[mnky9800n]

I was talking about this with someone today, that before perhaps there is an exactness you expect. But actually, what really matters is "good enough." And if AI written code takes you to "good enough" according to whatever metric you've set, then what exactly is the problem? Because a lot of the technical part of the job is taking X data, doing f(x) transformation to that data, and thus Y is born and handed to the next step. So if it passes whatever metric you have set to make sure that going from X to Y handles Z% of the problem space, and doesn't create downstream issues (probably this should be part of your metric), then you have done your job. And yes, of course sometimes the job will require you writing the code yourself because that level if precision is necessary. But why should we consider that always to be the case? And thus, actually, there are probably new programming languages and paradigms to consider that we haven't thought of yet that makes this kind of problem solving more efficient. Because right now we are not super effective at juggling both the human and the machine's problem space context. Except some experts who say they can orchestrate tens of agents all at once doing whatever. I dunno. I think right now is exciting and not hand wringing. A computer is meant to help you think. Why shouldn't new computational tools bring excitement?