> I would love to see an anti-AI take that doesn't hinge on the idea that technology forces people to be lazy/careless/thoughtless.
Here's a couple points which are related to each other:
1) LLMs are statistical models of text (code being text). They can only exist because huge for-profit companies ingested a lot of code under proprietary, permissive and copyleft licenses, most of which at the very least require attribution, some reserve rights of the authors, some give extra rights to users.
LLM training mixes and repurposes the work of human authors in a way which gives them plausible deniability against any single author, yet the output is clearly only possible because of the input. If you trained an LLM on only google's source code, you'd be sued by google and it would almost certainly reproduce snippets which can be tracked down to google's code. But by taking way, way more input data, the blender cuts them into such fine pieces that the source is undetectable, yet the output is clearly still based on the labor of other people who have not been paid.
Hell, GPT3 still produced verbatim snippets of inverse square root and probably other well known but licensed code. And github has a checkbox which scans for verbatim matches so you don't accidentally infringe copyright by using copilot in a way which is provable. Which means they take extra care to make it unprovable.
If I "write a book" by taking an existing book but replacing every word with a synonym, it's still plagiarism and copyright infringement. It doesn't matter if the mechanical transformation is way more sophisticated, the same rules should apply.
2) There's no opt out. I stopped writing open source over a year ago when it became clear all my code is unpaid labor for people who are much richer than me and are becoming richer at a pace I can't match through productive work because they own assets which give them passive income. And there's no license I can apply which will stop this. I am not alone. As someone said, "Open-Source has turned into a form of unpaid internship"[0]. It might lead to a complete death of open source because nobody will want to see their work fed into a money printing machine (subscription based LLM services) and get nothing in return for their work.
> But if you like the doing, the typing, fiddling with knobs and configs, etc etc, all AI does is take the good part away.
I see quite the opposite. For me, what makes programming fun is deeply understanding a problem and coming up with a correct, clear to understand, elegant solution. But most problems a working programmer has are just variations of what other programmers had. The remaining work is prompting the LLMs in the right way that they produce this (describing the problem instead of thinking about its solutions) and debugging bugs LLMs generated.
A colleague vibe coded a small utility. It's useful but it's broken is so many ways, the UI falls apart when some text gets too long, labels are slightly incorrect and misleading, some text handle decimal numbers in weird ways, etc. With manually written code, a programmer would get these right the right time. Potential bugs become obvious as you're writing the code because you are thinking about it. But they do not occur to someone prompting an LLM. Now I can either fix them manually which is time consuming and boring, or I can try prompting an LLM about every single one which is less time consuming but more boring and likely to break something else.
Most importantly, using an LLM does not give me deeper understanding of the problem or the solution, it keeps knowledge locked in a black box.
Here's a couple points which are related to each other:
1) LLMs are statistical models of text (code being text). They can only exist because huge for-profit companies ingested a lot of code under proprietary, permissive and copyleft licenses, most of which at the very least require attribution, some reserve rights of the authors, some give extra rights to users.
LLM training mixes and repurposes the work of human authors in a way which gives them plausible deniability against any single author, yet the output is clearly only possible because of the input. If you trained an LLM on only google's source code, you'd be sued by google and it would almost certainly reproduce snippets which can be tracked down to google's code. But by taking way, way more input data, the blender cuts them into such fine pieces that the source is undetectable, yet the output is clearly still based on the labor of other people who have not been paid.
Hell, GPT3 still produced verbatim snippets of inverse square root and probably other well known but licensed code. And github has a checkbox which scans for verbatim matches so you don't accidentally infringe copyright by using copilot in a way which is provable. Which means they take extra care to make it unprovable.
If I "write a book" by taking an existing book but replacing every word with a synonym, it's still plagiarism and copyright infringement. It doesn't matter if the mechanical transformation is way more sophisticated, the same rules should apply.
2) There's no opt out. I stopped writing open source over a year ago when it became clear all my code is unpaid labor for people who are much richer than me and are becoming richer at a pace I can't match through productive work because they own assets which give them passive income. And there's no license I can apply which will stop this. I am not alone. As someone said, "Open-Source has turned into a form of unpaid internship"[0]. It might lead to a complete death of open source because nobody will want to see their work fed into a money printing machine (subscription based LLM services) and get nothing in return for their work.
> But if you like the doing, the typing, fiddling with knobs and configs, etc etc, all AI does is take the good part away.
I see quite the opposite. For me, what makes programming fun is deeply understanding a problem and coming up with a correct, clear to understand, elegant solution. But most problems a working programmer has are just variations of what other programmers had. The remaining work is prompting the LLMs in the right way that they produce this (describing the problem instead of thinking about its solutions) and debugging bugs LLMs generated.
A colleague vibe coded a small utility. It's useful but it's broken is so many ways, the UI falls apart when some text gets too long, labels are slightly incorrect and misleading, some text handle decimal numbers in weird ways, etc. With manually written code, a programmer would get these right the right time. Potential bugs become obvious as you're writing the code because you are thinking about it. But they do not occur to someone prompting an LLM. Now I can either fix them manually which is time consuming and boring, or I can try prompting an LLM about every single one which is less time consuming but more boring and likely to break something else.
Most importantly, using an LLM does not give me deeper understanding of the problem or the solution, it keeps knowledge locked in a black box.
[0]: https://aria.dog/barks/forklift-certified-license/