The obvious difference is that AI has abundant use-cases, while Crypto only has tenuous ones.
Maybe there is added negativity considering it is a technology where there is clearly a potential threat to jobs on a personal level (e.g. lift operators were very negative towards automatic lifts).
Subjectively, the two flavors of AI-negative sentiment I've seen most commonly on HN are (1) its potential to invade privacy, and (2) its potential to displace workers, including workers in tech.
I think that (1) was by far the most common concern up until around the ChatGPT release, at which point (2) became a major concern for many HN readers.
In terms of actually automating any form of ”thinking” tech work, LLMs are proving increasingly terrible. I say this as someone who work in a place where GPT writes all our documentation except for some very limited parts of our code base which can’t legally be shared with it. It increasingly also replaces our code-generation tools for most ”repetitive” work and it auto-generates a lot of our data-models based on various forms of inputs. But the actual programming? It’s so horrible at it that it’s mostly used as a joke. Well, except that it’s also not used like that by people who aren’t CS educated. The thing is though, we’ve already had to replace some of the “wonderful” automation that’s being cooked up by Product Owners, BI engineers and so on. Things which work, until they need to scale.
This is obviously very anecdotal, but I’m very underwhelmed and very impressed by AI at the same time. On one hand it’s frighteningly good at writing documentation… seriously, it wrote some truly amazing documentation based on a function named something along the lines of getCompanyInfoFromCVR (CVR being the Danish digital company registry) and the documentation GPT wrote based on just that was better than what I could’ve written. But tasked with writing some fairly basic computation it fails horribly. And I mean, where are my self driving cars?
So I think it’s a bit of a mix. But honestly, I suspect that for a lot of us, LLMs will generate an abundance of work when things need to get cleaned up.
> But the actual programming? It’s so horrible at it that it’s mostly used as a joke.
Please, for the sake of your future selves, hire someone who can write good documentation. (Or, better still but much harder, develop that skill yourself!) GPT documentation is the new auto-generated Javadoc comments: it looks right to someone who doesn't get what documentation is for, and it might even be a useful summary to consult (if it's kept up-to-date), but it's far less useful than the genuine article.
If GPT's better than you at writing documentation (not just faster), and you don't have some kind of language-processing disability, what are you even doing? Half of what goes into documentation is stuff that isn't obvious from the code! Even if you find writing hard, at least write bullet points or something; then, if you must, tack those on top of that (clearly marked) GPT-produced summary of the code.
Whether it's obvious from the code or not is kind of irrelevant. It gets non obvious things as well.
Honestly, I don't actually care what you do. The more documentation is poisoned by GPT-4 output, the less useful future models built by the “big data” approach will be, but the easier it'll be to spot and disregard their output as useless. If this latest “automate your documentation” fad paves the way for a teaching moment or three, it'll have served some useful purpose.