> While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc.

I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment).

The real failure mode is letting AI flood the repo with half-baked abstractions without a playbook. It's helpful to have the model review the existing code and plan out the approach before writing any new code.

The leverage may be in using LLMs more systematically across the lifecycle, including the grunt work the author says remains human-only.