Will AI replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
Most of the work in Textile Knitting and Weaving Machine Setters, Operators, and Tenders still leans on things AI struggles with — research rates its theoretical AI reach at only ~16%, and real-world use lower still.
O*NET-SOC 51-6063
How your 12 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~16%). By late 2025, real-world AI use had reached about 3% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~16%), real-world use (~3%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 12 tasks, ratedBased on real task-level AI scores — click to collapse
- Record information about work completed and machine settings.
- Observe woven cloth to detect weaving defects.
- Remove defects in cloth by cutting and pulling out filling.
- Inspect products to ensure that specifications are met and to determine if machines need adjustment.
- Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.
- Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.
- Notify supervisors or repair staff of mechanical malfunctions.
- 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.
- Start machines, monitor operations, and make adjustments as needed.
- Inspect machinery to determine whether repairs are needed.
- Confer with co-workers to obtain information about orders, processes, or problems.
- Stop machines when specified amounts of product have been produced.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.