We're doomed, when even supposed experts, working on these "AI" tools can't help but anthropomorphize those tools.

"when edge cases emerge that the AI didn't anticipate"

The only "anticipation" that is happening within those tools is on token level, the tools have no idea (and are fundamentally unable to even have an idea) what the code is even supposed to do in the real world.

So if you query the model to implement a function and it handles a case YOU didn't anticipate, what actually happened there? What word would you use?

>The only "anticipation" that is happening within those tools is on token level

When you were composing your reply, did you just start typing, then edit and compose your thoughts better a few times before hitting the reply button?

I ask, because that's what I do. Most of the time, I never know what the next word is going to be, I just start typing. Sometimes I'll think it out, or even type out a whole screed until I run out of thoughts... then review it several times before hitting "reply".

By your logic, I'm no more advanced than any other LLM. I think there's a serious misunderstanding of the depths at which the internal state of the LLM is maintained across token outputs. It's just doing the same thing I do (and I suspect most other people do, which is decide then make up a convincing story that agrees with the decision, on a word by word basis)

Other times, when I'm trying to explain something technical or complex, there's a word, or a name I can't remember... it drives me nuts, if I'm in a hurry, I'll just use a synonym that's almost as good, and work around it. Yesterday, for example, it took a while to I remember the name Christopher Walken via the Fat Boy slim video on YouTube.

The only difference is we have the ability to edit it first, before the all powerful "reply" button. Then of course, there's edit... but that's like agentic LLMs.

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