video1.0<iframe src="https://www.loom.com/embed/ba08d864ed9a4a38b9477ecfa213acef" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/ba08d864ed9a4a38b9477ecfa213acef-2af9b575715e9a06.gif1545.654This Loom explains why managing an AI product is fundamentally different from managing a traditional one. It argues AI products are probabilistic, so you ship ranges of behaviors and must govern that spread before launch, shifting the definition of done from building features to achieving reliable outcomes through evaluation. The role changes to co-owning trade-offs with engineers and using new skills such as data intuition, evaluation design, and prompt fluency, including planning for the unhappy cases. It emphasizes the data flywheel, where usage generates data that improves models over time, and illustrates with GitHub Copilot, noting that its key metric is how often suggestions are kept.