Why do teams keep losing context, and why hasn't any tool fixed it?

A developer argues that the real bottleneck in software engineering is not AI code generation but the loss of context and reasoning behind system decisions, leading to inconsistent sprints, slow onboarding, and architecturally wrong AI suggestions. The post asks whether any tool has successfully solved context continuity on real engineering teams.

Requirements in Confluence. Architecture decisions in someone's head or a six monthbold Notion page. Code in Git. Slack threads nobody searches. And a new developer joining who has to piece all of it together from scratch every single time. We talk a lot about building smarter with AI, but the actual bottleneck isn't code generation. It's that by the time AI touches anything, half the reasoning behind the system is already gone. It generates against the "what" while the "why" has completely evaporated. The result: inconsistent decisions across sprints, onboarding that takes weeks instead of days, and AI suggestions that are locally correct but architecturally wrong. Has anyone actually solved context continuity on a real engineering team? Curious what's worked or what's failed spectacularly. Comments URL: https://news.ycombinator.com/item?id=48742163 https://news.ycombinator.com/item?id=48742163 Points: 1 Comments: 0