AI-assisted programming can automate boilerplate, syntax recall, unfamiliar-library exploration and other routine implementation work, but software engineers still need to understand the systems they maintain. The source argues that code ownership has never depended on personally writing every line: developers routinely use documentation, internet searches, Stack Overflow examples, colleagues’ code and inherited services. Ownership instead depends on knowing a system’s behavior, boundaries, dependencies and failure modes. The source distinguishes delegating typing from delegating understanding. A developer who defines the required behavior and asks an AI to implement it retains control, while a developer who repeatedly submits errors and applies generated patches without understanding them may produce working software without a mental model of it. The source says debugging builds that model by requiring engineers to compare expected and observed behavior and trace where they diverge. The source warns that this risk may be greater for junior developers, whose experience traditionally develops through difficult debugging sessions. It recommends using AI aggressively for tedious coding tasks while keeping architectural decisions, system design and explanation of component interactions under human control. It predicts that as code generation becomes cheaper, architecture, integration, distributed systems, observability, failure modes, boundaries and trade-offs will become more important differentiators for developers.