Mixing and Blending Machine Setters, Operators, and Tenders
In short: today's best AI models could do about 3% 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 97% of the time, stays with people.
Set up, operate, or tend machines to mix or blend materials, such as chemicals, tobacco, liquids, color pigments, or explosive ingredients.
94,920 people do this job in the US. $48,990 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 on1 h
- A person checks AI's work0 h
- Desk work AI can't do yet0 h
- Stays with people39 h
Mixing and Blending Machine Setters, Operators, and Tenders, now: of a 40-hour week, AI could take on 1 hours, 0 hours become checking its work, 0 hours are desk work it can't do yet, and 39 hours stay with people.
The largest pieces: Record production data (under an hour); Read work orders (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.
- 1%
- 2%
- 0%
- 1%
- 96%
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 3% 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, 2%. With Claude Mythos Preview (early), measured in April 2026, 3%. Today's estimate: 3%.
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, 0.011% went to this job's tasks in May 2026, the 389th largest of the 447 jobs that showed up at all; 1 of its 20 tasks showed up. Within each of Anthropic's methods, never across a change: 0.0059% to 0.0021% (released February and March 2025); 0.011% to 0.011% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.0059% | 588 of 708 | 1 of 20 |
| Released March 2025 | 0.0021% | 660 of 703 | 1 of 20 |
| The week of Aug 4, 2025 (released September 2025) | none | n/a | 0 of 20 |
| The week of Nov 13, 2025 (released January 2026) | none | n/a | 0 of 20 |
| The week of Feb 5, 2026 (released March 2026) | none | n/a | 0 of 20 |
| April 2026 (released June 2026) | 0.011% | 370 of 434 | 1 of 20 |
| May 2026 (released June 2026) | 0.011% | 389 of 447 | 1 of 20 |
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 89% of the working time, a robot has been shown doing most of the task somewhere: 71% in a purpose-built cell, 18% in a structured facility. 1.07 of 3, the 89th highest of the 923 jobs.
- Not physical 9%
- No robot shown 2%
- In a purpose-built cell 71%
- In a structured facility 18%
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
97% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Run mixing and blending machines, the hands-on work, 15%
- Compound ingredients to formula, the hands-on work, 10%
- Monitor production, the hands-on work, 9%
- Tend pumps and conveyors, the hands-on work, 7%
- Run mixing cycles, the hands-on work, 6%
- Clean and maintain equipment, the hands-on work, 6%
What becomes checking the machine
No desk work within reach by then in tasks where a mistake costs something. Another 3% is desk work that is small if wrong, which can be handed over with lighter checks.
What to learn
- The hands-on craft. About 96% of the time is hands-on work, which no model here does; robots are rated separately on this page.
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 . 20 tasks from .
| Task | ||||
|---|---|---|---|---|
| Operate or tend machines to mix or blend any of a wide variety of materials, such as spices, dough batter, tobacco, fruit juices, chemicals, livestock feed, food products, color pigments, or explosive ingredients.Machines operated and tended.Done daily by most who do it. Robots: in a purpose-built cell, Repete Batching Automation control systems (sold). Anthropic's rating. | 15% | 50 min17 min to 2.5 h | Hands-on 100% | Stays with people |
| Compound or process ingredients or dyes, according to formulas.Ingredients and dyes processed to the formula.Done daily by most who do it. Robots: in a purpose-built cell, AZO RoLog (sold). Anthropic's rating. | 10% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Observe production or monitor equipment to ensure safe and efficient operation.Watching the equipment for safe operation.Done daily by most who do it. Robots: in a structured facility, ANYmal (ANYbotics / W.R. Grace) (deployed). Anthropic's rating. | 9% | 25 min8 min to 1.3 h | Hands-on 100% | Stays with people |
| Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.Accessory equipment tended.Done daily by most who do it. Robots: in a purpose-built cell, Sonic TechBlend inline blending systems (sold). Anthropic's rating. | 7% | 25 min8 min to 1.3 h | Hands-on 100% | Stays with people |
| Mix or blend ingredients by starting machines and mixing for specified times.Machines started and run for the set time.Done daily by most who do it. Robots: in a purpose-built cell, Shaffer horizontal arm mixers / Gemini and Zeppelin automated mixing systems with PLC recipe management (sold). Anthropic's rating. | 6% | 21 min8 min to 50 min | Hands-on 100% | Stays with people |
| Clean and maintain equipment, using hand tools.Maintenance with hand tools.Done daily by most who do it. Robots: in a purpose-built cell, Rosie (R6) (CleanBotix) (sold). Anthropic's rating. | 6% | 38 min13 min to 1.7 h | Hands-on 100% | Stays with people |
| Test samples of materials or products to ensure compliance with specifications, using test equipment.Tests run with the equipment.Done daily by most who do it.Robots: in a purpose-built cell, HTR 3000 automated rheometer (Anton Paar) (sold). Anthropic's rating. | 6% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
| Weigh or measure materials, ingredients, or products to ensure conformance to requirements.Weighing to the requirements.Done daily by most who do it. Robots: in a purpose-built cell, APRIL Robotics Weighing System (OAL Group) (deployed). Anthropic's rating. | 5% | 13 min4 min to 25 min | Hands-on 100% | Stays with people |
| Clean work areas.Cleaning up.Done daily by most who do it. Robots: in a purpose-built cell, Avidbots Neo autonomous floor scrubber (deployed). Anthropic's rating. | 4% | 17 min8 min to 50 min | Hands-on 100% | Stays with people |
| Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.Machines stopped, valves opened and pumps started.Done daily by most who do it. Robots: in a purpose-built cell, Rockwell PlantPAx / FactoryTalk Batch (integrated by Grantek) (deployed). Anthropic's rating. | 4% | 13 min4 min to 29 min | Hands-on 100% | Stays with people |
| Unload mixtures into containers or onto conveyors for further processing.Mixtures unloaded into containers or onto conveyors.Done daily by most who do it. Robots: in a purpose-built cell, Carolina Material Technologies fully automatic bulk bag filling system (sold). Anthropic's rating. | 4% | 13 min4 min to 33 min | Hands-on 100% | Stays with people |
| Collect samples of materials or products for laboratory testing.Samples taken for the lab.Done daily by most who do it. Robots: in a purpose-built cell, Sentry ISOLOK M4KSA / MSA automatic point sampler (sold). Anthropic's rating. | 4% | 13 min4 min to 29 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.
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders5% now, 5% by 2028
- Food Batchmakers15% now, 17% by 2028
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders5% now, 5% by 2028
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders6% now, 6% by 2028
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders13% now, 13% by 2028
- Machine Feeders and Offbearers5% now, 5% 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 Mixing and Blending Machine Setters, Operators, and Tenders is within reach of AI now?
- An estimated 3% of the working time is within reach of AI models now and 3% 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 Mixing and Blending 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 production data (100% of the task's time now); and Read work orders (50% of the task's time now).
- Which tasks of Mixing and Blending 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: Run mixing and blending machines; Compound ingredients to formula; and Monitor production. Together such tasks are about 96% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Mixing and Blending Machine Setters, Operators, and Tenders 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. By the end of 2028 on the long-run pace at four in five, about 97% of the working time stays with people, 0% becomes checking a model's work and 3% 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 Mixing and Blending Machine Setters, Operators, and Tenders?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.011% went to this job's tasks, the 389th largest share of the 447 jobs that showed up; 1 of its 20 tasks appeared. 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 Mixing and Blending Machine Setters, Operators, and Tenders?
- 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 89% of the working time, 18% in a structured facility. Its index is 1.07 of 3. One example: ANYmal for "monitor production". 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 20 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.
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