I'm not surprised. AI tools can be great at providing a quick, working example in simple scenarios, but it's just that: a quick(often dirty) working example. Bu

I'm not surprised. AI tools can be great at providing a quick, working example in simple scenarios, but it's just that: a quick(often dirty) working example. But I've seen people taking it as-is and putting it into a production codebase. Sure, a function that iterates over an array, and checks if some item exists - fine. In those cases it will do the job simply because you(it) can't get it wrong. However I had this experience where senior developers were fully invested into using it. And because managers see code just erupting like a volcano, they embrace it and in fact rise their expectation when it comes to delivering a feature: "you could do it in a week before, you should be able to do it in 2 hours now, right?". And on more than one occasion this has backfired massively in everyone's face. The last time me and another dev spent two straight days rewriting and debugging everything cause there was an international exhibition that was about to start and the company was at the front of the line and everyone else simply pushed a ton of code that was 75% AI-generated, completely ignoring the edge cases, which were more than people anticipated. But probably the most off-putting thing I've experienced is an OKR session with 50 people in it, where a lead dev publicly opened chatgpt, prompted "how do we reduce the number of critical bugs by 30% in the next quarter", chatgpt came up with a generic response, everyone said "perfect", copy-paste that into Jira and call it a day. And I'm just sitting there and wondering if there was something rotten in my breakfast and I'm hallucinating. Unfortunately my breakfast was fine and that really happened. The few times I've tried using those, they were only helpful with dumb, repetitive and mundane tasks. Anything else, you have to rinse and repeat until you get a working solution. And when you read it(for those of us that do), you realize you might have been better off recruiting a freelancer from year one in University to spend a day mashing it up and likely coming up with something better. But I bet much of those year ones would be doing this exact thing day in and day out until they come up with a solution: Occasionally I will grab my laptop and go work at a cafe on my personal projects for a change and I can't tell you how many times I've seen people just copy pasting stuff from chatgpt and pasting it back into their IDE/editor and calling it a day - students and clearly people who are doing this for a living. Not to mention copilot, that's the de-facto standard at this point. In fact I had this conversation last year with a guy(developer) who is 20-something years older than me(so mid 50-s): most of the LLM's are trained on stuff that is in the documentation, examples, reddit and stackoverflow. It's only a question of time until the content found in those exact locations where the training data is pulled from will become more and more AI-generated, models will be re-trained on those and eventually shit hitting the fan. I don't think we are too far off from this event.