I'd suspect that a very large proportion of code has always been "private code" written for personal or intra-organizational purposes, and which never get released publicly.

But if we expect the ratio of this sort of private code to publicly-released code to remain relatively stable, which I think is a reasonable expectation, then we'd expect there to be a proportional increase in both private and public code as a result of any situation that increased coding productivity generally.

So the absence of a notable increase in the volume of public code either validates the premise that LLMs are not actually creating a general productivity boost for software development, or instead points to its productivity gains being concentrated entirely in projects that never do get released, which would raise the question of why that might be.

Oh yeah, I love building one off tools with it. I am working on a game mod with a friend, we are hand writing the code that runs when you play it, but we vibe code all sorts of dev tools to help us test and iterate on it faster.

Do internal, narrow purpose dev tools count as shipped code?

This seems to be a common thread. For personal projects where most details aren't important, they are good at meeting the couple things that are important to you and filling in the rest with reasonable, mostly-good-enough guesses. But the more detailed the requirements are, the less filler code there is, and the more each line of code matters. In those situations it's probably faster to type the line of code than to type the English equivalent and hand-hold the assistant through the editing process.

I don't think so, although I think at that point experience heavily comes into play. With GPT-5 especially, I can basically point cursor/codex at a repo and say "refactor this to this pattern" and come back 25 minutes later to a pretty much impeccable result. In fact that's become my favourite past time lately.

I linked some examples higher up, but I've been maintaining a lot of packages that I started slightly before chatgpt and then refactored and worked on as I progressively moved to the "entirely AI generated" workflow I have today.

I don't think it's an easy skill (not saying that to make myself look good, I spent an ungodly amount of time exploring programming with LLMs and still do), akin to thinking at a strategic level vs at a "code" level.

Certain design patterns also make it much easier to deal with LLM code: state reducers (redux/zustand for example), event-driven architectures, component-based design systems, building many CLI tools that the agent can invoke to iterate and correct things, as do certain "tools" like sqlite/tmux (by that I mean just telling the LLM "btw you can use tmux/sqlite", you allow it to pass hurdles that would otherwise just make it spiral into slop-ratatouille).

I also think that a language like go was a really good coincidence, because it is so amenable to LLM-ification.