Peter and Dave dig into a pattern they're both starting to see: organizations using AI-assisted development as a reason to bring back big upfront planning and large project releases. The logic makes a certain kind of sense. If AI can build faster, why not design bigger? But that reasoning skips the part that actually mattered when teams moved to product delivery in the first place: validating that you're building what customers actually need.
The conversation covers why large releases make it harder to learn what's working, why feature parity with competitors is a trap, and what "North Star context" actually means when you're coordinating AI agents. The core argument: the planning layer is back in vogue for good reason, but the delivery layer still needs to be small and iterative. Cheaper to build doesn't reduce business risk. It just makes it easier to build the wrong thing faster.
Chapters:
0:00 Welcome Back And The Big Question
1:00 AI And The Return Of Projects
4:40 Why Validation Still Matters
7:10 The Hidden Cost Of Big Releases
9:40 North Star Context And Outcome Metrics
13:40 Systems Thinking And Feedback Loops
15:50 Summary And Listener Call To Action
This week's takeaways:
AI augmentation speeds up building and releasing features, but it doesn't replace the need to validate whether those features are what customers actually want.
A big picture plan is useful as context for AI agents and delivery teams, but over-specifying every step upfront wastes time on details that will change anyway.
The goal isn't projects vs. product delivery. It's combining a clear long-term direction with small, measurable, iterative delivery tied to real outcome metrics.
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