Quick answer
Do not use a made-up “AI resistance score” as a career guarantee.
BLS categorizes occupations into low, moderate, high and very high relative AI exposure using five external data sources. BLS explicitly says the categories are not forecasts of employment growth, automation probability, worker replacement, wages or productivity.
What BLS actually measures
The BLS Employment Projections program combines theoretical measures of whether AI could assist occupational tasks with observed evidence from real-world AI usage. It then groups detailed occupations into four relative exposure categories.
Low
Current AI capabilities match relatively few of the occupation's tasks.
Moderate
Some task overlap exists, but exposure is not among the highest occupations.
High
A larger share of tasks may be assistable by AI relative to other occupations.
Very high
AI capabilities and observed usage overlap with a comparatively larger fraction of tasks.
Why skilled trades often look different from desk-based work
Many trade occupations contain a large amount of physical, site-specific work: installing equipment, climbing, troubleshooting real systems, handling tools, working around hazards and responding to conditions that vary from building to building. BLS's 2025 Occupational Requirements Survey, for example, reports electricians spend the large majority of their workday standing and that climbing and outdoor exposure are common requirements.
That does not make the work immune to technology. AI can still affect estimating, scheduling, documentation, diagnostics, training, code lookup and customer communication. The likely question for many trades is which tasks change, not whether every worker disappears.
Use AI exposure together with the employment outlook
Career planning is stronger when you combine technology exposure with current wages, projected growth, annual openings, licensing rules and your local market. For example, BLS currently projects 2025–35 growth of 9% for electricians, 11% for HVAC technicians and 7% for plumbers/pipefitters/steamfitters. Those projections already incorporate BLS's broader assessment of structural change.
Five rules for reading an “AI-proof jobs” list
- 1Ask who created the score and whether the methodology is public.
- 2Do not treat AI exposure as a probability of job loss unless the source explicitly measures that outcome.
- 3Separate task automation from occupation elimination. A job can change substantially without disappearing.
- 4Check current BLS growth and annual openings instead of relying on one technology headline.
- 5Consider physical demands, licensing, local demand and your own fit—not only AI exposure.
Choose a career using more than one risk score.
TrainingForFuture compares career fit, official pay/outlook benchmarks, state resources and training paths so you can make a broader decision.
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