This was my thought when browsing this list, too, and it helped crystalize one of the feelings I had when trying to with with LLMs for coding: I'm a senior developer, and I want to develop as a senior developer does, and turn in senior developer-quality code. I _don't_ want to spend the rest of my career in development simply pairing with/babysitting a junior developer who will never learn from their mistakes. It may be quicker in the short run in some cases, but the code won't be as good and I'm likely to burn out, further amplifying the quality issue.
> I expect the models will continue improving though
I try to push back on this every time I see it as an excuse for current model behaviour, because what if they don't? Like, not appreciably enough to make a real difference? What if this is just a fundamental problem that remains with this class of AI?
Sure, we've seen incredible improvements over a short period of time in model capability, but those improvements have been visibly slowing down, and models have gotten much more expensive to train. Not to mention that a lot of the problem issues mentioned in this list are problems that these models have had for several generations now, and haven't gotten appreciably better, even while other model capabilities have.
I'm saying this not to criticize you, but more to draw attention to our tendency to handwave away LLM problems with a nebulous "but they'll get better so they won't be a problem." We don't actually know that, so we should factor that uncertainly into our analysis, not dismiss it as is commonly done.
I definitely agree that for current models, the problem is finding where the LLM has comparative advantage. Usually it's something like (1) something boring, (2) something where you don't have any of the low level syntax or domain knowledge, or (3) you are on manager schedule and you need to delegate actual coding.
> I expect the models will continue improving though
I try to push back on this every time I see it as an excuse for current model behaviour, because what if they don't? Like, not appreciably enough to make a real difference? What if this is just a fundamental problem that remains with this class of AI?
Sure, we've seen incredible improvements over a short period of time in model capability, but those improvements have been visibly slowing down, and models have gotten much more expensive to train. Not to mention that a lot of the problem issues mentioned in this list are problems that these models have had for several generations now, and haven't gotten appreciably better, even while other model capabilities have.
I'm saying this not to criticize you, but more to draw attention to our tendency to handwave away LLM problems with a nebulous "but they'll get better so they won't be a problem." We don't actually know that, so we should factor that uncertainly into our analysis, not dismiss it as is commonly done.