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Just a tangential note, it is useful for programming in the sense you can use it as a faster google to lookup a snippet. By the same token, asking "what's the

Just a tangential note, it is useful for programming in the sense you can use it as a faster google to lookup a snippet. By the same token, asking "what's the kelly criterion" is faster than googling (for a finance example). But most programmers (other than sheer juniors) also don't spend most of their day doing "insert snippet here". ChatGPT isn't close to figuring out the proper solution to link up multiple disparate APIs, read through the docs to figure out why the system was written a particular way, spend hours chatting with various people, wade through the political mire, understand vague customer and business requirements, and then finally submit a PR that forms the first in a long series of fraught attempts to cobble together such a system. Upon PR review, deliver correctly worded responses depending on the personalities of the people who are ripping your code to shreds for pedantic and often meaningless reasons (mostly to seem important), and notice when the feedback is actually legitimate and requirees changes, or signals a bigger issue with the entire project. Do that in a loop, keeping the context of the progress over the last few months/years so that a steady stream of design documents, political agreements, assignments, knowledge transfer as employees change over, and system requirements evolve, and changes come together to form a project that adjusts subtly over time as the politics and business requirements change as well. In the end, everyone's "day job" is full of nuance and subtlety, but GPT is really good for automating everyone else's jobs (like those lawyers, its just a bunch of text rules, right? Or doctors, its just looking up a matching list of symptoms I think...)

Havoc 3y ago
>everyone's "day job" is full of nuance and subtlety, but GPT is really good for automating everyone else's jobs That's not really where I was going with that, but can see how it may come across as such. My point wasn't that one job has nuance & high level skills while the other doesn't but rather that ChatGPT has different usefulness at the low end of each. Or put differently for coding ChatGPT use generating boilerplate is the obvious win at the low end. I haven't found the equivalent low hanging fruit in my day to day despite enthusiastic trying. >google to lookup a snippet. By the same token, asking "what's the kelly criterion" Think a step slightly more complicated than wikipedia like info retrieval. For coding it can do a fair bit more thanks to vast amounts of github code...for other jobs there is no equivalent depth of knowledge baked into the models. Maybe a couple of medical journals, some transcripts of law cases? It's just nowhere near though in scale, code or applicability and that shows. Nor are the other jobs as inherently machine readable (or modular as I said - code is usually split nicely into functional procedures etc)
- program_whiz 3y ago
  Just to echo this, I can see if you are a program manager or something "programming adjacent" you also would have little to no use for ChatGPT (despite being in an area its mostly very good at). It removes all the "write me a snippet" low hanging fruit out of the job. E.g. if you are the one running the team of engineers, there's nothing like that, its all just the nuance / politics and human decision making that requires the full context (not something you can jot down easily for GPT). For finance, probably anyone doing math / quant / research could still have low hanging fruit (solve this integral, what's the formula, how do I do this in excel), but anyone who is mostly decision making can't get much value from GPT.

Havoc 3y ago

>everyone's "day job" is full of nuance and subtlety, but GPT is really good for automating everyone else's jobs That's not really where I was going with that, but can see how it may come across as such. My point wasn't that one job has nuance & high level skills while the other doesn't but rather that ChatGPT has different usefulness at the low end of each. Or put differently for coding ChatGPT use generating boilerplate is the obvious win at the low end. I haven't found the equivalent low hanging fruit in my day to day despite enthusiastic trying. >google to lookup a snippet. By the same token, asking "what's the kelly criterion" Think a step slightly more complicated than wikipedia like info retrieval. For coding it can do a fair bit more thanks to vast amounts of github code...for other jobs there is no equivalent depth of knowledge baked into the models. Maybe a couple of medical journals, some transcripts of law cases? It's just nowhere near though in scale, code or applicability and that shows. Nor are the other jobs as inherently machine readable (or modular as I said - code is usually split nicely into functional procedures etc)

program_whiz 3y ago
Just to echo this, I can see if you are a program manager or something "programming adjacent" you also would have little to no use for ChatGPT (despite being in an area its mostly very good at). It removes all the "write me a snippet" low hanging fruit out of the job. E.g. if you are the one running the team of engineers, there's nothing like that, its all just the nuance / politics and human decision making that requires the full context (not something you can jot down easily for GPT). For finance, probably anyone doing math / quant / research could still have low hanging fruit (solve this integral, what's the formula, how do I do this in excel), but anyone who is mostly decision making can't get much value from GPT.