It's being used for many purposes. But also, let's be clear on the meaning of AI

There's the original, academic computer science meaning, which is "computer systems which replicate human decision making", which includes everything from chatbots and content generation tools to rules-based spell-checkers and Akinator.

There's the current layman's hype term which means LLMs and content generation tools.

There's the current marketing term which means almost nothing, because everything from the scheduler in your CPU to actuarial models in insurance to recommenders to chatbots to... anything marketers think they can get away with.

And there's pop-culture AI, which basically just means "(super)human with metal instead of meat"

So going through one by one

CompSci AI: not necessarily, it's a really mixed bag, thought to say because it contains many many things. Are there bad and harmful applications of this? Yes, many. Are there good and positive applications? Yes, many. This world has both ML-supported protein folding simulations to improve medicine, it also has mass-scale generation of racially charged propaganda.

Layman's hype: yes, vastly more bad than good, should be robustly challenged and limited until the economy can find a way of surviving the need for the last bastion of human labour being undermined without causing mass starvation and homelessness etc. It's also making people dumber, while simultaneously making it more difficult to navigate important spheres like politics. People are turning to chatbots to replace genuine human connection or even healthcare, and it's already quite literally killing people.

Marketing: too mixed to say anything. Many examples of both good and bad, but mostly just dumb.

Pop culture: doesn't yet exist, and won't exist with transformer-based models. Chatbots are good at coming up with language given context, and their training data includes a lot of stories and roleplays and all sorts of content in which the dialogue and supposed internal experience of a conscious AI is discussed, so it's trivially easy for a dumb language model to produce language which regurgitates that which authors think a sapient computer would use, coming across convincingly in the process. Transformers are at their limit for the size of their networks and available training data, only a new kind of architecture will be able to take the next step towards computerised sapience.

Overall, we've been sleepwalking into a total disaster when it comes to this sort of technology, with some more niche positives along for the ride.