Software fundamentals are back, because AI makes them harder to skip

The thread on 'Software Engineering fundamentals matter more' was pointed and practical. The core argument: AI-generated code makes the consequences of bad fundamentals worse, not better. When a model generates code, the architectural decisions it makes are often incoherent, and debugging or extending that code requires a human who understands the underlying concepts deeply. One commenter put it plainly: the model 'often gets stuck and doubles down on the wrong path' when reasoning about architecture.

This is a direct counterweight to the 'AI will replace developers' narrative. The thread argued that knowing how LLMs predict versus reason is not just semantics, it is operationally important for knowing when to trust the output and when to intervene. The working-memory thread reinforced this from the other direction: yes, AI can hold more in context, but humans still need to understand what it is doing to steer it.

The implication is that the junior developer who relies entirely on AI-generated code without understanding data structures, state management, or interface design is going to produce increasingly broken systems as the complexity of those systems grows.