If you are getting deskilled using LLMs, it's your usage pattern that's to blame

I'm not speaking of myself, here...

Some companies are releasing code that nobody on the owning team has ever read or reviewed, or has any inkling exists.

That sounds pretty irresponsible, I can't imagine serious orgs doing that without custom governance / approval software in place

I work for big tech. We don’t have human in the loop anymore. Ai tools handle 99%. It’s not irresponsible if done right. And the next billionaire will show that ai can be tamed, managed, and humans are the problem.

It’s irresponsible.

By what measure?

For one, your company ought to publish its Scope 1, Scope 2, and Scope 3 carbon emissions for all to see. If you understand how the electricity is produced for the AI data centers your company uses, you’ll see that replacing the humans with natural gas guzzling AI workflows is a dramatic rape on the environment and health of species/ecosystems. But what I say doesn’t matter because all tech is in on AI and it’s a race to the bottom for the almighty dollar.

Depends on which data centers. They aren’t all equal. Some use less water and less energy than others. The data centers being more efficient isn’t the company using the service problem.

That’s what they say about communism and anarchism and many other isms: if it’s not working, you aren’t doing it right. Every failed implementation gets the No True Scotsman argument.

What are you trying to say here? That no-one can ever be accountable for using tools wrong? Always blame the tool, or it's "no true scotsman"?

I'm not sure you've quite thought this through. "Holding it wrong" is very, very often the case, especially with technology.

Youre holding it wrong bro, as they say.

This is certainly what I saw. I was at a company that was a very eager early adopter of GenAI for all work. One big thing that came out of it was a bunch of effort wasted on unprofitable projects.

The chatbots kept steering people toward uncreative ideas and overshadowing novel insights that might come from genuine domain expertise. AI generated meeting summaries became a force for miscommunication as they de-emphasized or even outright misrepresented what was really important about the conversation. And people became much worse at proving ideas out before making significant resource commitments thanks to effects that we now describe with terms like AI psychosis.

If people are in charge of project management, and repeatedly fail to recognise poor ideas and instead pursue them to the detriment of productivity, then they probably shouldn't be managing that project.

All AI is doing here is rapidly identifying the people who have Peter-principled themselves.

> It's a lot easier for a human to organically have good ideas than to refute LLM-generated ideas that are a dead end.

What works better is when you can use an LLM to obtain a negative opinion on your own ideas that you think are good. LLMs aren't great at original thinking, but they're pretty good at finding the flaws in poorly thought-through human ideas.