Rebuilding the Prioritization Filter Lost with AI-Assisted Coding
This article discusses the loss of a natural prioritization filter when using AI-powered coding assistants, and provides a framework to deliberately rebuild that filter.
Why it matters
As AI-assisted coding becomes more prevalent, teams need to proactively rebuild the prioritization filters they lost, to ensure quality and stability don't suffer from the increased development velocity.
Key Points
- 1AI-assisted coding removes the implementation cost friction that previously acted as a natural filter on the backlog
- 2Research shows AI adoption leads to increased software delivery throughput but decreased delivery stability
- 3A framework of 4 key questions can help teams prioritize AI-generated code and maintain quality control
- 4Developers are generating code faster than they can assimilate it, leading to a gap between usage and trust
Details
Before AI coding assistants, the cost of implementation acted as a natural filter on a team's backlog - high-cost features needed stronger justification, while low-cost ones still had some friction. However, with 'vibe coding', this friction disappears, leading to more features being shipped faster but with increased instability. Research shows 66% of developers find 'almost right but not quite' AI solutions frustrating, and 45% say debugging AI-generated code is more time-consuming than expected. To address this, the article proposes a framework of 4 key questions to deliberately prioritize AI-generated code: 1) Would I build this if it took 2 weeks? 2) Who specifically asked for this? 3) What breaks if this doesn't exist? 4) What's the rollback plan? This helps teams maintain a user-centric focus and delivery stability, rather than just shipping more features faster.
No comments yet
Be the first to comment