Here’s a job the computers can take without much complaint: sorting recyclables. For humans, it is a foul, laborious job that entails standing over a conveyor belt, plucking beer cans and detergent bottles from a stream of refuse. The job pays little and is hard to fill.
At one recycling facility near Hartford, machines are taking over the dirtiest jobs, reports The Wall Street Journal (Jan. 8, 2026). A few workers remain on the line, mostly to watch for hazardous items. Otherwise, the system of conveyors, magnets, optical sorters and pneumatic blocks runs largely unmanned. The technology allows them to sort up to 60 tons an hour of curbside recycling into precisely sorted bales of paper, plastic, aluminum cans and other materials. The material is sold to mills, manufacturers and remelt facilities, which pay more for cleaner bales.

Watching over it all are computers that analyze material as it passes by at 7 mph. The devices use AI to identify recyclables, flag food-grade material, gauge items’ mass, assess market value and calculate points at which a robotic claw might best clasp each piece.
The U.S. 50% aluminum tariff has lifted demand for scrap metal, while pulp mill closures have left box makers more reliant than ever on old corrugated containers. And consumer goods companies want to reclaim their bottles as states adopt extended producer responsibility laws aimed at reducing plastic pollution.
Part of the problem: Americans’ poor recycling habits are an obstacle to profit. A lot of beer cans and delivery boxes never even make it to sorting centers. A study in Virginia’s waste stream showed that 28% was recyclable, yet the system was stuck at a recycling rate of about 7% no matter how much it spent trying to teach people how and what to recycle.
The big breakthrough in recycling technology has been combining vision recognition systems with pneumatic blocks. Using puffs of air to separate items has proved much faster and more accurate than robotic pickers, which are limited to about 40 items a minute, compared with thousands for pneumatic system.
Classroom discussion questions:
- Why has recycling been so inefficient?
- Should job loss through automation be a concern?
Prof. Jon Jackson is Associate Professor of OM at Providence College. He created AI classroom exercises–appearing in our Instructors Resource Manual– for every chapter of the text.
A common fear associated with automation is job loss. Sharpie’s story shows an alternative path. Rather than cutting staff, Newell retrained workers for higher-skill roles such as robotics support, maintenance, and technical troubleshooting. These new roles led to average wages increasing by nearly 50%.











