Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic
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 conversations, in-person and hands-on work, about 94% of the time, stays with people.
Set up, operate, or tend machines to saw, cut, shear, slit, punch, crimp, notch, bend, or straighten metal or plastic material.
171,200 people do this job in the US. $46,330 a year.
Reviewed on September 30, 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 work2 h
- Desk work AI can't do yet0 h
- Stays with people38 h
Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic, now: of a 40-hour week, AI could take on 0 hours, 2 hours become checking its work, 0 hours are desk work it can't do yet, and 38 hours stay with people.
The largest pieces: Plan operation sequences (about 1 h, a person checks); Read work orders (under an hour); Run machines and record data (under an hour, a person checks).
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.
- 5%
- 1%
- 0%
- 1%
- 93%
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 6% 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, 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 31 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0046% to 0.0029% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | none | n/a | 0 of 31 |
| Released March 2025 | none | n/a | 0 of 31 |
| The week of Aug 4, 2025 (released September 2025) | 0.0046% | 540 of 655 | 1 of 31 |
| The week of Nov 13, 2025 (released January 2026) | 0.0033% | 609 of 672 | 1 of 31 |
| The week of Feb 5, 2026 (released March 2026) | 0.0029% | 612 of 677 | 1 of 31 |
| April 2026 (released June 2026) | none | n/a | 0 of 31 |
| May 2026 (released June 2026) | none | n/a | 0 of 31 |
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 95% of this job's working time as physical work. For 94% of the working time, a robot has been shown doing most of the task somewhere: 78% in a purpose-built cell, 16% in a structured facility. 1.10 of 3, the 78th highest of the 923 jobs.
- Not physical 5%
- No robot shown 1%
- In a purpose-built cell 78%
- In a structured facility 16%
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
94% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Run cutting and press machines, the hands-on work, 13%
- Install and align punches and dies, the hands-on work, 9%
- Clean and lubricate machines, the hands-on work, 6%
- Set stops and change dies, the hands-on work, 6%
- Clean the work area, the hands-on work, 5%
- Replace worn machine parts, the hands-on work, 4%
What becomes checking the machine
5% 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.
- Plan operation sequences, costly if wrong, 4%
- Run machines and record data, severe if wrong, 1%
What to learn
- The hands-on craft. About 93% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Judgment where errors are severe. Some tasks are severe if wrong. Knowing when not to trust an output matters more there than speed.
- 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 or sign: 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 . 31 tasks from .
| Task | ||||
|---|---|---|---|---|
| Set up, operate, or tend machines to saw, cut, shear, slit, punch, crimp, notch, bend, or straighten metal or plastic material.Setting up and tending the saws, shears and presses.Done daily by most who do it. Robots: in a purpose-built cell, Bystronic Bending Cell (Xpert Pro press brake + 7-axis robot, BySoft Cell Control Bend) (sold). Anthropic's rating. | 13% | 38 min13 min to 2.5 h | Hands-on 100% | Stays with people |
| Install, align, and lock specified punches, dies, cutting blades, or other fixtures in rams or beds of machines, using gauges, templates, feelers, shims, and hand tools.Fitting and locking punches, dies and blades with gauges and shims.Done hourly or more by most who do it.Robots: in a purpose-built cell, Amada HG-ATC (Automatic Tool Changer press brake) (sold). Anthropic's rating. | 9% | 33 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Clean and lubricate machines.Cleaning and oiling the machines.Done daily by most who do it.Robots: in a purpose-built cell, AMD Machines robotic machine tending cells (FANUC/ABB robots, e.g., CRX-10iA/L) (Advanced Manufacturing Development (AMD Machines), integrator using FANUC and ABB robots) (sold). Anthropic's rating. | 6% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
| Set stops on machine beds, change dies, and adjust components, such as rams or power presses, when making multiple or successive passes.Setting the bed and changing dies between passes.Done hourly or more by most who do it.Robots: in a structured facility, AMADA HG ATC / HRB ATC press brake with Automatic Tool Changer (sold). Anthropic's rating. | 6% | 29 min13 min to 1.1 h | Hands-on 100% | Stays with people |
| Start machines, monitor their operations, and record operational data.Starting and watching the machine, with the run data entered.Done hourly or more by most who do it. Robots: in a purpose-built cell, Robotiq Machine Tending Solution (UR cobot + button-pusher actuator + stack-light sensor) (sold). Anthropic's rating. | 5% | 13 min4 min to 50 min | Hands-on 80%, systems 20% | 20%Desk part half by now |
| Plan sequences of operations, applying knowledge of physical properties of workpiece materials.Working out the order of operations from the material's properties.Done daily by most who do it. | 5% | 25 min8 min to 50 min | Documents 70%, hands-on 30% | 70%Desk part half by now |
| Clean work area.Tidying the work area.Done daily by most who do it. Robots: in a purpose-built cell, Okuma ARMROID (in-machine robot) (sold). Anthropic's rating. | 5% | 13 min4 min to 25 min | Hands-on 100% | Stays with people |
| Remove housings, feed tubes, tool holders, or other accessories to replace worn or broken parts, such as springs or bushings.Taking off housings and holders to change springs and bushings.Done yearly or less by most who do it.Robots: no robot shown. Anthropic's rating. | 4% | 38 min17 min to 1.7 h | Hands-on 100% | Stays with people |
| Position guides, stops, holding blocks, or other fixtures to secure and direct workpieces, using hand tools and measuring devices.Setting guides and stops with hand tools and measures.Done hourly or more by most who do it.Robots: in a purpose-built cell, Bystronic Xpert Tool Changer (Xpert / Xpert Pro press brake) (sold). Anthropic's rating. | 4% | 17 min4 min to 42 min | Hands-on 100% | Stays with people |
| Test and adjust machine speeds or actions, according to product specifications, using gauges and hand tools.Adjusting speeds and actions with gauges and hand tools.Done hourly or more by most who do it. Robots: in a purpose-built cell, TRUMPF Active Speed Control (TruLaser 2D laser cutting machines) (sold). Anthropic's rating. | 4% | 13 min4 min to 29 min | Hands-on 100% | Stays with people |
| Sharpen dulled blades, using bench grinders, abrasive wheels, or lathes.Sharpening on bench grinders and lathes.Done more than monthly by most who do it.Robots: in a purpose-built cell, MVM KX automatic knife grinder / surface grinder (M.V.M. S.r.l.) (sold). Anthropic's rating. | 4% | 33 min13 min to 1.3 h | Hands-on 100% | Stays with people |
| Examine completed workpieces for defects, such as chipped edges or marred surfaces and sort defective pieces according to types of flaws.Looking over the pieces and sorting the flawed ones.Done hourly or more by most who do it. Robots: in a purpose-built cell, Kistler IVAN DWS-512 inline vision inspection system (sold). Anthropic's rating. | 4% | 8 min3 min to 17 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.
- Cutting and Slicing Machine Setters, Operators, and Tenders7% now, 7% 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
- Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic10% now, 10% by 2028
- Rolling Machine Setters, Operators, and Tenders, Metal and Plastic14% now, 14% by 2028
- Forging Machine Setters, Operators, and Tenders, Metal and Plastic6% now, 6% by 2028
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
- Motor Vehicle Parts Manufacturing27,220 people
- Fabricated Metal Product Manufacturing (3321, 3322, 3325, 3326, and 3329 only)26,710 people
- Fabricated Metal Product Manufacturing (3323 and 3324 only)22,210 people
- Plastics Product Manufacturing12,850 people
- Merchant Wholesalers, Durable Goods (4232, 4233, 4235, 4236, 4237, and 4239 only)10,120 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 Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic 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 Cutting, Punching, and Press Machine 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: Plan operation sequences (70% of the task's time now); Read work orders (50% of the task's time now); and Run machines and record data (20% of the task's time now).
- Which tasks of Cutting, Punching, and Press Machine 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 cutting and press machines; Install and align punches and dies; and Clean and lubricate machines. Together such tasks are about 87% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Cutting, Punching, and Press Machine 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; judgment where errors are severe; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 94% of the working time stays with people, 5% 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 Cutting, Punching, and Press Machine 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 Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic?
- Anthropic's robot exposure rating (September 2026) finds 95% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 94% of the working time, 16% in a structured facility. Its index is 1.10 of 3. One example: AMADA HG ATC / HRB ATC press brake with Automatic Tool Changer for "set stops and change dies". 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 31 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 September 30, 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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