I'll take this a step further:
most of the AI proponents I see - especially with social platforms - don't understand the methods, theories, debates, and philosophies of machine learning either (aka - the three raccoons raccoons tech companies are shoving into a trenchcoat and calling AI).
because of those constraints, good ML is rarely power or compute hungry at anything like the scale of "AI".
high compute is usually restricted to development, maintenance, or targeted runs limited people or orgs (the audience / consumers of the tool).
they're happily dragging a genuinely powerful and valuable discipline and collection of tools into the mud to break things and make money.
and they either understand ML enough to know it and they don't care, or they're ignorant (and probably don't care or can't see it).
and when does it sometimes work kinda ok?
when it's used by experts on their area of expertise...
esp if the use is focused on (relatively) structured and context-insensitive inputs (eg, code)...
and esp if developers of AI tend to be familiar with or sensitive to those use cases (eg, CODE).
put together, it encourages ongoing and at scale compute to try to get a non-specialized tool to do stuff it wasn't really designed for (since it wasn't designed for much in particular).
solve-anything AI is built on tools and methods that are inherently NOT meant to be solve-anything.
it's built at a scale and speed that makes the needed expert curation, review, and context understanding impossible.
and it is presented confidently to a general audience, with fig leaves for attempts to educate and convey limits (at best, usually just enough to limit liability; at worst, marketing to make AI seem like more than it is).
good ML:
* carefully designed for narrow contexts (or with awareness of context limits)
* designed by ML and topical experts, with expert curated data and expert reviewed outputs
* designed for specific needs and consumers, and kept up to date for both
* clear training / comms on use and limits
in other words: AI works best when it... is developed the way ML is meant to be developed, lol.
which could be achieved with much more focused, less expensive, and more accurate ML solutions.