workslop
In plain wordsAI-generated work that looks polished but does not actually get the job done, quietly passing the real effort to whoever receives it, who must then puzzle it out or start over.
Material produced with generative AIgenerative AI, n.: AI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like. in the workplace (memos, reports, slide decks, code, emails) whose professional finish hides a lack of real content, so that the labor of making it usable falls on the person who receives it rather than on the one who sent it. In the survey that introduced the term, roughly 40% of US desk workers said they had received such material recently; recipients spent nearly two hours dealing with each instance and reported thinking less of the sender's competence and reliability. The authors estimated the hidden cost at about US$186 per employee per month and argued that blanket mandates to use AI encourage it.
The quarterly review was pure workslop: twelve tidy pages and not one number we could use.
Origin
Coined by Kate Niederhoffer, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano and Jeffrey T. Hancock of BetterUp Labs and the Stanford Social Media Lab in a Harvard Business Review article published on 22 September 2025. A blend of 'work' and 'slop', it spread quickly through business media.
Usage note
Unlike AI slopAI slop, n.: Low-quality content churned out in bulk by AI — bland articles, fake-looking images, filler videos — posted to grab clicks or fill space, with little care for accuracy or value., which is published to attract attention, workslop circulates inside organizations and its cost falls on colleagues. The figures come from a self-reported online survey.
Related
- AI slopAI slop, n.: Low-quality content churned out in bulk by AI — bland articles, fake-looking images, filler videos — posted to grab clicks or fill space, with little care for accuracy or value.
- overrelianceoverreliance, n.: Leaning on an AI system more than its reliability justifies: accepting its answers without checking, or letting it make decisions it often gets wrong.
- automation biasautomation bias, n.: The habit of trusting a computer's suggestion too much: following it even when it is wrong, or missing problems because the machine did not flag them.
- augmentationaugmentation, n.: Using AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own.
- AI hypeAI hype, n.: Exaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers.
- generative AIgenerative AI, n.: AI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like.
Sources
- Niederhoffer, K., Rosen Kellerman, G., Lee, A., Liebscher, A., Rapuano, K. & Hancock, J. T. (2025). AI-Generated "Workslop" Is Destroying Productivity. Harvard Business Review, 22 September 2025. hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity
Mentioned in
AI slopAI slop, n.: Low-quality content churned out in bulk by AI — bland articles, fake-looking images, filler videos — posted to grab clicks or fill space, with little care for accuracy or value., productivity paradoxproductivity paradox, n.: The puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce., botshitbotshit, n.: A blunt term for false or made-up chatbot output that people pass on and use as if it were true, especially at work., cognitive debtcognitive debt, n.: The idea that letting AI do your thinking or writing saves effort now but weakens your own thinking, memory and learning over time., tokenmaxxingtokenmaxxing, n.: Using as much AI as possible — huge prompts, long sessions, many agents at once — mainly to rank high on workplace measures of how much AI people use.