Why AI apps fail in production (And how Google solved it)

AI apps often fail in production due to unpredictable behavior in enterprise environments. This is because local prototypes don't account for corporate networks, errors, and leadership concerns. Only 5% of AI prototypes make it to production, while the rest fall into validation loops. To bridge the speed-risk paradox, YouTube's AI prototyping stack decouples rapid development from production infrastructure, allowing for faster prototyping without introducing systemic risk. Engineers should consider adopting a similar approach to improve their AI software development lifecycle.