AI Job Risk in Technology
Technology is unusual among industries because it is simultaneously the source of the automation wave and one of the fields most affected by it. Code completion, test generation, and log triage are now routine parts of a developer's daily toolkit, and they have measurably sped up the mechanical parts of building software. What has not gotten easier is deciding what to build in the first place, how a system should be structured so it doesn't collapse under its own complexity two years later, and who is responsible when a production system fails at three in the morning with customers watching.
Industry average exposure
86.9
Jobs Analyzed
21
How to read this page in practice
The notes below explain how to read the industry figure, where generative AI reaches the work first, and where human-led work is more likely to remain.
How to Read This Industry
Separate technology work into code production and system responsibility, since AI reshapes them very differently. Writing boilerplate, generating unit tests, completing routine functions, and summarizing logs for debugging are tasks that AI tools now do quickly and reasonably well, often faster than a junior engineer would. Choosing an architecture that will hold up as a product scales, deciding what tradeoffs a system should make under real constraints, and being the person who gets paged when something breaks in production require judgment about consequences that unfold over months or years, and that is where engineering seniority continues to matter most.
What Automation Hits First
AI moves first through code completion, boilerplate generation, unit and integration test writing, log and error triage, and first-draft technical documentation that engineers used to postpone indefinitely. Code review assistants already catch a meaningful share of style, correctness, and security issues before a human reviewer ever looks at a pull request. It stalls on system design decisions, debugging genuinely novel production incidents with no precedent in the runbooks, negotiating technical tradeoffs with product and business stakeholders who don't share an engineering vocabulary, and taking on-call responsibility for a system's uptime.
What Still Depends on People
Durable technology roles are the ones carrying design and operational responsibility: architects who decide how services should be decomposed and where the seams should go, senior engineers who make the call during a live incident about what to roll back and what to leave alone, and staff engineers who negotiate tradeoffs between speed, cost, and reliability across competing teams. Security engineers assessing genuinely new threats and engineering managers navigating team conflict and stakeholder pressure also depend on judgment that doesn't reduce to pattern completion.
How to Use the Gap
Score a technology role by asking whether it is mostly writing code to a known spec or mostly making decisions a team and a business depend on. Roles built around routine implementation, test writing, and boilerplate work show higher exposure because AI assistants already handle much of that volume. Roles centered on system design, incident response, and technical tradeoffs under real operational stakes score lower, since AI assistance speeds up the typing without removing the responsibility for what actually gets shipped.
Jobs Most At Risk from AI
This table is a current snapshot of jobs in this industry that sit on the higher-risk side. Read it together with the fixed commentary above rather than as a permanent list of examples.
| Rank | Job | Generative-AI exposure |
|---|---|---|
| 1 | Web Developer | 97 |
| 2 | Technical Writer | 96 |
| 3 | Programmer | 96 |
| 4 | Data Analyst | 95 |
| 5 | Data Scientist | 95 |
| 6 | Software Tester | 95 |
| 7 | IT Support Specialist | 94 |
| 8 | Data Entry Clerk | 93 |
| 9 | Database Administrator | 92 |
| 10 | Cloud Engineer | 91 |
| 11 | System Administrator | 88 |
| 12 | Cybersecurity Analyst | 87 |
| 13 | Network Engineer | 86 |
| 14 | AI Engineer | 85 |
| 15 | DevOps Engineer | 85 |
| 16 | Game Developer | 85 |
| 17 | Software Engineer | 85 |
| 18 | Product Manager | 82 |
| 19 | Machine Learning Engineer | 80 |
| 20 | Robotics Engineer | 68 |
Exposure runs from 0 to 100 and compares occupations with each other, using public datasets (see sources). It is not a probability that the job disappears. Methodology
Sources
Exposure index calculated by ai-job-risk.net from the datasets below (data as of October 15, 2026). US labour figures are from the BLS, unchanged.
- Eloundou, Manning, Mishkin & Rock, "GPTs are GPTs: Labor market impact potential of LLMs", Science (2024); data MIT licence
- Gmyrek, Berg, Kamiński, Konopczyński, Ładna, Nafradi, Rosłaniec, Troszyński. Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO Working Paper 140. Geneva: ILO, 2025. © ILO, CC BY 4.0
- Tomlinson, Jaffe, Wang, Counts & Suri, "Working with AI: Measuring the Occupational Implications of Generative AI", Microsoft Research; CC BY 4.0
- Anthropic Economic Index (job_exposure); CC BY
- U.S. Bureau of Labor Statistics, Employment Projections 2025–35 and AI exposure categories
The publishers did not prepare, review or endorse this site. Full citations, licences and method
Frequently asked questions
Q.Which jobs in Technology are most exposed to AI?
Most exposed: Web Developer, Technical Writer, Programmer. AI moves first through code completion, boilerplate generation, unit and integration test writing, log and error triage, and first-draft technical documentation that engineers used to postpone indefinitely. Code review assistants already catch a meaningful share of style, correctness, and security issues before a human reviewer ever looks at a pull request.
Q.Which Technology jobs are safest from AI?
Durable technology roles are the ones carrying design and operational responsibility: architects who decide how services should be decomposed and where the seams should go, senior engineers who make the call during a live incident about what to roll back and what to leave alone, and staff engineers who negotiate tradeoffs between speed, cost, and reliability across competing teams. Security engineers assessing genuinely new threats and engineering managers navigating team conflict and stakeholder pressure also depend on judgment that doesn't reduce to pattern completion.
Q.Is Technology safe from AI?
Score a technology role by asking whether it is mostly writing code to a known spec or mostly making decisions a team and a business depend on. Roles built around routine implementation, test writing, and boilerplate work show higher exposure because AI assistants already handle much of that volume. Roles centered on system design, incident response, and technical tradeoffs under real operational stakes score lower, since AI assistance speeds up the typing without removing the responsibility for what actually gets shipped.
Q.How is the Technology figure calculated?
It is the average exposure index of the Technology occupations we cover, weighted by US employment where BLS data exists. See the methodology page for the sources and how to read them.