Textile Knitting and Weaving Machine Setters, Operators, and Tenders

Most exposed tasks

Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.

• Record information about work completed and machine settings.71
• Confer with co-workers to obtain information about orders, processes, or problems.63

Augmentable tasks

Work where AI assists rather than replaces — the productivity frontier of this role.

• Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.52
• Program electronic equipment.51
• Repair or replace worn or defective needles and other components, using hand tools.49

Most durable tasks

Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.

The current data release does not distinguish durable tasks for this role.

Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.

What this means

A score of 47 puts Textile Knitting and Weaving Machine Setters, Operators, and Tenders in the second quartile of analyzed occupations. In practice, exposure this level is about the mix: 2 of 19 analyzed tasks lean automatable, 17 augmentable, and 0 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.

One next move: adopt AI deliberately on the augmentable tasks and build visible evidence of the durable ones.

Lower-exposure adjacent roles

Shown only when the target is at least 10 points lower under the same score version and skill overlap is at least 50%. These are adjacent roles with lower task exposure — not guaranteed “safe careers”.

Labor-market context

• $39,530median wage
• 13,030employed
• 1,700annual openings
• -11.1%projected growth

Context only — labor statistics are not inputs to the exposure score. See methodology.

Related roles

Adjacent by skills or family — no exposure claim implied.

• Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders 42
• Textile Cutting Machine Setters, Operators, and Tenders 46
• Paper Goods Machine Setters, Operators, and Tenders 47
• Shoe Machine Operators and Tenders 37
• Textile Bleaching and Dyeing Machine Operators and Tenders 51
• Cutting and Slicing Machine Setters, Operators, and Tenders 53

FAQ — Textile Knitting and Weaving Machine Setters, Operators, and Tenders

What does a score of 47 mean for a Textile Knitting and Weaving Machine Setters, Operators, and Tenders?

It means that, weighted across the 19 tasks we analyzed for this role, the task mix sits at 47 on a 0–100 exposure scale — in the second quartile of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.

Which tasks in this role are most exposed to AI?

The highest-exposure tasks are: Record information about work completed and machine settings; Confer with co-workers to obtain information about orders, processes, or problems. Exposure is scored per task from structured data, not generated by a language model.

Which parts of this job are most durable?

The current data release does not distinguish durable tasks for this role.

Is this score personalized to me?

No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.