In the other corner, Proposal B seems to lay out something similar to how AI-generated code is handled on the Linux kernel. The proposal allows AI-generated code only if the submitter takes full accountability for it, ensures it’s free of copyright issues, and declares any LLM usage.
Assuming full technical responsibility would probably make most AI-generated code pointless, as the whole point of using AI this way is that generating code saves work. Which is not any more the case if the result is really fully reviewed.
This is different from using AI for vulnerability scanning (as it is practised in the Linux kernel).
The other thing is there is no way one can declare AI generated code safely free of copyright isses. You’d need to prove that the result code was not contained in the training data - which is not possible.
Disagree on your first point. AI will generate code that is mostly correct, the human time cost of (properly) reviewig this code and correcting it is generally less than also writing it. People that do more serious work than vibe-coding oneshot skateboard simulators in html do actually get noticeable productivity boosts even when every generated line of code is reviewed by multiple people.
That is why the kernel allows it, because they mostly care that a human is around that will take responsability for it, that the patches are of the same quality than always and that everything is manageable by humans. This is in contrast to the techbro github repo with commits that span 20 files and 2k edits where there is no hope in hell to understand what is going on.
AI will generate code that is mostly correct, the human time cost of (properly) reviewig this code and correcting it is generally less than also writing it.
I do not see it play out like this in practice, for example at my workplace. The work of debugging, reviewing, verification, and maintenance is simply offloaded to other people. Which, as a result, have more work, not less.
The people running the agent need to be looking at the code and cleaning it, not someone else, that is the key thing that is missing in all these failures. Linux is the same, they would never accept something that has not at least the same quality as something written by a competent human
AI will generate code that is mostly correct, the human time cost of (properly) reviewig this code and correcting it is generally less than also writing it
Just calling LLMs “AI”, you disqualified yourself from being able to assess what proper programming is about.
That said, your statement is also objectively false in all other aspects.
I am a software dev with more than 10 years of experience I have code in production and need to support legacy systems. I am trying to explain how I found a way to use this stuff in helping me in doing my job and getting food on my family’s table, but sure buddy I don’t know what I am talking about.
I have had to debug code of “software devs with more than 10 years of experience” often enough to know experience does not equal skill. If you think slop generators are helping you, while you by violating the licenses of all developers whose code went into the training data, then you apparently did not invest those 10 years well.
Assuming full technical responsibility would probably make most AI-generated code pointless, as the whole point of using AI this way is that generating code saves work. Which is not any more the case if the result is really fully reviewed.
This is different from using AI for vulnerability scanning (as it is practised in the Linux kernel).
The other thing is there is no way one can declare AI generated code safely free of copyright isses. You’d need to prove that the result code was not contained in the training data - which is not possible.
Disagree on your first point. AI will generate code that is mostly correct, the human time cost of (properly) reviewig this code and correcting it is generally less than also writing it. People that do more serious work than vibe-coding oneshot skateboard simulators in html do actually get noticeable productivity boosts even when every generated line of code is reviewed by multiple people.
That is why the kernel allows it, because they mostly care that a human is around that will take responsability for it, that the patches are of the same quality than always and that everything is manageable by humans. This is in contrast to the techbro github repo with commits that span 20 files and 2k edits where there is no hope in hell to understand what is going on.
I do not see it play out like this in practice, for example at my workplace. The work of debugging, reviewing, verification, and maintenance is simply offloaded to other people. Which, as a result, have more work, not less.
The people running the agent need to be looking at the code and cleaning it, not someone else, that is the key thing that is missing in all these failures. Linux is the same, they would never accept something that has not at least the same quality as something written by a competent human
Just calling LLMs “AI”, you disqualified yourself from being able to assess what proper programming is about.
That said, your statement is also objectively false in all other aspects.
I am a software dev with more than 10 years of experience I have code in production and need to support legacy systems. I am trying to explain how I found a way to use this stuff in helping me in doing my job and getting food on my family’s table, but sure buddy I don’t know what I am talking about.
I have had to debug code of “software devs with more than 10 years of experience” often enough to know experience does not equal skill. If you think slop generators are helping you, while you by violating the licenses of all developers whose code went into the training data, then you apparently did not invest those 10 years well.
Ok thought you where a crusader, I am sure you also are against systemd and probably a rust keyboard warrior too eh?