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.
- Fonte dos dados: BLSPublicado: 2026-08
Muito alto· relativa
BaixoQuatro faixas relativasMuito altoValor por grupo ocupacional
Escala, base e fonte
Quatro faixas relativas (Baixo / Moderado / Alto / Muito alto)
831 ocupações detalhadas da tabela de projeções de emprego do BLS. O valor é atribuído por código da National Employment Matrix (NEM), pelo que as ocupações que partilham um código NEM recebem a mesma banda
- Fonte dos dados: AnthropicPublicado: 2026-03
0.288
0.000Intervalo dos valores aqui apresentados0.745Escala, base e fonte
Índice de exposição observada, 0–1 tal como publicado
Mapeado sobre tarefas O*NET
Publicado por ocupação SOC 2018; toda ocupação O*NET com o mesmo código SOC 2018 recebe este valor
- Fonte dos dados: ILOPublicado: 2025
0.53
0.09Intervalo dos valores aqui apresentados0.70Valor por grupo ocupacional
Escala, base e fonte
Índice de exposição à IA generativa, 0–1 tal como publicado
Publicado por grupo de base ISCO-08. Ligado a esta ocupação, total ou parcialmente, aplicando tal como publicadas as tabelas de correspondência do U.S. Bureau of Labor Statistics (ISCO-08 para SOC 2010, SOC 2010 para SOC 2018)
Que tipo de valor esta fonte publica
A categoria de BLS é uma posição relativa, não um nível absoluto, e também não é uma medição de primeira mão: agrupa em quatro faixas as posições percentis da ocupação em vários estudos publicados. Não é uma previsão de emprego ou de salários, não é uma probabilidade de adoção e não distingue automação de aumento.
Task-level exposure
Ver 9 tarefas ocultas19 tarefas avaliadas · 18 tarefas com β ≥ 0,5 (94.7%)
β = exposição direta (E1) + 0,5 × exposição com ferramentas disponíveis (E2), segundo a definição do repositório de origem.
- Unidade de origem: tarefas do O*NET 27.2 → código de ocupação do O*NET 31.0
- 2 tarefas pontuadas não constam da lista de tarefas do O*NET 31.0; as suas pontuações são mantidas como publicadas para o O*NET 27.2.
- Fonte
- OpenAI "GPTs are GPTs" exposure rubric
- Versão
- gh-main-0471612
- Licença
- MIT License, Copyright (c) 2024 OpenAI
| Task (O*NET 30.2) | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.15-1252.00 | 0.200058.8 | 0.340073.9 |
Prepare reports or correspondence concerning project specifications, activities, or status.15-1252.00 | 0.050014.7 | 0.02004.3 |
Consult with customers or other departments on project status, proposals, or technical issues, such as software system design or maintenance.15-1252.00 | 0.040011.8 | 0.02004.3 |
Analyze information to determine, recommend, and plan installation of a new system or modification of an existing system.15-1252.00 | 0.03008.8 | 0.03006.5 |
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.15-1252.00 | 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.15-1252.00 | 0.01002.9 | 0.00000.0 |
Store, retrieve, and manipulate data for analysis of system capabilities and requirements. | —0 | 0.02004.3 |
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 (O*NET 27.2) | 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
Todas as tarefas do O*NET 31.0 (17)
Todas as tarefas do O*NET 31.0 desta ocupação (em inglês, como publicadas; tipo de tarefa principal ou complementar). Os valores de exposição não constam desta lista; estão na tabela acima, ao lado de cada tarefa na redação da versão do O*NET usada por cada fonte.
| Tarefa (O*NET 31.0) | 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. | Complementar |
| Monitor functioning of equipment to ensure system operates in conformance with specifications. | Complementar |
| Obtain and evaluate information on factors such as reporting formats required, costs, or security needs to determine hardware configuration. | Complementar |
| Supervise and assign work to programmers, designers, technologists, technicians, or other engineering or scientific personnel. | Complementar |
| Supervise the work of programmers, technologists and technicians and other engineering and scientific personnel. | Complementar |
| Train users to use new or modified equipment. | Complementar |
0,00/ 3· Índice de exposição a robôs
Escala, base e fonte
Cada tarefa física recebe uma nota de 0 a 3 conforme o ambiente em que robôs já implantados, vendidos ou demonstrados poderiam realizá-la (0 nenhum · 1 ambientes feitos para robôs, como uma célula de máquinas cercada ou uma linha de engarrafamento · 2 instalações estruturadas, como uma farmácia hospitalar ou um porto de contêineres · 3 ambientes não estruturados, como uma residência particular ou um canteiro de obras), e as notas são promediadas pela parcela do tempo de trabalho
Tarefas físicas entre as tarefas O*NET 29.3 desta ocupação
A Anthropic estimou as notas e as parcelas de tempo com o Claude Opus 5 usando pesquisa na web (situação em 2026).
O índice de exposição a robôs é uma medida diferente dos números de exposição à IA deste site e não pode ser somado a eles nem comparado com eles. Não mede a adoção real nem o custo, e 0 também abrange ocupações sem tarefas físicas.
Nota de cada tarefa física
Nenhuma das tarefas O*NET 29.3 desta ocupação é classificada como tarefa física.
Informação ocupacional
Alterações recentes relacionadas com esta ocupação
Os resumos e as indicações de relação são da AI Changing Work; confira os números na fonte indicada.
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.
[Fonte: 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.
[Fonte: 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.
[Fonte: Brookings Institution]
Mostrar mais 6
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.
[Fonte: 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.
[Fonte: 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.
[Fonte: 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.
[Fonte: Frank et al. (2026) arXiv:2601.02554]Diretamente 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.
[Fonte: 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%.
[Fonte: Anthropic Research (2025)]
Fontes de dados e licenças desta página, e avisos de modificação e tradução da AI Changing Work: Créditos e fontes