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Light Truck Drivers

Occupation · SOC 53-3033.00

Drive a light vehicle, such as a truck or van, with a capacity of less than 26,001 pounds Gross Vehicle Weight (GVW), primarily to pick up merchandise or packages from a distribution center and deliver. May load and unload vehicle.

Also called: Delivery Driver · Driver · Package Car Driver · Truck Driver · Bulk Delivery Driver · Light Truck Driver · Package Delivery Driver · Route Driver · Service Provider · Warehouse Driver · Car Escort · Commercial Driver

Job family: Transportation and Material Moving Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-53-3033-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

30th-percentile task overlap — yet about 120,200 openings a year (+7.3% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 14th -1.1
LLM task exposure, γ (OpenAI / Eloundou) Moderate 44th 0.5
AI assistant applicability (Microsoft) Moderate 38th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.4), and including AI-powered software (γ 0.5). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.7 · 57th percentile among occupations · Moderate

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Growing fast · +7.3% by 2034
Projected annual openings 120,200
Employment 2024 → 2034 1,079,800 → 1,158,600

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

28% mean task exposure (2025)
51st percentile of 427 placed occupations
+4 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Car, Taxi and Van Drivers · 8322 28% Gradient 1

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 14 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

English Language 3.8
Transportation 3.4
Customer and Personal Service 3.4
Public Safety and Security 2.9

Abilities

Multilimb Coordination 3.6
Far Vision 3.6
Spatial Orientation 3.5
Problem Sensitivity 3.3
Near Vision 3.3
Oral Comprehension 3.1
Oral Expression 3.1
Deductive Reasoning 3.1
Manual Dexterity 3.1
Control Precision 3.1
Reaction Time 3.1
Static Strength 3.1
Trunk Strength 3.1
Stamina 3.1
Written Comprehension 3.0
Inductive Reasoning 3.0
Information Ordering 3.0
Perceptual Speed 3.0
Response Orientation 3.0
Extent Flexibility 3.0
Depth Perception 3.0
Speech Recognition 3.0
Speech Clarity 3.0
Category Flexibility 2.9
Visualization 2.9
Selective Attention 2.9
Arm-Hand Steadiness 2.9
Finger Dexterity 2.9
Rate Control 2.9

Essential skills

Reading Comprehension 3.1
Active Listening 3.1
Speaking 3.1
Monitoring 3.1
Critical Thinking 3.0

Transferable skills

Operation and Control 3.1
Time Management 3.0

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft Windows Operating system software Hot technology
Automatic routing software Route navigation software
Computerized inventory tracking software Inventory management software
Eko Desktop communications software
FreightDATA Industrial control software
IBM Domino Communications server software
Inventory management systems Inventory management software
Package location and tracking software Industrial control software
Recordkeeping software Data base user interface and query software
Vehicle location and tracking software Industrial control software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

In an Enclosed Vehicle or Operate Enclosed Equipment 4.6
Contact With Others 4.6
Importance of Being Exact or Accurate 4.3
Telephone Conversations 4.2
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.2
Time Pressure 4.2
Outdoors, Exposed to All Weather Conditions 4.2
Deal With External Customers or the Public in General 4.1
Frequency of Decision Making 4.0
Impact of Decisions on Co-workers or Company Results 3.8
Face-to-Face Discussions with Individuals and Within Teams 3.7
Spend Time Sitting 3.6
Exposed to Very Hot or Cold Temperatures 3.5
Work With or Contribute to a Work Group or Team 3.5
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 3.5
Importance of Repeating Same Tasks 3.4
Spend Time Walking or Running 3.3
Freedom to Make Decisions 3.3
Work Outcomes and Results of Other Workers 3.3
E-Mail 3.3
Health and Safety of Other Workers 3.1
Determine Tasks, Priorities and Goals 3.1
Exposed to Contaminants 3.1
Consequence of Error 3.1
Coordinate or Lead Others in Accomplishing Work Activities 3.1
Spend Time Bending or Twisting Your Body 3.0
Level of Competition 3.0
Physical Proximity 3.0
Indoors, Not Environmentally Controlled 2.8
Spend Time Standing 2.8
Spend Time Making Repetitive Motions 2.8
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.7
Degree of Automation 2.7
Dealing With Unpleasant, Angry, or Discourteous People 2.6
Outdoors, Under Cover 2.6
Indoors, Environmentally Controlled 2.4
Pace Determined by Speed of Equipment 2.4
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.4
Conflict Situations 2.3
Exposed to Cramped Work Space, Awkward Positions 2.1

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Transportation and Materials Moving . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 74.0%
Less than a High School Diploma 24.6%
Associate's Degree (or other 2-year degree) 1.2%
Some College Courses 0.3%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 6.9
Conventional 4.4
Enterprising 2.1
Investigative 1.5
Social 1.4

Interest areas

Transportation/Machine Operation 6.5
Physical/Manual Labor 4.5
Mechanics/Electronics 2.3
Sales 1.6
Personal Service 1.5
Accounting 1.4
Office Work 1.3

Work styles

Dependability 2.6
Attention to Detail 1.8
Cautiousness 1.8
Integrity 1.7

Wages & employment

U.S. · annual wages (BLS OEWS)

$30k10th$37k25th$44kMedian$52k75th$80k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
1.08M20241.16M2034 (proj.)+7.3% · Growing fast
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $29,580
25th percentile $36,670
Median (50th) $44,140
75th percentile $52,460
90th percentile $79,630
People employed 994,410

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Transportation and Warehousing · Sector 480,180 $47,390
Retail Trade · Sector 174,340 $33,950
Wholesale Trade · Sector 152,990 $43,710
Manufacturing · Sector 34,280 $43,120
Administrative and Support and Waste Management and Remediation Services · Sector 29,290 $42,780
Real Estate and Rental and Leasing · Sector 24,430 $38,950
Construction · Sector 19,090 $46,820
Other Services (except Public Administration) · Sector 18,120 $38,170
Health Care and Social Assistance · Sector 16,490 $37,120
Professional, Scientific, and Technical Services · Sector 14,800 $43,670
Accommodation and Food Services · Sector 12,940 $39,430
Pharmacies and Drug Retailers · National industry 12,330 $34,170

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Transportation and Warehousing · Sector 10.07× 480,180
Wholesale Trade · Sector 3.93× 152,990
Pharmacies and Drug Retailers · National industry 2.7× 12,330
Farm and Garden Machinery and Equipment Merchant Wholesalers · National industry 2.28× 1,680
Retail Trade · Sector 1.73× 174,340
Real Estate and Rental and Leasing · Sector 1.6× 24,430
Newspaper Publishers · National industry 1.45× 850
Testing Laboratories and Services · National industry 1.22× 1,340

Part of the Supply Chain & Transportation career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Light Truck Drivers sits at the 30th percentile of AI task-overlap and the 19th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Light Truck Drivers Industrial Truck and Tractor Operators Tank Car, Truck, and Ship Loaders Driver/Sales Workers Railroad Conductors and Yardmasters Cargo and Freight Agents AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Light Truck Drivers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 51st percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Light Truck Drivers show 30th-percentile AI task overlap — and about 120,200 annual U.S. openings

  • Light Truck Drivers rank in the 30th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 120,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be growing fast (+7.3%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $44,140, across about 994,410 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Light Truck Drivers show 30th-percentile AI task overlap — and about 120,200 annual U.S. openings

• Light Truck Drivers rank in the 30th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 120,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be growing fast (+7.3%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $44,140, across about 994,410 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Light Truck Drivers". https://singulariki.com/roles/role-53-3033-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Light Truck Drivers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-53-3033-00

APA

Singulariki. (2026). Light Truck Drivers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-53-3033-00

BibTeX
@misc{singulariki-role-53-3033-00,
  title  = {Light Truck Drivers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-53-3033-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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