{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/fa6f1e035d46432e9d82496cd29fd5da\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/fa6f1e035d46432e9d82496cd29fd5da-052703a9ec0831df-full.jpg","duration":1366.16,"title":"Recursive AI Self Improvement and Regulation","description":"This Loom discusses recursive self-improvement in AI and why government regulation may not slow development. The author describes AI systems improving themselves by saving changes at scale, potentially involving many connected agents, and compares this to the human brain because people often do not understand how thoughts arise. They argue regulation is unlikely due to competitive pressure, including a race with China and the concern that slowing down could cede advantage. The video also emphasizes bias in AI compared with doctors, uncertainty about privacy, and the need to hear perspectives from people building in the trenches rather than only public figureheads."}