I personally find AI generated code to be pretty average. I might get AI to write a function, then rework it. I use it a lot for reviews, which helps. And also as a sounding board for research - this is by far the most valuable use case, saves a ton of time. Or get it to write tests similar to what you have, just tell it what you want tested, and get it to suggest.
I definitely don't trust the code it writes, especially for anything remotely complicated.
There is some interesting proposals on short term allocations, being able to specify that a local allocation will not leak.
Most recently, I've been fighting with the ChaCha20-Poly1305 implementation because someone in their 'wisdom' added a requirement for contiguous memory for the implementation, including extra space for a tag. Both ChaCha20 and Poly1305 are streaming algorithms, but the go authors decide 'you cannot be trusted' - here's a safe one-shot interface for you to use.
Go really needs a complete overhaul of their Standard Library to fix this, but I can't see this ever getting traction due to the focus on not breaking anything.
Go really is a great language, but should include performance / minimise the GC burden as a key design consideration for it's APIs.