I think your experience matches well with mine. There are certain workloads and use cases where these tools really do well and legitimately save time; these tend to be more concise tasks and well defined with good context from which to draw from. The wrong tasking and the results can be pretty bad and a time sink.

I think the difficulty is exercising the judgement to know where that productive boundary sits. That's more difficult than it sounds because we're not use to adjudicating machine reasoning which can appear human-like ... So we tend to treat it like a human which is, of course, an error.

I find ChatGPT excellent for writing scripts in obscure scripting languages - AppleScript, Adobe Cloud products, IntelliJ plugin development, LibreOffice, and others.

All of these have a non-trivial learning curve and/or poor and patchy docs.

I could master all of these the hard way, but it would be a huge and not very productive time sink. It's much easier to tell a machine what I want and iterate with error reports if it doesn't solve my problem immediately.

So is this AGI? It's not self-training. But it is smart enough to search docs and examples and pull them together into code that solves a problem. It clearly "knows" far more than I do in this particular domain, and works much faster.

So I am very clearly getting real value from it. And there's a multiplier effect, because it's now possible to imagine automating processes that weren't possible before, and glue together custom franken-workflows that link supposedly incompatible systems and save huge amounts of time.

My thoughts as well, good at somethings and terrible for somethings and you will lose time.

Somethings are best written by yourself.

And this is with the mighty claude opus 4.5