The confusion comes from conflating "repetitive in structure" with "repetitive in specifics."

Traditional automation handles tasks that are repetitive in specifics - the exact same input produces the exact same output every time. Press this button, get that result.

What AI handles is tasks that are repetitive in structure but variable in specifics. "Review this pull request" is structurally the same every time (read code, check for issues, suggest improvements), but the actual code and context varies. You can't write a script for it because the inputs are too variable, but it's not intellectually novel work either.

Same with writing boilerplate. The pattern is repetitive (create CRUD endpoint, add validation, wire up to database), but the specifics change each time (different fields, different validation rules, different table schemas). Traditional code generation works when you can parameterize everything upfront. AI works when the parameters are implicit in context you'd have to explain to another developer anyway.

The real threshold isn't repetitive vs. novel. It's "can the task be fully specified with formal rules" vs. "does it require judgment calls based on fuzzy context." AI is good at the latter in ways traditional automation isn't.

You _could_ write non-AI tools for those examples. That's basically what sonarqube, coverity, lint, etc... are. They might even do a better job, with better correctness and deterministic results.

There are also plenty of code generation / templating tools available. Maybe there are other tasks where AI offers novel capabilities, like interacting with humans, but at least for those two examples I think a lot of people are really just distracted by the novelty of AI, and perceiving the results of non-deterministic / black box system as "magic" or "creative".