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Will AI replace Engineers, All Other?

All engineers not listed separately. The percentage shown is a task-exposure model, not a prediction of certain job loss.

O*NET 30.3 · 17-2199.00 · modeled exposure, not guaranteed job loss
LOW TASK EXPOSURE
28%
Estimated share of representative tasks exposed to current AI capabilities.
Task automation shareⓘ
28%Automatable
Automatable28%
Augmentable40%
Hard to automate32%
Based on analysis of representative responsibilities and current AI capability maturity.
Category#
Tech
Engineering & Product
Modeled pay midpoint$
$65,300/yr
Directional estimate — verify locally
Time until impact⌁
6–10+ years
Earliest 2032 · widespread 2036+
Task-level replaceability ⓘ
Representative tasks ranked from highest to lowest AI exposure.
◇Organize role-specific information and routine work38%
✦System accountability and architecture judgment24%
⚙Prepare standard outputs and records23%
✦Handling ambiguous requirements and trade-offs20%
Impact timeline ⓘ
How AI assistance may move from early support to wider adoption.
2032Next2036+
Light supportAI provides limited help with organize role-specific information and routine work.
AugmentationTools improve productivity without owning outcomes.
Human-ledCore work stays anchored in system accountability and architecture judgment.
6–10+ yearsdirectional confidence window
HOW AI CHANGES THIS JOBtyping the logic in plain English
01>Start with the real work done by Engineers, All Other.
02>AI can help with: Organize role-specific information and routine work.
03>If a task is repetitive and easy to check, AI pressure goes up.
04>But a person is still needed for: System accountability and architecture judgment.
05>Likely result: AI mostly acts as a helper while the core job stays human-led.
06>Important: 28% is estimated task exposure—not a 28% chance of losing the job.
What AI can do
Organize role-specific information and routine workLOW MATCH
Prepare standard outputs and recordsLOW MATCH
How estimates work →
Your human moat
◆

System accountability and architecture judgment

HIGH MOAT

Combined with handling ambiguous requirements and trade-offs, this keeps a human in the loop.

Strength8.8
Similar-role comparison
RoleRisk
Engineers, All Other28%
Cloud Architect28%
Civil Engineers29%
Electrical Engineers27%
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Why this score?

✓The model evaluates 2 representative core tasks.
✓The core work depends on presence, dexterity, or accountability.
✓Occupation and task language comes from O*NET 30.3.
✓The strongest human advantage is system accountability and architecture judgment.
Research basis

O*NET 30.3 occupation tasks plus the ILO 2025 exposure framework. Scores are directional estimates.

Read ILO research →
Data: O*NET 30.3 · Model framework: ILO 2025About O*NETShare report
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Cloud Architect · 28% exposureCivil Engineers · 29% exposureElectrical Engineers · 27% exposure