AI-based and LLM-based systems are often cast as a threat to software engineering: coding agents grow more capable, junior roles shrink or disappear, and end-user programming appears ready to bypass experts altogether. This talk argues for a different conclusion. The deeper shift is not mainly about tools or tasks, but about organizational operating logic: how decisions are made, how knowledge moves, how work is coordinated, and how value is created. As these systems become more agentic, they will not merely support that shift; they will increasingly help analyze, adapt, and redesign it.

This change does not diminish the importance of software engineering. It broadens it. More of society will come to depend on semi-executable systems built from natural language, code, tools, policies, and workflows, while agentic environments increasingly generate, coordinate, and evolve many of those components. I take seriously concerns about reliability, maintainability, organizational inertia, and power. But these are better seen as constraints on the route than as arguments against the direction itself. Even imperfect AI can be transformative when deployed continuously at scale, and the central activities of software engineering are more likely to be redefined than replaced. For researchers, this creates a real risk that some AI4SE work targets practices already being folded into agentic workflows. For practitioners, it means that engineering discipline becomes more important, not less, as solution creation extends beyond traditional software developers.