AI can now produce output faster than most organizations can absorb it. Mik Kersten explains why the move from output to outcome has become a leadership job.

Mik, author of Project to Product and his new book Output to Outcome, joins Peter and Dave to talk about what happens when coding stops being the bottleneck. For years, business teams fought to get their feature into the next plan because delivery capacity was scarce. With agents taking that constraint away, the bottleneck moves. It might be customer adoption, security reviews, budget, or how long it takes to get real feedback. Mik walks through the outcome loop, which connects strategy and budget to outputs and then to customer and business outcomes, and explains how to apply the theory of constraints to it. He also covers the outcome tree, a way of cascading company-level targets down to the value streams doing the work. Peter and Dave push on the hard parts: business leaders who can’t name their top outcome, B2B teams that wait months for usable feedback, and operating models that fall apart once the leader who championed them moves on.

This week’s takeaways:

• When AI removes coding as the constraint, the bottleneck shows up somewhere else, so measure the whole loop from budget to customer outcome and go find it.
• If a team can’t connect its work to a company-level outcome, the strategy was never decomposed, and moving faster only makes that gap more obvious.
• Outcome ownership needs to sit with the teams closest to the work, with the business at the table, and the operating model has to outlast any single leader.

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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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AI agents will tell you everything works. Here is how to validate AI output when the mistakes are ones no human would ever make.

Peter and Dave compare notes from building software, training material, and presentations with AI. The problems they keep running into are strange ones: the wrong database ID dropped into a system, 236 passing tests that test nothing, and five layers of abstraction added to a solution that needed none of them. Their answer borrows from good agile practice. Define what validation means before the agent starts, the same way a team agrees on its definition of done. Keep checking that the context you gave the AI is still relevant as the work moves. And keep someone in the loop who knows the problem well enough to look at the result and say, "Actually, you got this wrong."

This week's takeaways:
- Decide what "done" and "valid" mean before you hand work to an AI agent, and use a separate agent to grade what comes back.
- A test suite that pings components or mocks everything out can report hundreds of passes and prove nothing, so ask for synthetic transactions that run through the whole system.
- AI will happily over-engineer a small problem, so someone needs enough knowledge to ask whether the solution fits the size and scale of what you are actually trying to solve.

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