> I'm a programmer, and I use automatic programming. The code I generate in this way is mine. My code, my output, my production. I, and you, can be proud.

Disagree.

So when there is a bug / outage / error, due to "automatic programming" are you ready to be first in line to accept accountability (the LLM cannot be) when it all goes wrong in production? I do not think that would even be enough or whether this would work in the long term.

No excuses like "I prompted it wrong" or "Claude missed something" or "I didn't check over because 8 other AI agents said it was "absolutely right"™".

We will then have lots of issues such as this case study [0] where everything seemingly looks fine at first, all tests pass but in production, the logic was misinterpreted by the LLM with a wrong keyword, [0] during a refactor.

[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...

> Pre-training is, actually, our collective gift

I feel like this wording isn't great when there are many impactful open source programmers who have explicitly stated that they don't want their code used to train these models and licensed their work in a world where LLMs didn't exist. It wasn't their "gift", it was unwillingly taken from them.

> I'm a programmer, and I use automatic programming. The code I generate in this way is mine. My code, my output, my production. I, and you, can be proud.

I've seen LLMs generate code that I have immediately recognized as being copied a from a book or technical blog post I've read before (e.g. exact same semantics, very similar comment structure and variable names). Even if not legally required, crediting where you got ideas and code from is the least you can do. While LLMs just launder code as completely your own.