Software engineering is undergoing its most radical shift since the move to Cloud. As AI assistants evolve from writing simple snippets to suggesting entire system architectures, the line between human-authored and machine-generated code is blurring. However, this newfound speed introduces a new breed of technical debt and ethical risk. How do we evolve our day-to-day practices without losing our grip on security, maintainability, and accountability?
This session will explore the transition from Human-Driven to AI-Driven Engineering. We will ask the hard questions and discuss practical strategies for: how to manage "synthetic debt," redefine peer reviews for AI-generated code, and ensure that 10x velocity doesn't bypass safety.
We will explore a series of community-driven practices designed to maintain quality while navigating the move from "experimental" to "operational" AI.