AI slop is everywhere. 69% of AI users admit to shipping work they haven’t verified, don’t fully understand, or couldn’t confidently defend, according to @glean's Work AI Index. And even when AI output is factually correct, it can still be generic, too long, or clearly not written by a human. The deeper problem is what researchers call “mental proof”: visible evidence that someone engaged with the work, cares about the result, and understands the person on the other end. When work feels like slop, it erodes trust and relationships. This is hard to solve. At Glean, we’re still figuring out how to keep AI from making our internal communication longer, blander, and less human. A few principles guide us: - 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗮 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗿𝗶𝗰𝗵 𝗔𝗜 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺. AI can help protect against slop when it understands the context around the work. It should match the length of a communication to its complexity. When communication involves a sensitive relationship, ambiguity, or higher stakes, it should recognize that more human judgment may be needed and prompt the user to pause or add humanity. - 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗔𝗜 𝘂𝘀𝗲, 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗶𝘁 𝘀𝗮𝗳𝗲 𝘁𝗼 𝗽𝘂𝘀𝗵 𝗯𝗮𝗰𝗸. I try to model this myself, and push my team to do the same by saying something like: “This is too long. Please rewrite it more concisely.”  - 𝗧𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘄𝗵𝗼 𝘀𝗵𝗶𝗽𝘀 𝘁𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲 𝗺𝘂𝘀𝘁 𝗿𝗲𝗺𝗮𝗶𝗻 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗶𝘁. Otherwise, it’s too easy to deflect responsibility to the AI. - 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. If you measure vanity metrics like output volume instead of quality and outcomes, you’ll optimize for more output, even when that output creates more work for everyone else. More AI output does not create more value if people spend their time sorting through slop.