At least with AI, you can (presumably) replicate the results if you re-run everything from the same state.
There's also a very interesting paragraph in the paper (I'm in no position to judge whether it's valid or not) that touches on this subject, but with a positive twist :
Interpretability. One major advantage of code generation models is that code itself is relatively interpretable. Understanding the behavior of neural networks is challenging, but the code that code generation models output is human readable and can be analysed by traditional methods (and is therefore easier to trust). Proving a sorting algorithm is correct is usually easier than proving a network will sort numbers correctly in all cases. Interpretability makes code generation safer for real-world environments and for fairer machine learning. We can examine code written by a human-readable code generation system for bias, and understand the decisions it makes.
People do advise against hiring people who write incomprehensible code.
Yeah every now and then you run across some genius with sloppy code style and you have to confine them to a module that you'll mark "you're not expected to understand this" when they leave because they're really that much of a genius, but usually the smart people are smart enough to write readable code.
I think this is more worthy of debate than anything about DSL models or current limits to problem spaces.
I'm not concerned about my job, but I am concerned about a world where corporate money starts shifting toward managing AIs as beasts rather than coding clever solutions. I'm concerned about it because (1) It has always been possible in theory to invent an infinite number of solutions and narrow them down, if you have the processing power, to those that "work", but, this leaves us in a position where we don't understand the code we're running (as a society) or how to fix it (as individuals). And (2) because learning to manage an elephant, as a beast, is utterly different from learning to build an elephant, and it will lead to a dumbing-down of people entering the trade. In turn, they'll become more reliant on things just working the way they're expected to work. This is a very negative cycle for humanity as a whole.
Given the thing you're looking forward to, it's only about 30 years before no one can write code at all; worse, no one will know how to fix a broken machine. I don't think that's the thing we should advocate for.