Ask HN: Is anyone else getting AI fatigue?

I've worked with teams that used Copilot. They claim it's great "Hey, now I don't have to actually spend any time writing all this boilerplate!" while for me, the person who has to review their code before releasing stuff, easier ways of writing boilerplate is not a positive, it's a negative.

If writing boilerplate becomes effortless, then you'll write more of it, instead of feeling the pain of writing it and then trying to reduce it, because you don't want to spend time writing it.

And since Copilot was accepted as a way to help the developers on the teams, the increase of boilerplate have been immersive.

I'm borderline pissed, but mostly at our own development processes, not at Copilot per se. But damn if I didn't wish it existed somehow, although it was inevitable it would at one point.

I've used it quite a lot and I agree with the original post. It seemed really useful at first but then it started introducing several bugs in large blocks of code. I've stopped using it in the end since the small snippets on the one line size is trivial enough to write myself (with just vim proficiency) and the larger blocks on the order of a function autocomplete is too bug prone (and kills too much willpower budget to fix).

I haven't. Now you know for a fact :)

What I have seen about it ranged from things that can be nearly just as well handled by your $EDITOR's snippet functionality to things where my argument kicked in - I have to verify this generated code does what I want, ergo I have to read and understand something not written by me. Paired with the at least somewhat legally and ethical questionable source of the training data, this is not for me.

I disagree; consider the use of the term "video game AI", which historically at least has just been a bunch of _if_ statements chained together. This is totally valid, it's an example of AI without machine learning.

The thing is that AI is just about the most general term for the type of computing that gives the illusion of intelligence. Machine learning is a more specific region of the space of AI, and generally is made of statistical models that lead to algorithms that can train and modify their behavior based on data. But this includes "mundane" algorithms like k-means clustering or line-fitting. Deep learning (aka neural networks) is yet a more specific subfield of ML.

I think the term AI just has more "sex appeal" because people confuse it with the concept of AGI, which is the holy grail of machine intelligence. But we don't even know if this is achievable, or what technology it will use.

So in terms of conceptual spaces, we can say that AI > ML > DL, and we can say (by definition) that AI > AGI. And it seems very likely that AGI > ML. But it's not known, for instance, whether AGI > DL, ie, we don't know for sure that deep learning/neural networks are sufficient to obtain AGI.

In any case, people should put less weight on the term AI, as it's a pretty low bar. But also yes, the term is way over hyped.

I feel like there will be some lucrative opportunities somewhere here to exploit the fallout from this hype. I get the fatigue, but our incomes are basically critically dependent on investor FOMO.

I'd rather we have bitcoin crazes, scaling crazes, nosql crazes and GPT crazes than this industry commoditizes itself to hell and I have to spend the rest of my career gluing AWS cognito to AWS lambdas for $55k / year.

At the same time I'm pretty sure that it will wildly change any industry where creativity is critically important and quality control either isn't that important or can be done by amateurs. There is substance at the core of the hype.