4 ms·

>the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. I think AI is being driven most by those interested in the "promise

>the whole reason for the "AI epidemic" is that people are avoiding effort like the plague. I think AI is being driven most by those interested in the "promise" of eliminating workers altogether. But, if by "AI epidemic", you more mean the increasing uptake of AI by workers/devs themselves (versus the overall frenzy), then I think the characterizations of "avoiding effort" and "laziness" are a bit harsh. I mean, of course people don't generally want to take the path of most resistance. And, there's value in efficiency being an "approach to life". But, I think there's still more from the human side: that is, if the things you're spending your time on have less value of the sort emphasized in your environment, then your interest will likely naturally wane over time. I think this is especially true of something tedious like coding. So, I believe there's a human/social element that goes beyond the pressure to keep up for the sake of job security.

andai 2y ago
What I meant specifically was overreliance on AI, in the sense that I'm now hearing that many junior devs can't do basic coding without AI. (The parallel that comes to my mind is that programmers raised without garbage collection learned to write more efficient code, and now a text editor uses several gigabytes. But yeah, someone called me a "dinosaur" already in this thread ;) Though this "increase in incompetence" might be a case of selection effect rather than degradation of ability, i.e. people who wouldn't even have gotten the job before are now getting into the industry. I'm not sure, it's probably a bit of both. I guess it's nothing new! See this great article from nearly 20 years ago: https://blog.codinghorror.com/why-cant-programmers-program/ https://blog.codinghorror.com/why-cant-programmers-program/
- unclebucknasty 2y ago
  Understood. Thanks for clarifying. Yeah, I've been in the game for "a while" myself. The example you gave with garbage collection reminds me of stories I've heard about Assembly language programmers back in the day, declaring that devs who use high level languages (like C) don't really know how to write good code. There's probably something to that, but it does beg the question "what's the right tradeoff?" Not only do we not have to deal with memory management, but also data structures and common algorithms, etc. I do think understanding those things made me a better coder, but perhaps progressively less meaningfully so, as time marched on and the lower level stuff was abstracted away by more and more layers on our move up stack to solve harder problems. So, the emphasis on writing code from scratch lessened over the years, and development became more about integrating other people's code (frameworks, libraries, API's, etc). So, I think that kind of teed up this AI approach to writing code, in that it's just more code to integrate that you didn't write. But, it is qualitatively different from the "natural progression", in that they're not writing code at all. In that way it feels like the last stop on the coding continuum. I suppose that makes sense, since we're told that we soon won't need human coders at all.
  - andai 2y ago
    I'm reminded of Buckminster Fuller's concept of ephemeralization: as technology improves, you can do more and more with less and less, until eventually you reach the point where you can do everything with nothing. https://www.umsl.edu/~sauterv/analysis/Fall2013Papers/Purcell/bucky.html https://www.umsl.edu/~sauterv/analysis/Fall2013Papers/Purcel...
- xign 2y ago
  With the garbage collection aspect, I do want to point out that eventually people rediscover the benefit of lower level ways to do things and instead come up with improved methods of doing them. See how Rust and Zig have been gaining in popularity for example as a modern alternative to C/C++. As you said, learning how to write in such languages also yield benefits in general as you learn to be more efficient. Old ideas being new again happens all the time. It's easy to get masked by the new hotness before the disadvantages start to pile up and you need to do a more honest evaluation on the pros and cons of each technique. For example with garbage collection, turns out computers are actually still dog slow today in an absolute sense and squeezing performance is still worth it (people who want to argue otherwise and say "computers are lightning fast", please tell me why a video game cannot render 4K / 120 fps even today, or why an LLM takes months to train with a gazillion GPUs, or why VSCode loads slower than an old copy of Visual Studio on an ancient machine). I think AI / LLM will probably stick around, but programmers who have become too reliant on them will eventually be filtered out much quicker than people who bother to learn to program well and retain more useful skillsets (even if they end up using LLMs themselves).

andai 2y ago

What I meant specifically was overreliance on AI, in the sense that I'm now hearing that many junior devs can't do basic coding without AI. (The parallel that comes to my mind is that programmers raised without garbage collection learned to write more efficient code, and now a text editor uses several gigabytes. But yeah, someone called me a "dinosaur" already in this thread ;) Though this "increase in incompetence" might be a case of selection effect rather than degradation of ability, i.e. people who wouldn't even have gotten the job before are now getting into the industry. I'm not sure, it's probably a bit of both. I guess it's nothing new! See this great article from nearly 20 years ago: https://blog.codinghorror.com/why-cant-programmers-program/ https://blog.codinghorror.com/why-cant-programmers-program/

unclebucknasty 2y ago
Understood. Thanks for clarifying. Yeah, I've been in the game for "a while" myself. The example you gave with garbage collection reminds me of stories I've heard about Assembly language programmers back in the day, declaring that devs who use high level languages (like C) don't really know how to write good code. There's probably something to that, but it does beg the question "what's the right tradeoff?" Not only do we not have to deal with memory management, but also data structures and common algorithms, etc. I do think understanding those things made me a better coder, but perhaps progressively less meaningfully so, as time marched on and the lower level stuff was abstracted away by more and more layers on our move up stack to solve harder problems. So, the emphasis on writing code from scratch lessened over the years, and development became more about integrating other people's code (frameworks, libraries, API's, etc). So, I think that kind of teed up this AI approach to writing code, in that it's just more code to integrate that you didn't write. But, it is qualitatively different from the "natural progression", in that they're not writing code at all. In that way it feels like the last stop on the coding continuum. I suppose that makes sense, since we're told that we soon won't need human coders at all.
- andai 2y ago
  I'm reminded of Buckminster Fuller's concept of ephemeralization: as technology improves, you can do more and more with less and less, until eventually you reach the point where you can do everything with nothing. https://www.umsl.edu/~sauterv/analysis/Fall2013Papers/Purcell/bucky.html https://www.umsl.edu/~sauterv/analysis/Fall2013Papers/Purcel...
xign 2y ago
With the garbage collection aspect, I do want to point out that eventually people rediscover the benefit of lower level ways to do things and instead come up with improved methods of doing them. See how Rust and Zig have been gaining in popularity for example as a modern alternative to C/C++. As you said, learning how to write in such languages also yield benefits in general as you learn to be more efficient. Old ideas being new again happens all the time. It's easy to get masked by the new hotness before the disadvantages start to pile up and you need to do a more honest evaluation on the pros and cons of each technique. For example with garbage collection, turns out computers are actually still dog slow today in an absolute sense and squeezing performance is still worth it (people who want to argue otherwise and say "computers are lightning fast", please tell me why a video game cannot render 4K / 120 fps even today, or why an LLM takes months to train with a gazillion GPUs, or why VSCode loads slower than an old copy of Visual Studio on an ancient machine). I think AI / LLM will probably stick around, but programmers who have become too reliant on them will eventually be filtered out much quicker than people who bother to learn to program well and retain more useful skillsets (even if they end up using LLMs themselves).