{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Innovation to Impact: Drug Development, AI, and Regulatory Strategy","title":"Innovation That Earns Deletion (Subtractive Trust)","html":"","width":"100%","height":180,"duration":1993,"description":"Drug development workflows only get heavier, and subtractive change requires predictivity and decision-grade evidence that can survive regulatory science scrutiny. In this episode we talk about how AI and translational science can earn the right to delete steps, not decorate them. The tension is uncomfortable: are you willing to remove something you have always done, or will you just add another layer and call it progress? We introduce the idea of a Predictivity Ledger, a simple way to make miss rates visible and force clarity about what is being claimed, in what context, and with what failure modes. Deletion is not a vibe. It is a governance decision with receipts. Takeaway: pick one legacy step, define deletion criteria, and start logging misses like they cost time and money, because they do.  If you liked this episode, steal the monthly cheat sheet at Innovation2Impact Newsletter (we do the digging, you keep the credit). ","thumbnail_url":"https://img.transistorcdn.com/Y1nZWqMkxS4Ee17tcD9y_wgj4JgHPHGNBpdmmK5mKz0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wZTcz/OTlhZDEzZTZhODdi/ZDM5OGU2MTdiYWEx/YTBmNy5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}