We were promised a productivity revolution: ask AI for the code, accept the suggestion, move faster.

But software engineering has never been measured by how quickly code appears on a screen.

As AI takes over more implementation work, the bottleneck can move somewhere else-to reviewing generated code, debugging “almost-right” solutions, validating architecture, maintaining systems and understanding decisions nobody remembers making.

This session explores that shift through the practical realities of AI-assisted development. We'll look at what developers should measure when AI becomes part of the workflow, which tasks benefit from automation, where human judgement remains critical, and how teams can avoid confusing “more code produced” with “more value delivered.”

We'll also examine the emerging evidence around AI and developer productivity, including the uncomfortable trade-offs reported by both the developer community and software-delivery research.

The takeaway isn't “AI is bad” or “AI is the future.” It's a more useful question: if AI changes the cost of writing code, how should we change the way we engineer software?