Packers and Packagers, Hand

Transportation & Material Moving

AI exposure

  • Data source: BLSPublished: 2026-08

    Low· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.20

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 86% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Record product, packaging, and order information on specified forms and records.

O*NET Task ID 3214

1.0
Mark and label containers, container tags, or products, using marking tools.

O*NET Task ID 3211

0.0
Measure, weigh, and count products and materials.

O*NET Task ID 3212

0.0
Remove completed or defective products or materials, placing them on moving equipment, such as conveyors, or in specified areas, such as loading docks.

O*NET Task ID 3215

0.0
Seal containers or materials, using glues, fasteners, nails, and hand tools.

O*NET Task ID 3216

0.0
Load materials and products into package processing equipment.

O*NET Task ID 3217

0.0
Assemble, line, and pad cartons, crates, and containers, using hand tools.

O*NET Task ID 3218

0.0
Clean containers, materials, supplies, or work areas, using cleaning solutions and hand tools.

O*NET Task ID 3219

0.0
Transport packages to customers' vehicles.

O*NET Task ID 3220

0.0
Place or pour products or materials into containers, using hand tools and equipment, or fill containers from spouts or chutes.

O*NET Task ID 3221

0.0
Obtain, move, and sort products, materials, containers, and orders, using hand tools.

O*NET Task ID 3222

0.0
Examine and inspect containers, materials, or products to ensure that product quality and packing specifications are met.

O*NET Task ID 21216

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information