For me, AI is an enabler for things you can't do otherwise (or that would take many weeks of learning). But you still need to know how to do things properly in general, otherwise the results are bad.

E.g. I'm a software architect and developer for many years. So I know already how to build software but I'm not familiar with every language or framework. AI enabled me to write other kind of software I never learned or had time for. E.g. I recently re-implemented an android widget that has not been updated for a decade by it's original author. Or I fixed a bug in a linux scanner driver. None of these I could have done properly (within an acceptable time frame) without AI. But also none of there I could have done properly without my knowledge and experience, even with AI.

Same for daily tasks at work. AI makes me faster here, but also makes me doing more. Implement tests for all edge cases? Sure, always, I saved the time before. More code reviews. More documentation. Better quality in the same (always limited) time.

I use Claude Code a lot but one thing that really made me concerned was when I asked it about some ideas I have had which I am very familiar with. It's response was to constantly steer me away from what I wanted to do towards something else which was fine but a mediocre way to do things. It made me question how many times I've let it go off and do stuff without checking it thoroughly.

Yes but in my experience this sometimes works great, other times you paint yourself in a corner and the sun total is that you still have to learn the thing, just the initial ram is less steep. For example I build my self a nice pipeline for converting jpegs on disk to h264 on disk via zero-copy nvjpeg to nvenc, with python bindings but have been pulling out my hair over bframe ordering and weird delays in playback etc. Nothing u solvable but I had to learn a great deal and when we were in the weeds, Opus was suggesting stupid hack quick fixes that made a whack a mole with the tests. In the end I had to lead e Pugh and read enough to be able to ask it with the right vocabulary to make it work. Similarly with entering many novel areas. Initially I get a rush because it "just works" but it really only works for the median case initially and it's up to you to even know what to test. And AIs can be quite dismissive of edge cases like saying this will not happen in most cases so we can skip it etc.

Huh. I'm extremely skeptical of AI in areas where I don't have expertise, because in areas where I do have expertise I see how much it gets wrong. So it's fine for me to use it in those areas because I can catch the errors, but I can't catch errors in fields I don't have any domain expertise in.

> Or I fixed a bug in a linux scanner driver. None of these I could have done properly (within an acceptable time frame) without AI. But also none of there I could have done properly without my knowledge and experience, even with AI

There are some things here that folks making statements like yours often omit and it makes me very sus about your (over)confidence. Mostly these statements talk in a business short-term results oriented mode without mentioning any introspective gains (see empirically supported understanding) or long-term gains (do you feel confident now in making further changes _without_ the AI now that you have gained new knowledge?).

1. Are you 100% sure your code changes didn't introduce unexpected bugs?

1a. If they did, would you be able to tell if they where behaviour bugs (ie. no crashing or exceptions thrown) without the AI?

2. Did you understand why the bug was happening without the AI giving you an explanation?

2a. If you didn't, did you empirically test the AI's explanation before applying the code change?

3. Has fixing the bug improve your understanding of the driver behaviour beyond what the AI told you?

3a. Have you independently verified your gained understanding or did you assume that your new views on its behaviour are axiomatically true?

Ultimately, there are 2 things here: one is understanding the code change (why it is needed, why that particular change implementation is better relative to others, what future improvements could be made to that change implementation in the future) and skill (has this experience boosted your OWN ability in this particular area? in other words, could you make further changes WITHOUT using the AI?).

This reminds me of people that get high and believe they have discovered these amazing truths. Because they FEEL it not because they have actual evidence. When asked to write down these amazing truths while high, all you get in the notes are meaningless words. While these assistants are more amenable to get empirically tested, I don't believe most of the AI hypers (including you in that category) are actually approaching this with the rigour that it entails. It is likely why people often think that none of you (people writing software for a living) are experienced in or qualified to understand and apply scientific principles to build software.

Arguably, AI hypers should lead with data not with anecdotal evidence. For all the grandiose claims, the lack of empirical data obtained under controlled conditions on this particular matter is conspicuous by its absence.

In my case I built a video editing tool fully customized for a community of which I am a member. I could do it in a few hours. I wouldn't have even started this project as I don't have much free time, though I have been coding for 25+ years.

I see it empowering to build custom tooling which need not be a high quality maintenance project.

I'm in the same boat. I've been taking on much more ambitious projects both at work and personally by collaborating with LLMs. There are many tasks that I know I could do myself but would require a ton of trial and error.

I've found giving the LLMs the input and output interfaces really help keep them on rails, while still being involved in the overall process without just blindly "vibe coding."

Having the AI also help with unit tests around business logic has been super helpful in addition to manual testing like normal. It feels like our overall velocity and code quality has been going up regardless of what some of these articles are saying.

I think what we'll see as AI companies collect more usage data the requirements for knowing what you do will sink lower and lower. Whatever advantage we have now is transient.

> But you still need to know how to do things properly in general, otherwise the results are bad.

Even that could use some nuance. I'm generating presentations in interactive JS. If they work, they work - that's the result, and I extremely don't care about the details for this use case. Nobody needs to maintain them, nobody cares about the source. There's no need for "properly" in this case.

I've found this is exact opposite of what I'd dare do with AI, things you don't understand are things you can't verify. Consider you want a windowed pane for your cool project, so you ask an AI to draft a design. It looks cool and it works! Until you bring it outside where after 30 minutes it turns into explosive shrapnel, because the model didn't understand thermal expansion, nor did you.

Contrast this to something you do know but can't be arsed to make; you can keep re-rolling a design until you get something you know and can confirm works. Perfect, time saved.

Also most of the studies shown start to be obsolete with AI rapid path of improvements. Opus 4.5 has been a huge game changer for me (combined with CC that I had not used before) since December. Claude code arrived this summer if I’m not mistaken.

So I’m not sure a study from 2024 or impact on code produced during 2024 2025 can be used to judge current ai coding possibilities.