Business and Financial Operations · United States (national)
Farm Labor Contractors
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is less exposed to automation, and a near-term shift looks unlikely.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 12.0
Physical / roboticsrange 15.9 to 39.9
When the change is likely
Largely unaffectedlocal demand growing
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Stay sharp; urgency is low
This role looks durable. Keep your skills current and watch for shifts in your field.
2
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
3
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
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Which of my tasks are exposed
Pay wages of contracted farm laborers.
27.9
Provide food, drinking water, and field sanitation facilities to contracted workers.
27.9
Recruit and hire agricultural workers.
50.0
Employ foremen to deal directly with workers when recruiting, hiring, instructing, assigning tasks, and enforcing work rules.
100.0
Supervise the work of contracted employees.
100.0
Furnish tools for employee use.
27.9
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Employ foremen to deal directly with workers when recruiting, hiring, instructing, assigning tasks, and enforcing work rules.
Task: Supervise the work of contracted employees.
Local demand is growing
How this score is calculated
Map Farm Labor Contractors to its standard O*NET task list (6 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 0.0, robotics 27.9.
The timeline comes from the higher axis (27.9), shifted for local demand (growing), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.