Productivity gains in programming have always been incredibly hard to prove, esp. on an individual level. We've had these discussions a million times long before AI. Every time a manager tries to reward some kind of metric for "good" code, it turns out that it doesn't work that way. Every time Rust is mentioned, every C fan finds a million reasons why the improvement doesn't actually have anything to do with using Rust.

AI/LLM discussions are the exact same. How would a person ever measure their own performance? The moment you implement the same feature twice, you're already reusing learnings from the first run.

So, the only thing left is anecdotal evidence. It makes sense that on both sides people might be a little peeved or incredulous about the others claims. It doesn't help that both sides (though mostly AI fans) have very rabid supporters that will just make up shit (like AGI, or the water usage).

Imho, the biggest part missing from these anecdotes is exactly what you're using, what you're doing, and what baseline you're comparing it to. For example, using Claude Code in a typical, modern, decently well architected Spring app to add a bunch of straight forward CRUD operations for a new entity works absolutely flawlessly, compared to a junior or even medior(medium?) dev.

Copy pasting code into an online chat for a novel problem, in an untyped, rare language, with only basic instructions and no way for the chat to run it, will basically never work.