{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"[Dev]olution","title":"AI Is Writing More Code Than Humans Can Review","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/7e1e0786\"></iframe>","width":"100%","height":180,"duration":2938,"description":"\nWhat happens when the AI coder moves faster than the review process can handle?\n\nNnenna Ndukwe has spent 8+ years as a software engineer and now works at the intersection of AI, developer relations, and enterprise engineering strategy. As AI developer relations lead at Qodo, she spends her time helping teams separate useful AI adoption from expensive chaos.\n\nIn this episode of [Dev]olution, Nnenna joins Nicky Pike to talk about the part of AI coding most teams skip: review, verification, governance, and the outer loop where AI-generated code either gets controlled or breaks production.\n\nThey dig into why AI does not fix broken engineering systems, how rework rate exposes the truth behind AI productivity, and why better code review needs more than another stream of AI comments. What it needs, instead, are deterministic gates, developer trust, and a clear view of where AI belongs in the software delivery process.\n\nIf your team is shipping faster but fixing more, this episode will make you rethink what “AI productivity” actually means.\n\nIn this episode, you’ll learn:\n\nWhy rework rate may expose AI productivity better than velocity\nHow deterministic gates keep AI review from becoming noise\nWhere engineering leaders should introduce AI first\n\nThings to listen for:\n(00:00) Meet Nnenna Ndukwe\n(01:52) From writing code to advising leaders\n(04:09) Why AI made the work more interesting\n(07:02) Paying it forward in women in tech\n(09:23) AI speed is breaking production\n(10:30) Where the delivery jam really happens\n(12:32) Why rework rate tells the truth\n(15:42) Can AI review AI code\n(17:21) AI amplifies your current process\n(20:34) The risk of AI review slop\n(23:19) Why acceptance rate matters\n(26:27) Human-agent work needs better rules\n(29:23) More tokens is not discipline\n(36:44) Where leaders should start\n(41:25) Predictions: More managing systems\n(45:49) Defining a Coder: A problem-solver\n(47:08) Final thoughts: Cut the noise of AI hype\n\nResources:\n\nNnenna Ndukwe’s...","thumbnail_url":"https://img.transistorcdn.com/NGioKOB49N-k877AC-twbJMVPLxekfS0gRkeRbVCBog/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81MTRi/MGJkNDYxN2ZlY2Rm/ODM2MjQyYjJmNGEy/NTY1Ny5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}