Computer Programmers
Computer & MathematicalAI exposure
Show 7 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Develop Web sites.15-1251 | 0.520032.1 | —0 |
Correct errors by making appropriate changes and rechecking the program to ensure that the desired results are produced.15-1251 | 0.330020.4 | 0.430032.3 |
Write, analyze, review, and rewrite programs, using workflow chart and diagram, and applying knowledge of computer capabilities, subject matter, and symbolic logic.15-1251 | 0.280017.3 | 0.380028.6 |
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.15-1251 | 0.280017.3 | 0.210015.8 |
Perform or direct revision, repair, or expansion of existing programs to increase operating efficiency or adapt to new requirements.15-1251 | 0.15009.3 | 0.210015.8 |
Compile and write documentation of program development and subsequent revisions, inserting comments in the coded instructions so others can understand the program.15-1251 | 0.03001.9 | 0.03002.3 |
Conduct trial runs of programs and software applications to be sure they will produce the desired information and that the instructions are correct.15-1251 | 0.01000.6 | 0.05003.8 |
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.15-1251 | 0.01000.6 | 0.01000.8 |
Write or contribute to instructions or manuals to guide end users.15-1251 | 0.01000.6 | 0.00000.0 |
Investigate whether networks, workstations, the central processing unit of the system, or peripheral equipment are responding to a program's instructions.15-1251 | 0.00000.0 | 0.01000.8 |
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 |
|---|---|
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Exposure figures by source
This site carries AI exposure figures from 4 datasets. For this occupation, 4 of them publish a figure of the kind shown below; the 3 published most recently are displayed. The full list is on the Credits & Sources page.
Data source: BLS
Very high
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
Data source: Anthropic
0.745
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Data source: ILO
0.57
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Each figure is published on its own scale and measures something different, so they cannot be added, averaged, or ranked against one another.
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.
BLS draws on research that is also shown here — Anthropic — so a resemblance between them is not independent confirmation but the same input read twice.
AI exposure (ILO)
0.57 / 1
top 7% of all occupations
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)]