I think you nailed it. In the grand scheme of things, critical thinking is always required.

The problem is that, when it comes to LLMs, people seem to use magical thinking instead. I’m not an artist, so I oohd and aahd at some of the AI art I got to see, especially in the early days, when we weren’t flooded with all this AI slop. But when I saw the coding shit it spewed? Thanks, I’ll pass.

The only legit use of AI in my field that I know of is an unit test generator, where tests were measured for stability and code coverage increase before being submitted to dev approval. But actual non-trivial production grade code? Hell no.

You know, I was happy to dig through 9yo StackOverflow posts and adapt answers to my needs, because at least those examples did work for somebody. LLMs for me are just glorified autocorrect functions, and I treat them as such.

A colleague of mine had a recent experience with Copilot hallucinating a few Python functions that looked legit, ran without issue and did fuck all. We figured it out on testing, but boy was that a wake up call (colleague in question has what you might call an early adopter mindset).

A 100% accurate AI would be useful. A 99.999% accurate AI is in fact useless, because of the damage that one miss might do.

It’s like the French say: Add one drop of wine in a barrel of sewage and you get sewage. Add one drop of sewage in a barrel of wine and you get sewage.