How do you measure the performance of:
Junior-level devs - by the number of lines of codes
Senior-level devs - by the number of calls using reusable functions
Expert-level devs - by the number of lines deleted
By this metric, A.I. is expert-level 
I know this is a HN post, but on the use of the word hallucinations … this is worth considering:
Keeping in mind 'ChatGPT' is a slang term by now, referring to all LLMs, like 'Xerox' once was for photocopies
Abstract from PDF
Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.
Most of these issues ( as the comments seem to indirectly state ) seem to be context issues, usually. I have been seeing beautiful code vandalized purely by not managing context. Sometimes the same LLM will both generate and deface the same code segment if not carefully worked with, as if it were bonsai tree trimming
“AI Halucinations” / bullshitting was one of the reasons why we said that AI won’t replace you in the latest TWIM. As said in the extract, the LLMs are more concerned about pleasing you rather than the truth.