What AI coding costs you

I recently accepted-ish a position at a very ai-forward company. Manual programming was somewhat discouraged entirely.

I've used AI tools in the past for maths I didn't understand or errors I couldn't make sense of, and wrote the bulk myself, but now we have as mentioned, opus/sonnet 4.5- which work great.

As part of this, I had to integrate two new apis- nornally, when I write an API wrapper I end up learning a lot about how the API feels, what leads to what and how it smells, etc. This time? I just asked Claude to read it's docs, then gave suggestions about how I wanted it to be laid out. As a result? I have no idea how these apis feel, their models, etc. If I want to interact with them, I ask Claude how I do a thing with the library it made.

Mind you, the library is good. I looked over everything, it's fairly thin and it's exactly how I would write it, as I suggested it do. But I have no deep understanding, much less an understanding of how it got integrated in.

Like, normally when I integrate something in I learn a bit about the codebase I'm integrating it into. Do that enough times, and I understand the codebase at depth, how things plug in. This time? Nada.

It's.... Deeply uncomfortable, to know so little but still be able to do so much. It doesn't matter if I get it to explain it, that's just information that washes off when I move onto the next thing. The reflexive memory isn't built.

All of which is to say, I agree with the article.

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In the section about Cognitive Debt the author wrote...

Apply this directly to fully agentic coding. If you stop writing code and only review AI output, your ability to reason about code atrophies. Slowly, invisibly, but inevitably. You can’t deeply review what you can no longer deeply understand.

I think this argument is flawed. On every team I've worked with we've always had the opinion that junior developers learn a lot about coding by reading and reviewing code written by other people, especially people more senior to them. Reviewing output doesn't weaken your skills, it improves them. Reviewing code in a large codebase forces you to explore and understand the paths that data takes. It pushes you to build an accurate mental model more than writing new code does, because that's usually isolated to a small, encapsulated domain where you only really need to care about the inputs and outputs (hopefully!).

The author is absolutely correct if you take 'review' to be 'click the accept button and move on', but if you're actually reviewing the code that your AI generates, and understanding it, and thinking about how to move forwards and prompt it to build the thing you really want, then AI only really removes the last type-the-code step. All of the architecture and process steps should be coming from you (maybe from a conversation with the AI during the planning step, but still, not just letting the AI do whatever it fancies.)

> "You don’t learn to recognize good code by reading about it in a textbook, or a PR. You learn by writing bad code, getting it torn apart, and building intuition through years of practice."

I'd like to point out though, that you also learn by AI producing bad outcomes you are responsible for, and building intuition how it might fail through practice... You also might experience more lessons than you would have if you would have coded manually.

Replace this with “writing assembly”