Perhaps your supposedly unique work is more repetitive than you thought: it just has a decision tree that's difficult to model with a regular algorithm, and annoyingly, it turns out you can just brute force that decision tree if you have enough electricity.

Unless your job is cutting-edge research where you are truly making new scientific discoveries and methods, you're just combining other peoples' ideas into a new unique package and selling it.

The truly valuable work is to notice that there is an underserved market and figure out how to meet their needs.

I am generally skeptical about AI but I do see the benefit here.

I write a bunch of widgets for my website. They're little calculators that use common components and apply simple logic. Think unit conversion or date arithmetic.

These currently take a few hours to write, and most of the work is just wiring things together in a predictable way: template, tests, common form controls.

I think that this would be a very good case for AI.

Seeing a lot of claims about using AI to write "boilerplate" and other repetitive bits of code, I was somewhat surprised, as I have historically written my own code generation tools to spit out repetitive, formulaic code. I didn't need AI; I just needed to understand what I wanted and write a script for it.

I suppose that generative AI was seen as such a boon to writing boilerplate because it could do so without you having to specifically program anything; it was trained on enough sufficiently-close examples that it could pull it off without a thorough description.