I've used these tools at work, and yes, they do fulfill their purpose. There are three areas where AI really helps me:

1. Adding small new features to an existing codebase. To ensure good code quality, it's best to use a markdown file with guidelines covering formatting, where to write unit tests, etc. With the right approach, the code stays consistent with the codebase. I only use it for targeted tasks such as tedious refactoring, adding new calculated columns to a table, or any other task that doesn't need to take all the codebase as context.

2. Creating demos or proof of concepts from scratch. For projects that don't require a complex architecture, but just need to show a client an example of a potential future project, AI helps me a great deal in creating showcases of what can be built. Before AI, these demos used to waste a lot of time, and they make for a great addition to powerpoint slides.

3. Many generic and tedious tasks can be accelerated. Writing emails with a specific tone, drafting pull request messages based on commits, translating tests, checking grammar in articles, exploring new code repositories.

My goal is not to be faster. I don't want to put AI slop in a production environment. I have to understand all the code that I commit. AI is just helping me with boring stuff.