Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
In short: today's best AI models could do about 6% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about the same by the end of 2028 if progress keeps its long-run pace. The people and hands-on work, about 94% of the time, stays with people.
Set up, operate, or tend machines to crush, grind, or polish materials, such as coal, glass, grain, stone, food, or rubber.
26,000 people do this job in the US. $48,540 a year.
A typical 40-hour week
Not yet reviewed task by task- AI could take on2 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people38 h
Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders, now: of a 40-hour week, AI could take on 2 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 38 hours stay with people.
The largest pieces: Record data from operations, testing, and production on specified forms (about 1 h); Notify supervisors of needed repairs (under an hour); Read work orders to determine production specifications and information (under an hour).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 6%
- 0%
- 94%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 3%. With Claude Mythos Preview (early), measured in April 2026, 6%. Today's estimate: 6%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, no reportable share went to this job's tasks in May 2026; 0 of its 21 tasks showed up. Within each of Anthropic's methods, never across a change: none to 0.0022% (released February and March 2025); 0.0071% to none (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 21 |
| Released March 2025 | 0.0022% | 650 of 703 | 1 of 21 |
| The week of Aug 4, 2025 (released September 2025) | 0.0071% | 497 of 655 | 2 of 21 |
| The week of Nov 13, 2025 (released January 2026) | 0.0026% | 628 of 672 | 1 of 21 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 21 |
| April 2026 (released June 2026) | none | n/a | 0 of 21 |
| May 2026 (released June 2026) | none | n/a | 0 of 21 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 92% of this job's working time as physical work. For 88% of the working time, a robot has been shown doing most of the task somewhere: 40% in a purpose-built cell, 48% in a structured facility. 1.36 of 3, the 36th highest of the 923 jobs.
- Not physical 8%
- No robot shown 4%
- In a purpose-built cell 40%
- In a structured facility 48%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
The tasks
Sorted by . 21 tasks from .
| Task | Kind | |||
|---|---|---|---|---|
| Observe operation of equipment to ensure continuity of flow, safety, and efficient operation, and to detect malfunctions.Done hourly or more by most who do it. Robots: in a structured facility, ANYmal (D) with visual/thermal/acoustic/gas payload (ANYbotics) (deployed). Anthropic's rating. | 21% | 25 min8 min to 50 min | Stays with people | |
| Clean, adjust, and maintain equipment, using hand tools.Done daily by most who do it. Robots: in a purpose-built cell, Robotic CNC machine-tending cell (AMD Machines integration) (sold). Anthropic's rating. | 12% | 38 min13 min to 1.7 h | Stays with people | |
| Tend accessory equipment, such as pumps and conveyors, to move materials or ingredients through production processes.Done hourly or more by most who do it. Robots: in a purpose-built cell, iSAM autonomous combined stacker/reclaimers (and autonomous ship loaders/unloaders) (deployed). Anthropic's rating. | 10% | 21 min8 min to 38 min | Stays with people | |
| Transfer materials, supplies, and products between work areas, using moving equipment and hand tools.Done daily by most who do it.Robots: in a structured facility, Cyngn DriveMod Tugger (deployed). Anthropic's rating. | 6% | 25 min8 min to 50 min | Stays with people | |
| Weigh or measure materials, ingredients, or products at specified intervals to ensure conformance to requirements.Done daily by most who do it. Robots: in a purpose-built cell, ISOLOK SAE automatic sampler (Sentry Equipment) (sold). Anthropic's rating. | 5% | 13 min4 min to 29 min | Stays with people | |
| Clean work areas.Done daily by most who do it. Robots: in a structured facility, Tennant X16 SWEEP (BrainOS autonomy) (sold). Anthropic's rating. | 5% | 17 min8 min to 50 min | Stays with people | |
| Examine materials, ingredients, or products, visually or with hands, to ensure conformance to established standards.Done daily by most who do it. Robots: in a purpose-built cell, Bühler SPARK Pro / SORTEX optical sorters (sold). Anthropic's rating. | 4% | 17 min4 min to 33 min | Stays with people | |
| Reject defective products and readjust equipment to eliminate problems.Done daily by most who do it. Robots: in a purpose-built cell, Bühler SORTEX S UltraVision / SORTEX A optical sorters (sold). Anthropic's rating. | 4% | 25 min8 min to 1.3 h | Stays with people | |
| Dislodge and clear jammed materials or other items from machinery and equipment, using hand tools.Done more than weekly by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 25 min8 min to 1.7 h | Stays with people | |
| Add or mix chemicals and ingredients for processing, using hand tools or other devices.Done hourly or more by most who do it.Robots: in a purpose-built cell, Tinsley robotic depalletizer with automatic bag dump station (sold). Anthropic's rating. | 4% | 21 min8 min to 42 min | Stays with people | |
| Inspect chains, belts, or scrolls for signs of wear.Done daily by most who do it. Robots: in a structured facility, Spot + Orbit AIVI-Learning (Gemini Robotics ER integration) (Boston Dynamics / Google DeepMind) (deployed). Anthropic's rating. | 4% | 17 min8 min to 38 min | Stays with people | |
| Move controls to start, stop, or adjust machinery and equipment that crushes, grinds, polishes, or blends materials.Done daily by most who do it. Robots: in a structured facility, Metso IC70C crusher automation (Nordberg HP cone crushers) (sold). Anthropic's rating. | 3% | 8 min2 min to 17 min | Stays with people |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic10% now, 10% by 2028
- Grinding and Polishing Workers, Hand11% now, 11% by 2028
- Machine Feeders and Offbearers5% now, 5% by 2028
- Cutting and Slicing Machine Setters, Operators, and Tenders7% now, 7% by 2028
- Tool Grinders, Filers, and Sharpeners6% now, 6% by 2028
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders13% now, 13% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders is within reach of AI now?
- An estimated 6% of the working time is within reach of AI models now and 6% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Record data from operations, testing, and production on specified forms (100% of the task's time now); Notify supervisors of needed repairs (100% of the task's time now); and Read work orders to determine production specifications and information (100% of the task's time now).
- Which tasks of Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Observe operation of equipment to ensure continuity of flow, safety, and efficient operation, and to detect malfunctions; Clean, adjust, and maintain equipment, using hand tools; and Tend accessory equipment, such as pumps and conveyors, to move materials or ingredients through production processes. Together such tasks are about 94% of the working time.
- Do people already use AI for the work of Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 4%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders?
- Anthropic's robot exposure rating (September 2026) finds 92% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 88% of the working time, 48% in a structured facility. Its index is 1.36 of 3. One example: ANYmal (D) with visual/thermal/acoustic/gas payload for "observe operation of equipment to ensure continuity of flow, safety, and efficient operation, and to detect malfunctions". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 21 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, each classed as desk, people or body work, and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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