As of August 2026,
Recycling Coordinators has an AI-exposure score of 64/100
(High exposure) on the AI-Safe Careers index. This is an estimate of task
exposure, not a prediction of job loss.
Score inputs for this role:
This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Anthropic Economic Index (June 26, 2026). BLS labor-market figures are separate context, not score inputs.
Recycling Coordinators
More exposed than 78% of the roles we track.
Will AI replace Recycling Coordinators?
No exposure score can predict whether AI will replace this role. The 64/100 score means our current model estimates high task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.
Early-career context (study ages 22-25)
This role's High score and automation-heavy task mix point in the same direction as the study's highest-exposure automation cohort. The measures are not identical, so the study result is context rather than a direct forecast for this role.
The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.
Use the task map to identify durable work and the skills worth building early.
Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision
This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.
How this role compares to similar Transportation roles
What this role usually involves
Supervise curbside and drop-off recycling programs for municipal governments or private firms.
Broad guidance for this preparation level; exact requirements vary by role and employer.
Source: O*NET 29.1 exact occupation - Recycling Coordinators, SOC 53-1042.01. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.
Recycling Coordinators tasks, by AI exposure
- Provide training to recycling technicians or community service workers on topics such as safety, solid waste processing, or general recycling operations.
- Prepare bills of lading, statements of shipping records, or customer receipts related to recycling or hazardous material services.
- Operate recycling processing equipment, such as sorters, balers, crushers, and granulators to sort and process materials.
- Develop community or corporate recycling plans and goals to minimize waste and conform to resource constraints.
- Supervise recycling technicians, community service workers, or other recycling operations employees or volunteers.
No durable tasks identified for this role - its individually-assessed tasks split 75% automatable / 25% augmentable.
We analyzed all 20 Recycling Coordinators tasks - 15 automatable and 5 augmentable. The full task map - every task with exactly what to do about each - is in your Career Report.
Your report unlocks three concrete artifacts
Every task scored with what to automate, augment, or protect.
Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.
A keepable roadmap plus resume and LinkedIn repositioning.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.
Adjacent career paths
Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.
Provide training to recycling technicians or community service workers on topics such as safety, solid waste processing, or general recycling operations.
Your AI-Safe Career Report
Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.
AI was the most-cited reason for U.S. layoffs through mid-2026 - the workers who adapt earliest fare best. - Challenger, Gray & Christmas, 2026The upside: Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026
Generation starts after checkout; reports are typically ready within a few minutes.