Also, a larger point is that this was fundamental research. A very hard thing. You're supposed to be getting it wrong most of the time. Then when you think you've got it right, you try to publish. Then you hope the reviewers actually read anything you wrote and catch all the errors [0]. But a lot of the time, errors sneak through and really only get discovered after a few years/decades. That's fine in the end, as science is a 'strongest-link' system where we really only care about the big and correct discoveries. 'Weak-link' systems are the opposite, they are governed by the small little falsehoods and errors.
[0] Very unlikely, reviews almost always only review to see if the paper is of a high enough quality for the journal to include. I want to be clear on this point: Reviewers do not re-do experiments and typically do not go back through your math, let alone the code you used. Now, each field is different and has different standards, and those vary wildly. But generally, reviewers aren't there to make sure the science is right.
Otherwise you'll be at the mercy of whatever autogenerated code that you happen to be using, since you literally don't have the skill to check if it's actually working.
The other alternative is to work with a programmer.
Our 'salary' was ~$24k/year. So, just enough to stay alive and for a beer on Fridays.
If my co-researcher had the coding abilities he needed, he would have left.
And, apologies if I wasn't clear enough here, we were grad students.
My co-researcher had never taken calculus, let alone any coding. Trying to get him up to speed with all the various skills he'd missed out on would have taken another 4 years of full time 'remedial' education. Of course, there was no time for that, let alone budget.
I totally agree with you though. And that's why I really think that AI would have helped out a lot. It's not perfect in any way, but your velocity through all the pitfalls of learning is a lot faster with the AI helping out.