Task-level reachability by current AI systems across the published technology sample.
AI and Technology jobs
Technology work is simultaneously building the disruption and absorbing it. Code generation compresses entry-level tasks across development, data, and IT roles, yet the industry keeps hiring — because the scarce work is architecture, judgment, security accountability, and shipping systems that own outcomes.
Estimated potential for task transfer to software.
Estimated potential for AI to expand worker output while keeping human accountability.
Displacement pressure 63 — the most exposed published role in this industry.
Displacement pressure 28 — the strongest anchor role in this industry.
Distribution
How technology roles spread across the pressure scale
Each bar counts published technology roles in a 5-point displacement-pressure band. Red bars mark scores of 70 or higher. The dashed line marks the industry median of 46.
The junior task bundle is compressing
Boilerplate code, first-draft tests, routine tickets, standard reports, and basic dashboards — the traditional entry-level workload — are exactly what AI coding assistants and copilots now generate. Programmers, QA analysts, support specialists, and report-focused data analysts feel this most directly. The career implication is not that tech stops hiring; it is that the rungs of the ladder are being redefined upward.
Augmentation dominates at the senior layer
Architecture, debugging production systems, security judgment, experiment design, and stakeholder translation all gain leverage from AI rather than losing value to it. Software developers, data scientists, ML engineers, and security analysts consistently score as augmentation-led: the tools raise individual output, which raises expectations, which keeps demand for people who can own systems and outcomes.
Infrastructure work is being re-platformed, not deleted
Database and network administrators illustrate tech's quieter pattern: managed cloud platforms and AI operations tooling absorb routine tuning, patching, and monitoring, but organizations still need humans accountable for data integrity, security posture, and failure response. The role migrates toward platform engineering, reliability, and automation — same employer, different toolchain.
Building AI is the most insulated position
Machine learning engineers and cybersecurity specialists occupy the two most structurally protected niches: the people creating the systems and the people defending them. Both face constant task churn — evaluation harnesses and alert triage automate like everything else — but demand for production ML ownership and human security accountability keeps growing precisely because AI adoption is spreading.
Occupation pages
Compare AI risk across technology roles
Each page below includes task-level exposure, automation and augmentation scores, wage context, transition pathways, upskilling priorities, and a 90-day planning outline.
SOC 15-1252
LowSoftware Developers
Code generation changes the junior task bundle, but architecture, debugging, security, product judgment, and system ownership keep the role augmentation-heavy.
- Exposure
- 63
- Automation
- 29%
- Augment
- 74%
SOC 15-1251
HighComputer Programmers
Writing code to someone else's specification is exactly what AI coding tools now do well, and BLS projected this occupation to decline even before modern code generation. The defensible move is up the stack: owning design, integration, review, and outcomes rather than implementation alone.
- Exposure
- 72
- Automation
- 49%
- Augment
- 61%
SOC 15-1254
ModerateWeb Developers
Template sites, simple storefronts, and routine page builds are increasingly produced by AI site builders, squeezing the low end of the market. Value consolidates in product engineering, performance, accessibility, integrations, and owning outcomes for businesses rather than pages.
- Exposure
- 68
- Automation
- 42%
- Augment
- 66%
SOC 15-1253
ModerateSoftware Quality Assurance Analysts and Testers
Manual regression passes and routine test-case writing are heavily exposed as AI generates tests and exercises applications directly. Quality strategy, risk-based judgment about what to test, and owning release confidence are the durable layer, pushing QA toward engineering and away from execution.
- Exposure
- 69
- Automation
- 45%
- Augment
- 64%
SOC 15-1232
ModerateComputer User Support Specialists
Password resets, how-to questions, and known-issue triage are moving to AI assistants that resolve tickets before they reach a human. Hands-on hardware, escalations, endpoint security, and judgment calls in messy environments keep the human tier, which shifts the job toward harder tickets and systems work.
- Exposure
- 66
- Automation
- 44%
- Augment
- 55%
SOC 15-2051
ModerateData Analysts
Routine reporting, data cleaning, and dashboard refreshes are highly exposed to automation. Analysts who frame business questions, validate model output, and translate findings into decisions remain strongly augmentable.
- Exposure
- 62
- Automation
- 38%
- Augment
- 66%
SOC 15-2051
ModerateData Scientists
AI coding assistants accelerate model prototyping, feature engineering, and analysis code. Problem formulation, experiment design, model validation, and communicating uncertainty to decision-makers keep the role firmly augmentation-led.
- Exposure
- 60
- Automation
- 34%
- Augment
- 76%
SOC 15-2051
ModerateMachine Learning Engineers
The engineers building AI systems benefit most from them: scaffolding, training code, and evaluation harnesses are increasingly AI-written. System design, production reliability, evaluation judgment, and data pipeline ownership keep demand strong.
- Exposure
- 62
- Automation
- 34%
- Augment
- 78%
SOC 15-1212
ModerateCybersecurity Specialists
AI accelerates threat monitoring, risk assessment documentation, and policy drafting, raising each specialist's leverage. Adversarial reasoning, incident containment, access governance, and security culture building remain human-accountable work.
- Exposure
- 52
- Automation
- 26%
- Augment
- 72%
SOC 15-1212
LowInformation Security Analysts
Alert triage, report drafting, detection tuning, and policy review can be accelerated by AI. Accountability, incident command, adversarial reasoning, and environment-specific context keep the role resilient.
- Exposure
- 48
- Automation
- 24%
- Augment
- 72%
SOC 15-1242
ModerateDatabase Administrators
Routine tuning, backup scheduling, patching, and access provisioning are increasingly automated by cloud database platforms and AI operations tools. Data modeling, security architecture, migration judgment, and outage accountability keep experienced administrators valuable.
- Exposure
- 60
- Automation
- 40%
- Augment
- 58%
SOC 15-1244
ModerateNetwork and Computer Systems Administrators
Monitoring, routine maintenance, backups, and configuration tasks are being absorbed by cloud services and AI-driven operations tooling. Troubleshooting complex failures, security implementation, and physical infrastructure judgment remain human-led.
- Exposure
- 52
- Automation
- 30%
- Augment
- 56%
Questions
AI and technology jobs: common questions
Will AI replace software developers?
Code generation changes the junior task bundle significantly, but the occupation scores as augmentation-heavy overall. Architecture, debugging, security reasoning, product judgment, and system ownership keep experienced developers in demand. The honest risk is concentrated in boilerplate-heavy and entry-level work, so the premium is on engineers who can review, direct, and ship AI-generated code responsibly.
Which tech jobs are most exposed to AI?
Roles whose output is closest to what models generate directly: computer programmers doing routine coding, QA analysts writing standard tests, support specialists answering scripted tickets, and analysts producing routine reports. Web developers face generative UI pressure too. Across all of these, the exposed layer is task production, while system ownership, diagnosis, and judgment remain defensible.
Are data science and machine learning careers safe?
They are among the most augmentation-led roles tracked. AI accelerates modeling code, data cleaning, and analysis drafts, but problem formulation, experiment design, model validation, and production ownership stay scarce. Machine learning engineers are additionally insulated by building the systems themselves — the churn is real, but demand for people who ship and operate AI keeps outrunning supply.
What should entry-level tech workers do differently now?
Skip the strategy of competing on volume output that AI produces free, and build depth AI cannot fake: debugging real systems, security fundamentals, data modeling, and ownership of production incidents. Pair every learning project with AI tooling fluency, since employers now expect it. The junior premium has shifted from writing code fast to reviewing, integrating, and being accountable for what ships.