But only 22% are accepted for those 8%, which means that the 78% code suggestions that are not accepted correspond to an equivalent of over 28% of all code written. Not sure that having to spend the time evaluating an additional 28% of code in vain amounts to an overall win.

Though I guess the success rates when using Stack Overflow aren’t too dissimilar.

What it doesn't tell us though is how useful the rejected recommendations were.

Meaning, how many rejected solutions were sufficient to give the engineer enough context to turn a 30m task into a 5m task because they generated a recommendation, got an idea, rejected it, and rewrote it more efficiently or more correctly?

There's a lot of "devil in the details" likely buried in here.

Interesting that 91% find it useful but only 8% of the code is generated by LLM. This is even with a LLM tuned on the internal codebase. This will give a mild boost but not replace anyone.