I'll believe it when I see it.

Genuine question, I have not followed this topic closely for years :)

Not only is it difficult to verify, but also the knowledge your team had of your messy codebase is now mostly gone. I would argue there is value in knowing your codebase and that you can't have the same level of understanding with AI generated code vs yours.

Maybe this doesn't apply to your case, but how would you measure outputs of say product development, or any data related project. Lot's of things don't have a good measure of output before the thing is done. Maybe your product / analysis improves profitability by 10x or maybe it was a flop and lost money.

Tangential, but I'm also seeing the quality of measures going down, with AI it seems that the number of [emails|code|analysis] produced is again a good measure.

I vividly remember understanding how calculus works after watching some 3blue1brown videos on youtube, but once I looked at some exercises I quickly realized I was not able to solve them.

Similar thing happens with LLMs and programming. Sure I understand the code but I'm not intimately familiar with it like if I programmed it "old school".

So yes, I do learn more but I can't shake the feeling that there is some dunning kruger effect going on. In essence I think that "banging my head against the wall" while learning is a key part of the learning process. Or maybe it's just me :D

True, but also there are perception biases that lead us to believe progress is exponential, even though it might as well be an S-curve.

I'm having a hard time finding the right terms, but I'm sure there is some bias to think that "the line goes up".

Surely I could do a mediocre job as a CxO by parroting whatever is hot on Linkedin. Probably wouldn't be a massively successful one, but good enough to survive 2 years and have millions in the bank for that, or get fired and get a golden parachute.

(half) joking - most likely I'm massively trivializing the role.

I have some hobby sites I host on a VM and currently I use docker-compose mainly because it's so "easy" to just ssh into the machine and run something like "git pull && docker-compose up" and I can have whatever services + reverse proxy running.

If I were to sum up the requirements it would be to run one command, either it succeeds or fails in it's entirety, minimal to no risk of messing up the env during deployment.

Nix seems interesting but I don't know how it compares (yet to take a good look at it).

Tangential, I don't even know what "responsible" in the corporate world means anymore, it seems to me no one is really responsible for anything. But the one thing that's almost certain is that I will fix the damn thing if I made it go boom.

If I can just vibe and shrug when someone asks why production is down globally then I'm sure the amount of features I can push out increases, but if I am still expected to understand and fix the systems I generate, I'm not convinced it's actually faster to vibe and then try to understand what's going on rather than thinking and writing.

In my experience the more I delegate to AI, the less I understand the results. The "slowness and thinking" might just be a feature not a bug, at times I feel that AI was simply the final straw that finally gave the nudge to lower standards.

And at the same time it's impossible to convince tech illiterate people that reducing complexity likely increases velocity.

Seemingly we only get budget to add, never to remove. Also for silver bullets, if Big Tech promises a [thing] you can pay for that magically resolves all your issues, management seems enchanted and throws money at it.

No takers yet.

I still don't use it (AI) for the game programming as it sucks the joy out of it for me. Especially when AI usage is currently being pushed hard at work.

That's only marginally sped up even if you could generate the code with a click of a button.

This was somehow related to the "social activity" part :D

Professionally I have an agent generating most code, but if I tell the AI what to do, I guide it when it makes mistakes (which it does), can we really say "AI writes my code".

Still a very useful tool for sure!

Also, I don't actually know if I'm more productive than before AI, I would say yes but mostly because I'm less likely to procrastinate now as tasks don't _feel_ as big with the typing help.

This also depends on your definition on "smartest".

I'm sure there are outliers, but this seems to be the norm.