I've mentioned this in another thread, but a GPT-3 that could reliably generate quizbowl questions like the ones on https://www.quizbowlpackets.com would be great in this domain. My experience with it indicates it's no where near being able to do this, though.

Content farms are hardly a productive application.

Yes and no.

It may be useful to hire less low skilled employees and keep a few senior ones that take input from machine and decide what to keep and what to throw away. I'm not sure if a senior engineer would be more productive patching up code written by a bot or writing it from scratch. It's going to be a hard sell while you still need human supervisors.

You can't trust a machine that can't reason with code implementation, or even content creation. You need a human to supervise or a better machine.

We already have AI based auto-completion for code, gpt-3 can be useful for that (but at what cost? Storing a huge model on your disk or making a slow / unsafe http request to the cloud?)

> if a senior engineer would be more productive patching up code written by a bot or writing it from scratch.

I have no doubt writing from scratch would win hands down. The main reason we patch wonky legacy code is because it's already running and depended on. If you remove that as a consideration, a senior engineer writing the equivalent code (rather than debugging code generated randomly from Google searches) would -IMO- would be more efficient and produce a higher quality program.

That's a big if though.

GPT-3 is much more interesting autocomplete based on most commonly used patterns than something which figures out that Problem X has a lot of conceptual similarities with Solved Problem Y so it can just reuse the code example with some different variable names.