wow I'm going to have lots of opinions about this.
1. A lot of people on this thread are concerned about licensing issues with GPL etc. I am sure Github will restrict the beta until it figures out that stuff.
2. I wonder if eventually our corrections to the code suggested by the model would be used to feedback to the model, and if that'll lead to a differential pricing - If I let it see my code, I get charged lesser.
3. I believe a mini-GPT-3 model is where it's at. GPT-3 (and similar) models look to be to too big to run locally. I've been using TabNine for past year or so & it gives me anywhere between 5-10% productivity boost. But one of the main reasons why it works so well is because it trains on my repo as well. TabNine is based off GPT-2 from what i've heard.
4. prediction: Microsoft is probably going to milk GPT-3. Expect a bumpy ride.
5. In all likeliness, this would be a great tool to make developers productive, rather than take their jobs - at least at levels that are more than just code-coolie.
6. Eventually all tasks with enough data around it will see automation using AI.
I'm curious as to how relevant Copilot would be when autocompleting code that is specific to my codebase in particular, like Tabnine completes most used filters as soon as I type the db table name for the query. I'm a big tabnine fan because it provides this feature. I'm much more often looking to be suggested a line than an entire function because I'm mostly writing business logic.
also tabnine is useless in multi-lines completes. which is where co-pilot should be strong.
if your problem is geeksforgeeks culture (which translates to poor knowledge of software coding standards I guess), then you're clearly not filtering your candidates well enough. Does your interview process has any debugging or refactoring rounds?