Computer Programmers

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

    0.745

    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

    0.57

    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, 7% 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

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
Correct errors by making appropriate changes and rechecking the program to ensure that the desired results are produced.

O*NET Task ID 1267

1.0
Conduct trial runs of programs and software applications to be sure they will produce the desired information and that the instructions are correct.

O*NET Task ID 1268

1.0
Compile and write documentation of program development and subsequent revisions, inserting comments in the coded instructions so others can understand the program.

O*NET Task ID 1269

1.0
Write, update, and maintain computer programs or software packages to handle specific jobs such as tracking inventory, storing or retrieving data, or controlling other equipment.

O*NET Task ID 1270

1.0
Consult with managerial, engineering, and technical personnel to clarify program intent, identify problems, and suggest changes.

O*NET Task ID 1271

1.0
Perform or direct revision, repair, or expansion of existing programs to increase operating efficiency or adapt to new requirements.

O*NET Task ID 1272

1.0
Write, analyze, review, and rewrite programs, using workflow chart and diagram, and applying knowledge of computer capabilities, subject matter, and symbolic logic.

O*NET Task ID 1273

1.0
Write or contribute to instructions or manuals to guide end users.

O*NET Task ID 1274

1.0
Investigate whether networks, workstations, the central processing unit of the system, or peripheral equipment are responding to a program's instructions.

O*NET Task ID 1275

1.0
Prepare detailed workflow charts and diagrams that describe input, output, and logical operation, and convert them into a series of instructions coded in a computer language.

O*NET Task ID 1276

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

Occupation information

Recent Changes Affecting This Occupation

May 2026: Anthropic observed-exposure score: 75% (highest in dataset). Highest in dataset (75%). Computer & Math category overall sits at 33% observed vs 90% theoretical.

[Source: Anthropic Economic Research (Massenkoff & McCrory, 2026)]

Apr 2026: BOK research identifies codified, textbook-style knowledge work as most AI-vulnerable. Korean youth employment in AI-exposed coding/programming roles shows paradoxical growth (+1.2pp) but collapsing new-hire rates.

[Source: Bank of Korea Employment Research (2025)]

Mar 2026: Dallas Fed: Computer systems design sector shows employment decline of 5% alongside 16.7% wage growth, suggesting AI automates routine coding while rewarding complex problem-solving.

[Source: Dallas Fed (Feb 2026)]