Needing more training data seems wrong. Imagine the space of all programs. Can we agree that one can only train a model on a given subset of that space? Now, imagine wanting a program P that solves a particular problem, and that the program P is not in the training data. How does more training data guarantee that the output is P and not a neighboring P' that is almost correct, but subtly different and therefore utterly wrong? I think (and the author seems to agree) that the most we can hope for is a less sloppy approximation, not a correct program.

What does "forecast model" mean in this context?

I am surprised to hear an unfamiliar term described as a basic tool!

As someone whose job involves primarily developing forecasting models, I’m not sure I’d trust ChatGPT for any but the most basic problems.

> One can guess as to why (...job market).

I guess the farm tractor killed way more jobs (from potato harvesters, cereal harvesters, ...) than AI would even in the most utopian ways. But that doesn't matter: Both tractors and AI would and did shift wealth from the working class to the owning class. So I don't get why the "above" would try to not give GPT-4 the training data that they currently own anyway...

There's an acute shortage of programmers, especially in a world that increasingly relies on software.

If AI can solve that problem it will be a paradigm shift.

But I find it incredibly amusing the tech industry considers itself working class. Truly the oppressed of our world LMAO.