One talk, that was quite interesting, was from a company with 180 developers that did a slow and careful rollout of AI, mainly due to high demand. They did a self-assessment, and developers claimed increased productivity. They said it was too early to say if it was actually true though, and promised to come back with results (presumably another year) if they had any.

It's the same where I work. Developers *feel* more productive, but I have yet to see any numbers backing it up.

@veronica I have looked at two AI tools, one is a simple line completion model that guesses at what I'm about to type. It's a simple model that runs locally and is right about 80% of the time. Overall I think it offers a benefit.

The other is a big model that tries to figure out the next few lines, and even after letting it at a code base for some time, the guesses are wildly inaccurate, and mess with keystrokes, so I wind up having that inaccurate code injected, which means I then need to remove it before typing what I was already planning to type. This has lowered my productivity. I have used this model to generate test cases; about 80% of the cases made sense, the rest needed minor tweaks, so there's a potential use case there.

I will try getting an advanced model to build a small application, but at this point I expect I'll spend as much time fixing the generated code as it would have taken to write it in the first place.

One place where it might be useful is generating code in a language I am familiar with but haven't mastered. At that point the logic would still be auditable, and I could use that review to raise my level of expertise.