Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
In short: today's best AI models could do about 10% 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 conversations, in-person and hands-on work, about 90% of the time, stays with people.
Set up, operate, or tend grinding and related tools that remove excess material or burrs from surfaces, sharpen edges or corners, or buff, hone, or polish metal or plastic work pieces.
67,000 people do this job in the US. $46,550 a year.
Reviewed on October 2, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on0 h
- A person checks AI's work4 h
- Desk work AI can't do yet0 h
- Stays with people36 h
Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic, now: of a 40-hour week, AI could take on 0 hours, 4 hours become checking its work, 0 hours are desk work it can't do yet, and 36 hours stay with people.
The largest pieces: Compute settings and indexings (about 2 h, a person checks); Read the job's drawings (about 1 h, a person checks); Keep parts and tools stocked (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.
- 9%
- 1%
- 2%
- 3%
- 85%
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.
What handing it over would take, now
The 10% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
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, 1%. With Claude Mythos Preview (early), measured in April 2026, 10%. Today's estimate: 10%.
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
None of this job's 18 tasks showed up in the Claude.ai conversations Anthropic sampled in any of its 7 releases, from February 2025 to May 2026.
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 91% 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: 88% in a purpose-built cell. 0.88 of 3, the 191st highest of the 923 jobs.
- Not physical 9%
- No robot shown 4%
- In a purpose-built cell 88%
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.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
90% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Run the grinding machine, the hands-on work, 19%
- Set the machine controls, the hands-on work, 11%
- Repair the machine or call engineering, the hands-on work, 11%
- Measure and lay out work, the hands-on work, 7%
- Watch the machine and adjust, the hands-on work, 7%
- Select the tooling, the hands-on work, 6%
What becomes checking the machine
9% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 1% is desk work that is small if wrong, which can be handed over with lighter checks.
- Compute settings and indexings, costly if wrong, 6%
- Read the job's drawings, costly if wrong, 3%
What to learn
- The hands-on craft. About 85% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 18 tasks from .
| Task | ||||
|---|---|---|---|---|
| Set up, operate, or tend grinding and related tools that remove excess material or burrs from surfaces, sharpen edges or corners, or buff, hone, or polish metal or plastic workpieces.Setting up and running the grinder.Done daily by most who do it. Robots: in a purpose-built cell, Studer S22 cylindrical grinder with integral gantry loader tended by a FANUC 100iC robot (United Grinding (Studer) / FANUC) (deployed). Anthropic's rating. | 19% | 38 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Set and adjust machine controls according to product specifications, using knowledge of machine operation.Setting the controls to the specifications.Done daily by most who do it. Robots: in a purpose-built cell, ANCA AIMS / AIMS AutoComp (with AutoFetch mobile robot and ZOLLER measurement) (sold). Anthropic's rating. | 11% | 21 min8 min to 42 min | Hands-on 100% | Stays with people |
| Repair or replace machine parts, using hand tools, or notify engineering personnel when corrective action is required.Fixing what can be fixed with hand tools, and calling engineering for the rest.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 11% | 38 min13 min to 2.5 h | Hands-on 80%, conversation 20% | Stays with people |
| Measure workpieces and lay out work, using precision measuring devices.Marking out the pieces with precision instruments.Done hourly or more by most who do it. Robots: in a purpose-built cell, Marposs Thruvar in-process grinding gauges / P3UP, P1dme control units (sold). Anthropic's rating. | 7% | 13 min4 min to 29 min | Hands-on 100% | Stays with people |
| Observe machine operations to detect any problems, making necessary adjustments to correct problems.Watching the run at the machine and correcting it.Done hourly or more by most who do it. Robots: in a purpose-built cell, Marposs in-process gauging and balancing systems for grinding machines (P7/P3UP/Micromar family) (sold). Anthropic's rating. | 7% | 13 min4 min to 38 min | Hands-on 100% | Stays with people |
| Compute machine indexings and settings for specified dimensions and base reference points.Working out the settings from the dimensions.Done daily by most who do it. | 6% | 21 min8 min to 42 min | Documents 100% | 100%Half by now |
| Maintain stocks of machine parts and machining tools.Counting and stocking the parts and tools, with the reorder.Done daily by most who do it.Robots: in a purpose-built cell, Fastems Central Tool Storage (CTS) / Gantry Tool Storage with tool-handling robot (deployed). Anthropic's rating. | 6% | 25 min13 min to 1.3 h | Hands-on 80%, systems 20% | 20%Desk part half by now |
| Study blueprints, work orders, or machining instructions to determine product specifications, tool requirements, and operational sequences.Reading the drawings and work order; the knowing is the operator's own.Done daily by most who do it. | 6% | 13 min4 min to 33 min | Documents 50%, in person 50% | 50%Desk part half by now |
| Select machine tooling to be used, using knowledge of machine and production requirements.Choosing the wheels and tools for the job at the machine.Done hourly or more by most who do it. | 6% | 13 min4 min to 25 min | Hands-on 100% | Stays with people |
| Inspect or measure finished workpieces to determine conformance to specifications, using measuring instruments, such as gauges or micrometers.Gauging each piece by hand.Done hourly or more by most who do it. Robots: in a purpose-built cell, Marposs FenarL in-process grinder gauge (with P7/P1dme control units) (sold). Anthropic's rating. | 4% | 8 min3 min to 21 min | Hands-on 100% | Stays with people |
| Mount and position tools in machine chucks, spindles, or other tool holding devices, using hand tools.Fitting the tools into the chucks and spindles.Done daily by most who do it. Robots: in a purpose-built cell, WALTER grinding wheel changer (Helitronic tool grinders) (sold). Anthropic's rating. | 4% | 13 min4 min to 33 min | Hands-on 100% | Stays with people |
| Lift and position workpieces, manually or with hoists, and secure them in hoppers or on machine tables, faceplates, or chucks, using clamps.Lifting pieces onto the machine, by hand or hoist, and clamping them.Done daily by most who do it. Robots: in a purpose-built cell, ActiNav Autonomous Bin Picking with UR10e cobot at New England Union Company (Universal Robots) (deployed). Anthropic's rating. | 3% | 8 min3 min to 21 min | Hands-on 100% | 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. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% 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.
- Tool Grinders, Filers, and Sharpeners6% now, 6% by 2028
- Grinding and Polishing Workers, Hand11% now, 11% by 2028
- Woodworking Machine Setters, Operators, and Tenders, Except Sawing9% now, 9% by 2028
- Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic18% now, 18% by 2028
- Cutting and Slicing Machine Setters, Operators, and Tenders7% now, 7% by 2028
- Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic38% now, 38% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
- Fabricated Metal Product Manufacturing (3321, 3322, 3325, 3326, and 3329 only)9,720 people
- Foundries8,840 people
- Machine Shops; Turned Product; and Screw, Nut, and Bolt Manufacturing8,110 people
- Aerospace Product and Parts Manufacturing4,590 people
- Fabricated Metal Product Manufacturing (3323 and 3324 only)4,060 people
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 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic is within reach of AI now?
- An estimated 10% of the working time is within reach of AI models now and 10% 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 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic 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: Compute settings and indexings (100% of the task's time now); Read the job's drawings (50% of the task's time now); and Keep parts and tools stocked (20% of the task's time now).
- Which tasks of Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Run the grinding machine; Set the machine controls; and Repair the machine or call engineering. Together such tasks are about 82% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are the hands-on craft; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 90% of the working time stays with people, 9% becomes checking a model's work and 1% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic?
- 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 0%. 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 Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic?
- Anthropic's robot exposure rating (September 2026) finds 91% 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. Its index is 0.88 of 3. One example: Studer S22 cylindrical grinder with integral gantry loader tended by a FANUC 100iC robot for "run the grinding machine". 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 18 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on October 2, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), 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.
This job in your state or metro area
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The full personal report is $19, to print or save as a PDF: every task with when it comes within reach, which ones your description names, the parts of your description the task list does not cover, what stays with you, what becomes checking the machine and what to learn.