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Digital Technology

National Career Cluster · the map of work

Digital Technology is one of the 14 national career clusters — the U.S. Department of Education's map that divides the whole world of work into broad families, each crosswalked to occupations, education programs, and industries. This cluster spans 20 occupations across 5 sub-clusters, employing about 5,773,790 workers, with a median wage of $106,795.

What's in this cluster

A career cluster is a navigational grouping, not a measured score. The counts below come from the framework's official crosswalk; employment and pay are aggregated from BLS OEWS (national, cross-industry, May 2024) across the occupations in the cluster.

20 occupations
5 sub-clusters
5,773,790 workers (BLS)
$106,795 median pay
89 education programs

Across occupations with wage data, the middle range (25th–75th percentile of occupation medians) runs $98,525 – $131,063. This describes the cluster, not any one job or person.

Sub-clusters

The framework splits each cluster into sub-clusters — tighter families of related work. The count is the number of occupations the crosswalk places in each.

Sub-cluster Occupations
Web & Cloud 9
Network Systems & Cybersecurity 8
Data Science & AI 6
Software Solutions 6
IT Support & Services 5

Largest occupations in this cluster

Exposure quadrant: AI task-overlap percentile vs Median pay AI task-overlap (horizontal) versus median pay (vertical), each as a percentile across all scored occupations, for 20 occupations in Digital Technology. Overlap measures shared tasks with AI, not automation. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Computer and Information Research Scientists Computer Network Support Specialists Computer User Support Specialists Information Security Analysts Network and Computer Systems Administrators Computer Systems Analysts Mathematicians AI task-overlap percentile → ↑ Median pay
The largest occupations in this cluster with both an AI task-overlap score and a wage, plotted by task-overlap percentile (horizontal) and median-pay percentile (vertical). Overlap measures shared tasks with AI, not automation.

Occupations in this cluster with the most workers nationally (BLS OEWS, May 2024), each linked to its full profile. Employment and pay describe the occupation, not an individual.

Occupation Workers Median pay
Software Developers 1,654,440 $133,080
Computer User Support Specialists 697,210 $60,340
Computer and Information Systems Managers 645,970 $171,200
Computer Systems Analysts 497,800 $103,790
Computer Occupations, All Other 439,380 $108,970
Network and Computer Systems Administrators 318,570 $96,800
Data Scientists 233,440 $112,590
Software Quality Assurance Analysts and Testers 199,800 $102,610
Information Security Analysts 179,430 $124,910
Computer Network Architects 177,010 $130,390
Computer Network Support Specialists 146,450 $73,340
Web and Digital Interface Designers 111,400 $98,090
Computer Programmers 109,870 $98,670
Web Developers 78,860 $90,930
Computer Hardware Engineers 75,710 $155,020
Database Administrators 73,180 $104,620
Database Architects 64,770 $135,980
Computer and Information Research Scientists 38,480 $140,910
Statisticians 29,800 $103,300
Mathematicians 2,220 $121,680

AI exposure across this cluster

Two published studies estimate how exposed each occupation is to today's AI. The OpenAI "GPTs are GPTs" study rates the share of an occupation's tasks a large language model (with tools) could speed up by half or more; averaged across this cluster it is 58% — 96th percentile of the 14 clusters. The independent Felten/Raj/Seamans AI Occupational Exposure index averages 1.26 here.

Computed across the 20 of 20 occupations in this cluster that carry a published exposure score.

Exposure measures where AI could assist tasks — it is not a prediction that these jobs will be automated. High exposure most often means augmentation (faster work), and many high-exposure occupations are also projected to grow.

Where the work sits

The framework anchors each cluster to one or more NAICS industry sectors — the parts of the economy where this work concentrates.

Education programs that lead here

A sample of the 89 CIP 2020 instructional programs the framework crosswalks to this cluster — the fields of study that prepare people for this work.

  • Accounting and Computer Science
  • Agricultural Business Technology/Technician
  • Algebra and Number Theory
  • Analysis and Functional Analysis
  • Applied Mathematics, General
  • Applied Mathematics, Other
  • Applied Statistics, General
  • Artificial Intelligence
  • Bioinformatics
  • Biomathematics, Bioinformatics, and Computational Biology, Other
  • Business Analytics
  • Business Statistics
  • Cheminformatics/Chemistry Informatics
  • Cloud Computing
  • Computational Biology
  • Computational Mathematics
  • Computational Science
  • Computational and Applied Mathematics
  • Computer Engineering Technology/Technician
  • Computer Engineering, General
  • Computer Engineering, Other
  • Computer Game Programming
  • Computer Graphics
  • Computer Hardware Engineering

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 3, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Digital Technology." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; CIP-2020 2020; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026. https://singulariki.com/clusters/digital-technology

APA

Singulariki. (2026). Digital Technology. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/clusters/digital-technology

BibTeX
@misc{singulariki-digital-technology,
  title  = {Digital Technology},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; Census NAICS 2022; CIP-2020 2020; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans. Accessed June 7, 2026},
  url    = {https://singulariki.com/clusters/digital-technology}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.