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AI Automation Index
Will AI Replace Software Developers?
Task-level automation exposure for Software Developers: 17 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
64%
weighted task index
17 of 17 tasks scored
Median Salary
$135,980 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
1,687,890 people hold this job — size, not growth
Projected 2025–35
+10.2%
employment change
95,300 openings a year · BLS line item
Pay by state
BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$79,380 – $174,410
Highest medians
California $174,410
Washington $166,540
New York $166,180
Massachusetts $165,210
Oregon $142,720
Lowest medians
Puerto Rico $79,380
Mississippi $95,330
South Dakota $96,310
Louisiana $102,800
Arkansas $104,490
State figures come from the 15-1252 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 64%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 69%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 4/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
17 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 69%
11 tasks
Medium exposure (≥ 35%) 22%
4 tasks
Low exposure (below 35%) 9%
2 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#21662 · Core · 72% automatable
Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.
data analysis — Derives specs from project data; human validates requirements
Analyze project data to determine specifications or requirements.
#21669 · Core · 52% automatable
Develop or direct software system testing or validation procedures, programming, or documentation.
text generation — AI drafts testing routines; engineer verifies coverage
Develop testing routines or procedures. Manage information technology projects or system activities.
#21664 · Core · 55% automatable
Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces.
interpersonal — Collaborative spec-setting requires human negotiation
Collaborate with others to determine design specifications or details.
#21670 · Core · 70% automatable
Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.
text generation — Performance refactoring largely AI; human tests and approves
Modify software programs to improve performance.
#21673 · Core · 68% automatable
Prepare reports or correspondence concerning project specifications, activities, or status.
text generation — Drafting project updates is AI-assisted; delivery is human
Communicate project information to others.
#21661 · Core · 72% automatable
Analyze information to determine, recommend, and plan installation of a new system or modification of an existing system.
data analysis — Derives specs from project data; human validates requirements
Analyze project data to determine specifications or requirements.
#21676 · Core · 83% automatable
Store, retrieve, and manipulate data for analysis of system capabilities and requirements.
data analysis — Data preparation for analysis automatable
Assess database performance. Prepare data for analysis.
#21667 · Core · 71% automatable
Design, develop and modify software systems, using scientific analysis and mathematical models to predict and measure outcomes and consequences of design.
data analysis — Applies math/statistics to problems; human validates
Apply mathematical principles or statistical approaches to solve problems in scientific or applied fields. Modify software programs to improve performance. Design software applications.
#21668 · Core · 70% automatable
Determine system performance standards.
data analysis — IT performance metrics well-suited to AI
Develop performance metrics or standards related to information technology.
#21665 · Core · 70% automatable
Consult with customers or other departments on project status, proposals, or technical issues, such as software system design or maintenance.
decision support — Software support is largely diagnosable with human review
Provide technical support for software maintenance or use.
#21663 · Core · 62% automatable
Confer with data processing or project managers to obtain information on limitations or capabilities for data processing projects.
decision support — IT troubleshooting collaboration well-supported by AI
Collaborate with others to resolve information technology issues.
#21671 · Supplemental · 85% automatable
Monitor functioning of equipment to ensure system operates in conformance with specifications.
data analysis — AI monitors system metrics and detects anomalies
Monitor computer system performance to ensure proper operation.
#21666 · Supplemental · 33% automatable
Coordinate installation of software system.
interpersonal — Coordinating installation involves physical and human oversight
Coordinate software or hardware installation.
#21678 · Supplemental · 32% automatable
Supervise the work of programmers, technologists and technicians and other engineering and scientific personnel.
interpersonal — IT staff supervision needs human judgment and accountability
Supervise information technology personnel.
#21677 · Supplemental · 50% automatable
Supervise and assign work to programmers, designers, technologists, technicians, or other engineering or scientific personnel.
decision support — Assignments drafted; accountability and negotiation human
Assign duties or work schedules to employees. Supervise information technology personnel.
#21672 · Supplemental · 72% automatable
Obtain and evaluate information on factors such as reporting formats required, costs, or security needs to determine hardware configuration.
data analysis — Derives specs from project data; human validates requirements
Analyze project data to determine specifications or requirements.
#21679 · Supplemental · 50% automatable
Train users to use new or modified equipment.
interpersonal — Hardware teaching aided by AI guides, human delivers
Teach others to use computer equipment or hardware.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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