I share the author's sentiment. I also think it's important to fully understand a codebase I own. So much is naturally lost when you let LLMs generate code for you, and the mental model of what the added code does is one of the biggest losses. When you write code by hand, you build that model as you go, and it's enormously helpful later - when adding a feature, or when debugging behavior you didn't expect.

I've been trying to address this by telling LLMs primarily how the code should be structured, rather than only what it should do. Still, any design I hand to an LLM will be underspecified in one way or another (if it were fully specified, it would just be code), and the LLM fills those gaps somehow - which adds to the cognitive debt, slowly but surely.

Retyping LLM-generated code is an interesting solution. You'd certainly end up understanding the generated code better than if you merely reviewed it, but I doubt it produces a mental model as reliable as the one you'd build writing the code yourself. The longer you think, the better your mental model gets - and outsourcing the thinking to an LLM means thinking less.

That said, I've started to wonder whether I'm solving the wrong problem. Should I really insist on an accurate mental model of the code I own? We'd find it strange for a non-engineering manager to try to fully understand every piece of code their reports produce. If that's the right analogy, then as LLMs' agentic capabilities improve, maybe we should stop treating LLMs as tools that boost our own productivity as a software engineer and start treating them as independent agents we manage and steer.