AI exposure: Textile Knitting and Weaving Machine Setters, Operators, and Tenders
Set up, operate, or tend machines that knit, loop, weave, or draw in textiles.
Reading this score
computedAt 20.4% of weighted task load, Textile Knitting and Weaving Machine Setters, Operators, and Tenders sits at the 37th percentile, below the point where a job's centre of gravity has moved. 68.9% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.
What holds the line here is embodiment. Across this occupation's 19 tasks it averages 2.48 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.
The most exposed thing this job does is Record information about work completed and machine settings, at 73.3%. The least is Clean, oil, and lubricate machines, at 0.0%. A gap of 73.3% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within production occupations, this one is more exposed than most. The median across the 107 roles in the group is 16.1%, and only 25 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 19 tasks, 5 are currently banded exposed, 0 assisted and 14 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 1.27 | 0-4 |
| Embodiment | 2.48 | 0-3 |
| Presence | 0.54 | 0-3 |
| Accountability | 0.36 | 0-3 |
| Context | 1.16 | 0-3 |
| Verification cost | 0.84 | 0-3 |
Task by task
19 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Record information about work completed and machine settings. | 73.3% | 26.7% | 0.0% | 4.29 | exposed |
| Confer with co-workers to obtain information about orders, processes, or problems. | 60.0% | 15.0% | 25.0% | 3.99 | exposed |
| Notify supervisors or repair staff of mechanical malfunctions. | 45.0% | 30.0% | 25.0% | 4.45 | exposed |
| Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements. | 43.8% | 18.8% | 37.5% | 4.20 | exposed |
| Program electronic equipment. | 26.7% | 23.3% | 50.0% | 4.38 | exposed |
| Observe woven cloth to detect weaving defects. | 18.3% | 6.7% | 75.0% | 4.65 | untouched |
| Inspect products to ensure that specifications are met and to determine if machines need adjustment. | 18.3% | 6.7% | 75.0% | 4.47 | untouched |
| Start machines, monitor operations, and make adjustments as needed. | 15.0% | 10.0% | 75.0% | 4.33 | untouched |
| Stop machines when specified amounts of product have been produced. | 15.0% | 10.0% | 75.0% | 4.25 | untouched |
| Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching. | 15.0% | 10.0% | 75.0% | 4.37 | untouched |
| Adjust machine heating mechanisms, tensions, and speeds to produce specified products. | 15.0% | 10.0% | 75.0% | 3.75 | untouched |
| Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects. | 13.3% | 11.7% | 75.0% | 4.48 | untouched |
| Inspect machinery to determine whether repairs are needed. | 13.3% | 11.7% | 75.0% | 4.19 | untouched |
| Operate machines for test runs to verify adjustments and to obtain product samples. | 13.3% | 11.7% | 75.0% | 3.84 | untouched |
| Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation. | 10.7% | 5.9% | 83.3% | 4.31 | untouched |
| Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing. | 0.0% | 0.0% | 100.0% | 4.58 | untouched |
| Remove defects in cloth by cutting and pulling out filling. | 0.0% | 0.0% | 100.0% | 4.50 | untouched |
| Repair or replace worn or defective needles and other components, using hand tools. | 0.0% | 0.0% | 100.0% | 4.12 | untouched |
| Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns. | 0.0% | 0.0% | 100.0% | 4.04 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.
What this means in practice
Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.