> There seems to be a strong bias where using AI feels like you're making a lot of progress very quickly, but compared to manual coding it often seems to be significantly slower in practice.

This metric highly depends on who uses the AI to do what, where strong emphasis is on "who" and "what".

In my line of work (software developer) the biggest time sinks are meetings where people need to align proposed solutions with the expectations of stakeholders. From that aspect AI won't help much, or at all, so measuring the difference of man hours spent from solution proposal to when it ends up in the test loops with and without AI would yield... very disappointing results.

But for troubleshooting and fixing bugs, or actually implementing solutions once they have been approved? For me, I'm at least 10x'ing myself compared to before I was using AI. Not only in pure time, but also in my ability to reason around observed behaviors and investigating what those observations mean when troubleshooting.

But I also work with people who simply cannot make the AI produce valuable (correct) results. I think if you know exactly what you want and how you want it, AI is a great help. You just tell it to do what you would have done anyway, and it does it quicker than you could. But if you don't know exactly what you want, AI will be outright harmful to your progress.

This struck me as odd too. 7 months? It wouldn’t take that long to write it in a new language.

Another thing I don’t see mentioned is code quality.

Vibe-coded code bases are an excellent example of why LLMs aren’t very good at writing code. It will often correct its own mistakes only to make them again immediately after and Inconsistent pattern use.

Recently Claude has been making some “interesting” code style choices, not inline with the code base it’s currently supposed to be working on.

Seems to be baked into the GPT- produce text- aka to produce language and code is life and purpose. So the whole system is inherently internally biased towards "roll your own everything?" unless spoke too in a "Senior-dev" language, that prevents these repetitions.

This was made in two days of vibe coding. It has flaws, but it's impressive as hell:

https://tinyskies.vercel.app/

It's got a fun Zelda-inspired mechanic (I won't say which one), and you'll have to unlock abilities and parts of the world over several quests and modes to "win".

It's also multiplayer.

It's more complex than that, I think the reality is that there's a lot of code that's just not that deep bro. I have some purely personal projects that have components that I don't understand anymore, I wrote that shit by hand, they still work but I haven't touched that shit in years. There's a lot of code that AI can write that's like that that helps me, the stuff I would forget about even if I wrote it by hand. I think you have to have discipline in it's use, it's a tool like any other.

AI, and especially agentic AI can make you lose situational awareness over a codebase and when you're doing deep work that SUUUUCKS, but it's not useless, you just have to play to it's strengths. Though my favorite hill to die on is telling people not to underestimate it's value as autocomplete. Turns out 40 gigabytes of autocomplete makes for a fucking amazing autocomplete. Try it with llama.vim + qwen coder 30b, it feels like the editor is reading your mind sometimes and the latency is so low.