Enterprise 'Workslop' Detection

When a colleague sends you four paragraphs of AI-generated text dressed up as a human communication, reading it costs you real time. Writing it cost them almost nothing. This asymmetry is getting worse as models improve and output becomes harder to distinguish from genuine writing. Teams are already drowning in AI-generated status updates, summaries, and replies that carry little real information.

The right time to build this is now because the problem is new enough that few organizations have policies around it, but common enough that people already have a name for it. A browser extension or email client plugin that scores incoming messages for likely AI generation and flags them for review would reduce the cost of reading low-effort output.

A starting point would be to train a lightweight classifier on pairs of human-written and AI-generated communications in common professional contexts: status updates, meeting summaries, code review comments. The extension does not need to be perfect. Even a 70% accuracy score creates enough friction that senders think twice before forwarding raw model output.