Picture someone skimming a 40-page privacy notice just to read an article. That's what mandatory AI-training disclosure becomes: noise, not knowledge. The other side is right that secrecy breeds distrust, but "deserve to know" overpromises. Training often uses aggregated data where no individual contribution is meaningful, and notification fatigue means real risks get buried. Users need enforceable protections, deletion rights and opt-outs for sensitive uses, not a checkbox saying your data helped train a model. Transparency about harm, yes. A blanket right to know every training use? No.

Picture a weather app slapping a "your data may train AI" banner on every launch. By week two, you're tapping through it blind, same as cookie popups. That's the real-world failure mode. My opponent's fitness-app example is fair, and the creepiness is real. But the fix isn't a universal label; it's an opt-out and deletion right for sensitive uses. Labels overwhelm the people they're meant to protect, while companies check a box and call it transparency. Knowing training happened rarely tells you whether it harmed you. Enforceable controls beat blanket disclosure.

Look at cookie banners: everyone clicks accept without reading, and tracking continues anyway. That's the real-world fate of a blanket AI-training label. My opponent is right that a locked door needs a sign, but the better fix is unlocking the door by default: ban training on sensitive data absent explicit opt-in, and guarantee deletion rights. Then people don't have to parse a label on every app. A generic "may train models" tag rarely tells someone whether their data actually mattered. Enforceable defaults beat notification noise.