There’s a new workplace jargon floating around: AI workslop. Coined by Stanford researchers, it captures a growing frustration with AI-generated content that looks polished at first glance but falls apart upon second look.
In this episode of In The Loop, I unpack what “ AI workslop” really means, its cost to organizations, and most importantly, question whether it's a tooling issue or a human problem.
We’ll explore how the so-called “efficiency trap” is lowering quality standards at work, how overconfidence in AI training can backfire, and why domain expertise matters more than ever. Plus, I’ll share four ways to fix this problem so we can all spend more time producing work we’re actually proud of.
⏭️ Episode Highlights
(00:55) – What “AI workslop” actually is and why it’s becoming everyone’s problem
(05:30) – The “efficiency trap” and the four stages of AI workshop evolution
(10:25) – Centaur vs. cyborg approach: what good AI use really looks like
(11:00) – Four ways to fix the AI workslop problem in your team
(14:40) – Closing thoughts: Setting expectations instead of blaming tools
🔗 Links & Resources
Episode transcript with more resources on the Mindset AI blog
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Jack Houghton
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