Source URL: https://simonwillison.net/2025/Sep/22/workslop/
Source: Simon Willison’s Weblog
Title: Quoting Kate Niederhoffer, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano and Jeffrey T. Hancock
Feedly Summary: We define workslop as AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.
Here’s how this happens. As AI tools become more accessible, workers are increasingly able to quickly produce polished output: well-formatted slides, long, structured reports, seemingly articulate summaries of academic papers by non-experts, and usable code. But while some employees are using this ability to polish good work, others use it to create content that is actually unhelpful, incomplete, or missing crucial context about the project at hand. The insidious effect of workslop is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver.
— Kate Niederhoffer, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano and Jeffrey T. Hancock, Harvard Business Review
Tags: productivity, ai-ethics, generative-ai, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the concept of “workslop,” which refers to AI-generated content that appears polished but lacks substantive value, potentially hindering productivity. It highlights the challenges associated with the proliferation of AI tools in workplaces, particularly how they can lead to ineffective output and increased workload for recipients.
Detailed Description:
The article addresses the growing concern regarding the use of AI tools in content creation and its impact on workplace productivity. It introduces the term “workslop” to describe AI-generated content that may look professional but does not contribute meaningfully to the tasks at hand.
Key insights include:
– **Definition of Workslop**: AI-generated work that lacks depth and fails to advance project goals.
– **Access to AI Tools**: With the increase in accessibility of AI tools, employees can produce visually appealing outputs such as:
– Well-formatted presentations
– Structured reports
– Articulate summaries of complex texts
– Code that appears functional but may not be effective
– **Dual Use of AI**: While some individuals leverage these tools to enhance their work quality, others produce content that is superficial and impractical.
– **Impact on Workload**: The creation of workslop shifts the responsibility of ensuring quality from the creator to the receiver, necessitating additional effort to interpret or revise the original content.
This text is particularly relevant to professionals in AI, as it sheds light on the ethical implications and challenges of using generative AI in the workplace, as well as its potential effects on efficiency and collaboration. It prompts a discussion about the responsibilities of AI creators and users in ensuring that content produced is not only polished but also meaningful and valuable.