Tarea | Tipo |
|---|---|
| Analyze information to determine, recommend, and plan installation of a new system or modification of an existing system. | Principal |
| Analyze user needs and software requirements to determine feasibility of design within time and cost constraints. | Principal |
| Confer with data processing or project managers to obtain information on limitations or capabilities for data processing projects. | Principal |
| Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces. | Principal |
| Consult with customers or other departments on project status, proposals, or technical issues, such as software system design or maintenance. | Principal |
| Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design. | Principal |
| Determine system performance standards. | Principal |
| Develop or direct software system testing or validation procedures, programming, or documentation. | Principal |
| Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance. | Principal |
| Prepare reports or correspondence concerning project specifications, activities, or status. | Principal |
| Store, retrieve, and manipulate data for analysis of system capabilities and requirements. | Principal |
| Coordinate installation of software system. | Complementaria |
| Monitor functioning of equipment to ensure system operates in conformance with specifications. | Complementaria |
| Obtain and evaluate information on factors such as reporting formats required, costs, or security needs to determine hardware configuration. | Complementaria |
| Supervise and assign work to programmers, designers, technologists, technicians, or other engineering or scientific personnel. | Complementaria |
| Supervise the work of programmers, technologists and technicians and other engineering and scientific personnel. | Complementaria |
| Train users to use new or modified equipment. | Complementaria |
Índice de exposición a robots
Anthropic, «What work can robots do?» (2026)
0,00/ 3
Cada tarea física entre las tareas O*NET 29.3 de esta ocupación se califica de 0 a 3 según el entorno en el que robots ya implantados, vendidos o demostrados podrían realizarla (0 ninguno · 1 entornos diseñados para robots, como una celda de máquinas vallada o una línea de embotellado · 2 instalaciones estructuradas, como una farmacia hospitalaria o un puerto de contenedores · 3 entornos no estructurados, como un hogar particular o una obra de construcción), y las calificaciones se promedian según la proporción del tiempo de trabajo. Anthropic estimó las calificaciones y las proporciones de tiempo con Claude Opus 5 mediante búsqueda web (situación en 2026). El índice no mide la adopción real ni el coste, y el 0 también incluye ocupaciones sin tareas físicas. Es una medida distinta de las cifras de exposición a la IA de este sitio y no puede sumarse a ellas ni compararse con ellas.
Calificación de cada tarea física (enunciados de las tareas tal como figuran en O*NET 29.3)
Ninguna de las tareas O*NET 29.3 de esta ocupación está clasificada como tarea física.
Información ocupacional
Cambios recientes relacionados con esta ocupación
Los resúmenes y las etiquetas de relación son de AI Changing Work; compruebe las cifras en la fuente enlazada.
Relacionado 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.
[Fuente: ADP Research / Stanford Digital Economy Lab, Unbundling Jobs (July 2026)]Relacionado 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.
[Fuente: EIG: AI and Young-Adult Jobs (March 2026)]Relacionado 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.
[Fuente: Brookings Institution]
Mostrar 6 más
Relacionado Acemoglu, Autor and Johnson (Hamilton Project/Brookings, Feb 2026) warn that if AI automates core roles in fields such as coding, customer service, marketing and translation, it will devalue the expertise workers rely on. They call for pro-worker AI that extends expertise instead, and for a tax code that stops taxing hiring labor more heavily than investing in algorithms.
[Fuente: Acemoglu, Autor, Johnson — Building Pro-Worker AI (Hamilton Project/Brookings, Feb 2026)]Relacionado Anthropic India Brief (Feb 2026): 45.2% of Indian Claude.ai tasks map to software-related occupations, the highest share of any country. Indian users finish tasks with AI about 15x faster than the estimated time without it, versus 12x globally.
[Fuente: Anthropic India Brief 2026]Relacionado Dallas Fed: Computer systems design sector wages up 16.7% but employment down 5%, indicating AI augments experienced developers while reducing entry-level demand.
[Fuente: Dallas Fed (Feb 2026)]Relacionado Frank et al. (arXiv, Jan 2026): unemployment risk in AI-exposed occupations started rising in early 2022, months before ChatGPT, in data from unemployment insurance claims and millions of LinkedIn profiles. Computer and math occupations (SOC 15), which include software developers, had the largest rise in unemployment risk in 2022-2024.
[Fuente: Frank et al. (2026) arXiv:2601.02554]Directamente relacionado EIG working paper by Google economists Iscenko and Curto Millet (Jan 2026): the drop in employment for early-career workers in AI-exposed occupations such as software engineering is better explained by the sharpest monetary tightening in four decades than by AI. They add that this does not mean young workers are safe from future disruption.
[Fuente: EIG / Google Economists (Jan 2026)]Relacionado Anthropic internal study: engineers use AI for 59% of work (up from 28%), with 67% more pull requests per day. Feature implementation delegation grew from 14% to 37%.
[Fuente: Anthropic Research (2025)]
Fuentes de datos y licencias de esta página, y avisos de modificación y traducción de AI Changing Work: Créditos y fuentes