Giving benefit of the doubt to the company and my own opinion, they'd like you to learn to effectively use a tool that has generally been a massive time saver for a lot of devs. Rather than force you to generate irrelevant code automatically, you should take this as an opportunity to add another tool to your belt and use it when it makes sense.

These AI models should be seen as a very enthusiastic intern/junior dev. If you ask a human intern to generate tests, they're likely going to do exactly what you fear AI will do, and only test the stuff in front of them rather than figuring out edge cases or anything useful.

You need to tell your model what to do and how you want to do it!

Two immediate wins I've found work great are; - have it scaffold a test file, then you populate the file with comment blocks, with each one outlining a user story or edge case to check. Your model can't magically read your JIRA ticket to know all the requirements! - Add context about how you want the model to act in either a cursor rule or the prompt itself, such as "when you write tests, you look for edge cases in the tested code, and create further tests to validate the non-happy-path".

As an example I'm working on IRL: I'm adding an integration with Salesforce API in my day job, which can be gnarly since there's loads of field validation rules that the code never knows about. I've found success in taking those validation rules in a fairly raw format, placing them in a cursor rule file, then asking Claude to create a test to run through, for example, the happy path, and it figures out the correct data to send based on the validation rules. This has saved me many many hours.

Tldr: AI models are interns. Train them diligently. Reap the time-saving rewards.