Personally, I find that if a model can vibe-code the functionality I'm working on then it's not very high-value functionality. Perhaps (a) it's boilerplate (fine), (b) I'm not creating enough/the right abstraction, or (c) the code could easily be written by a junior dev. If I'm working on truly new functionality, modeling complex states and assumptions, or producing something that generalizes to many settings, the model does poorly beyond being a souped up auto-complete.
That's not to say that these models don't provide value, especially when writing code that is straightforward but can't be easily generalized/abstracted (e.g., some test-case writing, lots of boilerplate idioms in Go, and basic CRUD).
In terms of labor I potentially see this increasing the value (and therefore cost) of actual experienced developers who can approach novel and challenging problems, because their productivity can be dramatically amplified through proper use of AI tooling. At the other end of spectrum, someone who just writes CRUD all day is going to be less and less valuable.
Boilerplate code is my signal to take a few moment and says do I really need all that code? Or should I write some generators/parsers instead? Even when I'm not able to, copy-pasting is often easier.
That's not to say that these models don't provide value, especially when writing code that is straightforward but can't be easily generalized/abstracted (e.g., some test-case writing, lots of boilerplate idioms in Go, and basic CRUD).
In terms of labor I potentially see this increasing the value (and therefore cost) of actual experienced developers who can approach novel and challenging problems, because their productivity can be dramatically amplified through proper use of AI tooling. At the other end of spectrum, someone who just writes CRUD all day is going to be less and less valuable.