Description

Spec-driven development with AI is getting a lot of attention. Peter and Dave ask when that structure actually helps you, and when it slows you down.

They start with the AI SDLC white paper Anthropic recently published. It lays out a structured, spec-driven approach built for consistency. Peter sees it working best on large legacy systems, the kind with millions of lines of code written over 15 years by thousands of people. For greenfield work, a looser approach of prototyping and exploring often fits better. Dave pushes on the piece most teams drop: the feedback loop. Everyone agrees to review and validate, and then the next shiny feature shows up. They also cover what happens when an AI agent follows an over-detailed spec too literally, why systems thinking beats chasing every edge case, and how explore versus exploit plays out differently for a startup and a company with 10 million customers.

This week’s takeaways:

• A structured, spec-driven SDLC pays off on large, complex systems where stability matters, but it can feel like far too much when you are just prototyping something new.
• Define enough up front to guide the work, then rely on regular check-ins and feedback loops to course-correct, because no spec can predict every case and an AI agent will happily take the shortcut you never meant to allow.
• Explore and exploit are not either/or. Organizations can now run both under one roof, as long as they know where they sit on the path from startup to established player and use judgment about how much structure to add.

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