0. It's not about "looking pretty", it's about being nigh impossible to understand or reason about. There is a 1000 LoC subroutine that I'm convinced was ported from FORTRAN 1 unchanged that cannot be understood by anyone under the age of 50 that underpins one of the "flagship" physics codes in my field. It's a fucking nightmare every time I have to worry about that stupid thing. I remind you that this was 100% the product of a human brain (pre computer age, pre social media, and I'm told quite the genius too). Perhaps one should not place the human brain on too high a pedestal and deify it.
1. Why did you take from my OP that I tell codex "write a climate simulation code, make no mistakes" and go suntanning on a beach in the tropics for the rest of the semester?
2. Perhaps you have a different experience from me in writing HPC codes, but my experience is that > 90% of the code is boilerplate. I find GPT 5.6 can be prone to overengineering, but with a little steering and good judgement it generates very nice interfaces and high level code. I just have to think about the solver structure or metastructure.
3. Even with core numerics - pre-AI, it was a bunch of iteration going back and forth between code and optreports. Now codex will just do it. I suppose this may seem grim to you if you loved decorating every variable with !DIR$ ASSUME_ALIGNED, and manually batching array operations or whatever, but I didn't and I'm glad I no longer need to.
4. I'm now highly motivated to write tests, and AI makes it way easier to write the immense boilerplate around good tests (sorry not sorry, my {FUNDING_AGENCY} program manager doesn't give a flying fuck what my test coverage is, and my next grant won't depend on that in the slightest, so pre-AI I did the bare minimum. You can argue that the results will be worse, yadda yadda, but the incentive structure that {FUNDING_AGENCY} has in place don't promote good software standards, and my career never suffered for it)
5. I can generate docstrings with high accuracy (see the above)