They don't get them all wrong, and even when they are not 100% correct they're usually better than nothing.

For instance, I needed to write code to spawn a child process and communicate with it via stdin/stdout in C++. This is pretty easy in most modern languages but in C++ you have to call POSIX's dump process spawning dance pretty much with raw syscalls. fork, execve, etc.

Rather than googling all the syscalls I would need and how to arrange them I just asked ChatGPT to do it. I've done it before so it was much easier to verify than to start from scratch.

And it got it 90% right. The only bit it got wrong was to make a single pipe and connect it to both stdin and stdout, rather than one pipe for each. But that was easy to spot and fix.

AI - at least for programming - is an enormous time saver. Could easily increase productivity by 50% in some cases.

In 5 years I expect it to be as normal as using an IDE. There are still people that slow themselves down by using unintelligent editors, and they will probably continue to live in the 80s, but people that use tools to help them will expect to use Copilot or similar all the time.

Gell-man is exactly what I’ve been referencing in conversation recently. Any professional will gladly explain why their field is really much too nuanced and complex for LLMs to threaten in the near term before seamlessly explaining how close we are to all those other engineers/doctors/clerks being automated right away.

GPT-4 outperforms average students in exams of several fields. There are a lot of benchmarks, and GPT-4 does mostly do very well as long as the field relies enough on declarative knowledge.