The kind of person who insists on understanding things and working through the problem has always been rarer. It's not "humble", it's "inquisitive" and "persistent".
The kind of person who insists on understanding things and working through the problem has always been rarer. It's not "humble", it's "inquisitive" and "persistent".
1. "Nonsense, if it was a code-quality issue from the AI stuff, we'd have seen problems sooner, like in the first few months or a year. Oh, sure, some engineers complained, but that was just an adjustment period because they stopped once we told them we didn't care and mandated that they up-skill into the new AI-centric workflow future."
2. "The bold new AI initiative I put into place can't possibly be wrong, this failure is on the engineers who were responsible for overseeing its operation. This is supremely disappointing because we made their jobs so easy, they no longer have to write code, just review several dozens of pages per day with unremitting paranoid vigilance and attention to detail."
I guess it will take some years till we are sane again or not...
I revisit it to see if the promises are unbroken now from time to time with every other update and these days it doesn't look so good. It is non deterministic all the way down.
the less friction the less growth.
So even if they copied from SO they would have better knowledge than an Ai user.
This isn’t speculation either, theres an MIT study which this is based on. https://www.media.mit.edu/publications/your-brain-on-chatgpt...
If I know where the error is (a script written by AI in powershell) and it's a logical one, it's ok to let the AI reason about it to get it fixed and move on.
If I have no clue about how this part of the system works, then it's worth reading closely what the error says, in order to understand it first, then have the agent check the assumption you have.
Reduce the friction by having the agent explain what happened and why the fix solved it. I know this might be an intellectual placebo, but sometimes you need to fix something fast to move on. Learning takes longer, and these days everyone expects you to be a 100x engineer with AI.
You don't have to be a certain person to fall into this trap, you just condition your brain to accept this workflow somewhere.
Pair this with day to day work stress like time and amount of tasks and you almost give in to a sort of addiction to deal with it all.
There's an infinity between someone who has to panel beat what they copied off SO and someone who just bangs their head repeatedly against an LLM.
Well, this is where I disagree. I have coworkers who used to insist on understanding and now are doing exactly as OP wrote - copy/pasting from the LLM to brute force error messages. I don't know if it's generalizable but this is what I see in <big tech> working on a frontend team with mid to senior level engineers who I respect.
One example a few weeks ago, I was helping a coworker root cause a bug in a React codebase. Pair programming isn't necessarily common but sometimes you see someone banging their head at a problem and you get curious. It turned out there was an effect (a callback that's invoked whenever some state changes) that invokes an API and this effect caused an infinite render loop because the error handling wasn't written correctly. It was something silly like - Call this API if we have no data -> get error -> update state -> call API again because we have no data...
That was almost immediately what I suspected but my colleague was pulling out all the stops usually reserved for when you're desperate or need a sanity check, like logging to stdout after each line. Both my colleague and the LLM were convinced the problem lied in the pagination logic of the helper that invokes the API. He ended up rewriting that helper imperatively and functionally. To his credit, he rewrote it by hand and implemented the recursion correctly but he was baffled when the problem remained. Completely surprised Pikachu face.
I don't think my coworker changed or suddenly stopped caring. It seems much more likely this is a predictable outcome when you lean heavily into AI authoring code for a sustained period of time. I'd also say that in itself is a consequence of the extreme pressure being exerted across the entire company to ship more code and review more code, faster.