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 Risk Score
54.78
Jobs Analyzed
23
How to read this page in practice
The notes below explain how to interpret the score, where automation pressure tends to show up first, and where human-led value is more likely to remain inside this industry.
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 | Risk Score |
|---|---|---|
| 1 | Software Tester | 85 |
| 2 | Data Entry Clerk | 82 |
| 3 | Data Analyst | 79 |
| 4 | QA Engineer | 77 |
| 5 | Software Engineer | 73 |
| 6 | Mobile App Developer | 73 |
| 7 | System Administrator | 71 |
| 8 | Programmer | 69 |
| 9 | IT Support Specialist | 67 |
| 10 | Technical Writer | 65 |
| 11 | Database Administrator | 64 |
| 12 | Web Developer | 63 |
| 13 | Game Developer | 59 |
| 14 | Network Engineer | 52 |
| 15 | DevOps Engineer | 40 |
| 16 | Data Scientist | 37 |
| 17 | Cloud Engineer | 37 |
| 18 | Electrical Engineer | 34 |
| 19 | Product Manager | 33 |
| 20 | AI Engineer | 32 |
Jobs Safest from AI
This table shows the jobs in this industry that currently sit on the lower-risk side. Use it as a comparison of task structure, not as a promise that these roles will never change.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Machine Learning Engineer | 17 |
| 2 | Cybersecurity Analyst | 25 |
| 3 | Robotics Engineer | 26 |
| 4 | AI Engineer | 32 |
| 5 | Product Manager | 33 |
| 6 | Electrical Engineer | 34 |
| 7 | Data Scientist | 37 |
| 8 | Cloud Engineer | 37 |
| 9 | DevOps Engineer | 40 |
| 10 | Network Engineer | 52 |
| 11 | Game Developer | 59 |
| 12 | Web Developer | 63 |
| 13 | Database Administrator | 64 |
| 14 | Technical Writer | 65 |
| 15 | IT Support Specialist | 67 |
| 16 | Programmer | 69 |
| 17 | System Administrator | 71 |
| 18 | Software Engineer | 73 |
| 19 | Mobile App Developer | 73 |
| 20 | QA Engineer | 77 |
Frequently asked questions
Q.Which jobs in Technology are most exposed to AI?
In Technology, the jobs with the highest AI risk scores include Software Tester. The full ranking of the most and least exposed Technology jobs is shown above.
Q.Which Technology jobs are safest from AI?
The Technology roles least exposed to AI automation include Machine Learning Engineer. These tend to depend on judgment, physical presence, or accountability that current AI cannot take on.
Q.Is Technology safe from AI?
No industry is uniformly safe or at risk. Within Technology, routine information-handling roles are far more exposed than roles built on judgment and responsibility, so the score is best read as a task-exposure signal rather than a prediction of job loss.
Q.How is the Technology AI risk score calculated?
It is the average AI risk across the Technology jobs we track, refreshed weekly. See the methodology page for how the underlying scores are produced and how to interpret them.