> the jump would be similar to the one from hand-written punchcards/assembly to higher level compilers

I wouldn't. Compilers are not stochastic text models and they can be verified and reasoned about to a great extent.

I would love that, I mostly work with ideas and the codes are implementation details for me, so yes, in some way, having automated code generation would allow me to be way more productive. I'm not against it, I'm just scared about the efficiency of the approach by an LLM (at the moment at least)

The example codes they give is 'implementing deep learning papers', I find those papers the easiest to implement compared to some obscure algorithm for example that can't rely on frameworks such as pytorch and where speed is critical.

I can't find the essay, but I think it was wolfram that wrote that we should let students use Mathematica and educate them in using it from a young age, the rationale behind is: before you had to use logarithmic tables, and it took much time during the education. Then, with the event of the calculator, students could instantaneously compute logarithms, so they could focus on more advanced ideas that use them. With Mathematica they could automatically execute matrix operations, so they would spend most of the time thinking about matrix operations instead of just learning how to manipulate a matrix by hand.

So with more powerful tools, you can expand the capabilities faster.

But the main difference I see here, is that maths are precise and well defined. Here you get a software which is a sample in the space of possible softwares that solve the problem (if you are lucky).

To get to the metaphorical point punchcards->GHC you need a LLM tool that give always the same answer, and hopefully, the optimal one, and with small changes in the paper, it moves the software in the space of viable softwares only a bit. Maybe we will get there, but this is not yet what this paper proposes