I find this a real concern when it comes to the proposition of "replace every programmer with GPT". I think the reality is, there are cases where it matters, a

I find this a real concern when it comes to the proposition of "replace every programmer with GPT". I think the reality is, there are cases where it matters, and cases where it doesn't matter (at least, not very much). I work on aerospace software systems. I suspect that most software developers would be surprised at the process and rigor involved. That does not mean that humans are flawless; of course not. But there are going to be a lot more hurdles to replacing all of the humans with non-deterministic machines. There are numerous software components on a modern airplane. How good is good enough? If the overall system works correctly 99% of the time, is that good enough? There are about 16 million flights annually in the United States alone. If 160,000 of them crash, is that good enough? I mean, like, if Microsoft Word failed to save your document 99% of the time, you might be irked. If Professor Tao's data was only 99% accurate, he probably still has a reasonably good picture of the information he needs. Regardless of if a human or a machine is writing the code, there are different levels of software criticality. As humans, we don't apply avionics software development process to iOS games because it's not worth the extra time and expense. Likewise, there will be a spectrum of where it makes good enough sense to automate tasks with AI and where it doesn't. Which is not to dismiss AI! It's amazingly useful technology. But there really are places where above average accuracy is needed, and AI that works great for handling some tasks might not suffice for other tasks. If you truly need all of the results correct, then a nondeterministic neural network is probably not the best path to be on. There are other methods; even other automated methods.