AI made engineers faster. It did not make companies faster at deciding what to build. That is now the hard part.
Mike Lyons and Greg Pfister of KaiRise join Oscar and Matt. The conversation starts with a $4.3 million voter registration system that shipped on time, on budget, and on scope. Users still rejected it. The team built what was requested, but not what people needed.
AI makes that risk bigger. Teams can produce more software in less time. Leaders still need to set direction. Product teams still need to understand customers. Someone still has to decide when to stop.
What we cover with Mike Lyons and Greg Pfister.
If writing the code was never the whole job, making it faster does not make the company faster. The slow part moved to everything around engineering — direction, decisions, approvals.
Annual budgets and long approval chains were designed for teams that delivered slowly. When delivery compresses, the planning cycle becomes the constraint.
Formal Agile roles are in decline. The underlying principles — short feedback loops, working software, real customer contact — did not stop mattering.
A live prototype settles in one meeting what a requirements document argues about for a month. Building got cheap enough to make showing the default.
Estimation assumed human hours. Points, lines of code, and token usage all measure output, and none of them prove a customer got value.
The scarce ability after AI is not producing more. It is recognizing the point where more code stops improving the outcome.
Unclear ownership, misaligned incentives, and teams shipping features nobody asked for are structural. No model fixes them.