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AI exposure Production

Will AI replace shoe machine operators and tenders?

Shoe Machine Operators and Tenders. US occupation 51-6042.

0.0%

of the time shoe machine operators and tenders spend on their work goes on tasks where AI is already being used to do that work.of shoe machine operators and tenders' working hours go on tasks AI has already started taking off their plate.

Ranked 346 of 756 US occupations. No AI use seen in the work yet, along with 410 other occupations.No handover started yet, along with 410 other occupations.

Average US job 7.7%

Among the least exposed jobs there are.Still waiting for liberation.

What AI already does for shoe machine operators and tendersWhat shoe machine operators and tenders no longer do alone

O*NET lists 18 tasks for shoe machine operators and tenders. None of them shows meaningful AI use at work yet.

What it does not touch, yetWhat still needs a human

18 of the 18 tasks show no AI use at work in the data.

  • Align parts to be stitched, following seams, edges, or markings, before positioning them under needles.Core task
  • Cut excess thread or material from shoe parts, using scissors or knives.Core task
  • Draw thread through machine guide slots, needles, and presser feet in preparation for stitching, or load rolls of wire through machine axles.Core task
  • Operate or tend machines to join, decorate, reinforce, or finish shoes and shoe parts.Core task
  • Perform routine equipment maintenance such as cleaning and lubricating machines or replacing broken needles.Core task
  • Remove and examine shoes, shoe parts, and designs to verify conformance to specifications such as proper embedding of stitches in channels.Core task
Show the other 12
  • Select and place spools of thread or pre-wound bobbins into shuttles, or onto spindles or loupers of stitching machines.Core task
  • Study work orders or shoe part tags to obtain information about workloads, specifications, and the types of materials to be used.Core task
  • Switch on machines, lower pressure feet or rollers to secure parts, and start machine stitching, using hand, foot, or knee controls.Core task
  • Test machinery to ensure proper functioning before beginning production.Core task
  • Turn knobs to adjust stitch length and thread tension.Core task
  • Collect shoe parts from conveyer belts or racks and place them in machinery such as ovens or on molds for dressing, returning them to conveyers or racks to send them to the next work station.
  • Fill shuttle spools with thread from a machine's bobbin winder by pressing a foot treadle.
  • Hammer loose staples for proper attachment.
  • Position dies on material in a manner that will obtain the maximum number of parts from each portion of material.
  • Staple sides of shoes, pressing a foot treadle to position and hold each shoe under the feeder of the machine.
  • Turn screws to regulate size of staples.
  • Turn setscrews on needle bars, and position required numbers of needles in stitching machines.

How that compares

Among the 98 production occupations, shoe machine operators and tenders rank 86. The group averages 0.8%, and its most exposed job is Computer Numerically Controlled Tool Programmers at 12.1%. The average US occupation sits at 7.7%.

All 756 occupations, ranked

Level with 0.0%

  1. 346Ship Engineers 0.0%
  2. 346Shipping, Receiving, and Inventory Clerks 0.0%
  3. 346Shoe and Leather Workers and Repairers 0.0%

What this does and does not mean

Exposure is observed use, not a forecast. Anthropic maps work conversations with Claude onto the tasks O*NET lists for each job and asks, of the tasks AI could speed up, which are actually being used for at work. A use that does the task outright counts in full and a use that assists a person counts half. The task scores are averaged into the occupation figure by the share of time spent on each task.

A high figure means AI is already part of how this work gets done. It does not mean anyone has been replaced. Anthropic's own reading of the labour market finds no systematic rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting exposed jobs.No measurable rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting these jobs. Gap years for everyone. Labor market impacts, 5 March 2026.

Task list and titles from O*NET 31.0 by the US Department of Labor, Employment and Training Administration, used under the CC BY 4.0 licence. Scores from the Anthropic Economic Index (CC BY). Checked 1 November 2026. How the ranking works.