Software Developers

Computer & Mathematical

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.288

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.53

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 14% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

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.

TaskβE1 + 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
Train users to use new or modified equipment.

O*NET Task ID 21679

0.0

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

Occupation information

Recent Changes Affecting This Occupation

Jul 2026: 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.

[Source: ADP Research / Stanford Digital Economy Lab, Unbundling Jobs (July 2026)]

Jun 2026: Cited by ADP Research as an example of a high-AI-exposure occupation. Group-level payroll data shows employment in high-exposure occupations down 0.2% year over year overall and down 4.3% for workers aged 22-25 (33rd consecutive monthly decline). The percentages are for the high-exposure group, not for this occupation alone.

[Source: ADP Research, Canaries Dashboard (June 2026)]

Apr 2026: Korean youth employment data shows codified knowledge work most vulnerable to AI displacement. Professional/technical services lost 98,000 workers in Jan 2026 (worst since 2013). Entry-level coding tasks at highest risk.

[Source: Econmingle / National Assembly Budget Office (2026)]