My opinion is that there isn’t a one-size-fits-all answer to this question. There exists a class of software for which the detriments of using LLMs in a non-disciplined way is kinda irrelevant. Generally, this is the kind of software that isn’t worth the money that would be spent on an actual developer. These would be, like, very small user-base internal applications.
I might also argue against the “meant to be used for all” part of that statement. That’s the way that AI companies - and many execs - are trying to position LLMs, but I don’t think that’s the reality of their usefulness. Kent Beck did a pretty good talk about this recently that I think summarizes the position well. LLMs let you make mistakes in days that used to take months. The corollary to that is that they also enable you to fix those mistakes in days instead of months, but that requires understanding, thought, and patience.
My opinion is that there isn’t a one-size-fits-all answer to this question. There exists a class of software for which the detriments of using LLMs in a non-disciplined way is kinda irrelevant. Generally, this is the kind of software that isn’t worth the money that would be spent on an actual developer. These would be, like, very small user-base internal applications.
I might also argue against the “meant to be used for all” part of that statement. That’s the way that AI companies - and many execs - are trying to position LLMs, but I don’t think that’s the reality of their usefulness. Kent Beck did a pretty good talk about this recently that I think summarizes the position well. LLMs let you make mistakes in days that used to take months. The corollary to that is that they also enable you to fix those mistakes in days instead of months, but that requires understanding, thought, and patience.