For years, software engineering has largely been optimized around one thing: producing code.
Better frameworks, better abstractions, better tooling, better infrastructure. Every wave of innovation pushed engineers toward higher leverage. We moved from assembly to high-level languages, from monoliths to cloud platforms, from manual provisioning to infrastructure as code.
Now AI is accelerating this trend dramatically.
But contrary to popular narratives, AI is not reducing the need for engineers. It is changing where engineering value lives.
As implementation becomes cheaper and increasingly automated, the bottleneck is shifting elsewhere: understanding systems, navigating ambiguity, integrating with real-world workflows, and orchestrating increasingly complex socio-technical environments.
