{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/bca91879dd884dfab9ec2d2ce817e8ee\" frameborder=\"0\" width=\"1706\" height=\"1280\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1280,"width":1706,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1280,"thumbnail_width":1706,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/bca91879dd884dfab9ec2d2ce817e8ee-642f176041d40e61.gif","duration":76.8,"title":"Accountability for AI-Coded Work","description":"This Loom discusses how to build AI era tech teams where individual contributors can take accountability for code they did not personally write. Brian argues that today’s ICs often think accountability means knowing and communicating line by line, but that is not how responsibility historically worked. He notes that tech managers typically do not know every line written by employees and instead rely on trust, proxy measurements, outcome-based review, and focus on high value code areas. His point is that ICs should adopt similar approaches when AI coding tools like Cloud Code change how code is produced."}