AI exposure Production
Will AI replace mixing and blending machine setters?
Mixing and Blending Machine Setters, Operators, and Tenders. US occupation 51-9023.
The reading
0.0%
of the time mixing and blending machine setters spend on their work goes on tasks where AI is already being used to do that work.of mixing and blending machine setters' working hours go on tasks AI has already started taking off their plate.
Ranked 346 of 756 US occupations. No AI use seen in the work yet, along with 410 other occupations.No handover started yet, along with 410 other occupations.
Per cent of working time Square-law scale
Average US job 7.7%
Among the least exposed jobs there are.Still waiting for liberation.
What AI already does for mixing and blending machine settersWhat mixing and blending machine setters no longer do alone
O*NET lists 20 tasks for mixing and blending machine setters. AI use at work shows up in 1 of them, though too little of the job's time goes on them to register in the exposure figure. Each score runs from 0.5, where every observed use helps a person with the task, to 1, where every use does the task outright.
- Record operational or production data on specified forms.Core task mostly automated 1.00
What it does not touch, yetWhat still needs a human
19 of the 20 tasks show no AI use at work in the data.
- Add or mix chemicals or ingredients for processing, using hand tools or other devices.Core task
- Clean and maintain equipment, using hand tools.Core task
- Clean work areas.Core task
- Collect samples of materials or products for laboratory testing.Core task
- Compound or process ingredients or dyes, according to formulas.Core task
- Dislodge and clear jammed materials or other items from machinery or equipment, using hand tools.Core task
Show the other 13
- Dump or pour specified amounts of materials into machinery or equipment.Core task
- Examine materials, ingredients, or products visually or with hands to ensure conformance to established standards.Core task
- Mix or blend ingredients by starting machines and mixing for specified times.Core task
- Observe production or monitor equipment to ensure safe and efficient operation.Core 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.Core task
- Read work orders to determine production specifications or information.Core task
- Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.Core task
- Tend accessory equipment, such as pumps or conveyors, to move materials or ingredients through production processes.Core task
- Transfer materials, supplies, or products between work areas, using moving equipment or hand tools.Core task
- Unload mixtures into containers or onto conveyors for further processing.Core task
- Weigh or measure materials, ingredients, or products to ensure conformance to requirements.Core task
- Open valves to drain slurry from mixers into storage tanks.
- Test samples of materials or products to ensure compliance with specifications, using test equipment.
How that compares
Among the 98 production occupations, mixing and blending machine setters rank 60. The group averages 0.8%, and its most exposed job is Computer Numerically Controlled Tool Programmers at 12.1%. The average US occupation sits at 7.7%.
Level with 0.0%
What this does and does not mean
Exposure is observed use, not a forecast. Anthropic maps work conversations with Claude onto the tasks O*NET lists for each job and asks, of the tasks AI could speed up, which are actually being used for at work. A use that does the task outright counts in full and a use that assists a person counts half. The task scores are averaged into the occupation figure by the share of time spent on each task.
A high figure means AI is already part of how this work gets done. It does not mean anyone has been replaced. Anthropic's own reading of the labour market finds no systematic rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting exposed jobs.No measurable rise in unemployment among exposed workers so far, and about 14% fewer 22 to 25 year olds starting these jobs. Gap years for everyone. Labor market impacts, 5 March 2026.
Task list and titles from O*NET 31.0 by the US Department of Labor, Employment and Training Administration, used under the CC BY 4.0 licence. Scores from the Anthropic Economic Index (CC BY). Checked 1 November 2026. How the ranking works.