Which of course they are going to try to brush it all away. Better than admitting that this problem very much still exists and isn’t going away anytime soon.
Don't get me wrong, I use AI every day, but it's mostly as a localized code complete or to help me debug tricky issues. Meaning I've written and understand the code myself, and the AI is there to augment my abilities. AI works great if it's used as a deductive tool.
Where it runs into issues is when it's used inductively, to create things that aren't there. When it does this, I feel the hallucinations can be off the charts -- inventing APIs, function names, entire libraries, and even entire programming languages on occasion. The AI is more than happy to deliver any kind of information you want, no matter how wrong it is.
AI is not a tool, it's a tiny Kafkaesque bureaucracy inside of your codebase. Does it work today? Yes! Why does it work? Who can say! Will it work tomorrow? Fingers crossed!
I use AI for rather complex tasks. It's impressive. It can make a bunch of non-trivial changes to several files, and have the code compile without warnings. But I need to iterate a few times so that the code looks like what I want.
That being said, I also lose time pretty regularly. There's a learning curve, and the tool would be much more useful if it was faster. It takes a few minutes to make changes, and there may be several iterations.
2) No matter what the learning curve, you're using a statistical tool that outputs in probabilities. If that's fine for your workflow/company, go for it. It's just not what a lot of developers are okay with.
Of course it's a spectrum with the AI deniers in one corner and the vibe coders in the other. I personally won't be relying 100% on a tool and letting my own critical thinking atrophy, which seems to be happening, considering recent studies posted here.
Where the AI fails is in doing anything which requires having a model of the world. I'm writing a simulator which involves agents moving through an environment. A small change in agent behaviour may take many steps of the simulator to produce consequential effects, and thinking through how that happens -- or the reverse: reasoning about the possible upstream causes of some emergent macroscopic behaviour -- requires a mental model of the simulation process, and AI absolutely does _not_ have that. It doesn't know that it doesn't have that, and will therefore hallucinate wildly as it grasps at an answer. Sometimes those hallucinations will even hit the mark. But on the whole, if a mental model is required to arrive at the answer, AI wastes more time than it saves.
I wonder if anyone has compared how well the AI auto-generating approach works compared to meta programming approaches (like Lisp macros) meant to address the same kind of issues with repetitive code.
The problem is, that phase is not the full life cycle of the boiler plate.
You have to live with it afterward.