AI coding assistants are trained on huge amounts of public code and documentation, which lets them suggest whole functions, catch obvious bugs, and translate code between languages. For repetitive, well-documented tasks — boilerplate, common algorithms, test scaffolding — they can save real time.
Where they struggle is context that lives outside the code: business rules, architectural decisions, and the reasons a system was built a certain way. A suggestion can be syntactically perfect and still be the wrong solution for your specific codebase, which is why review and understanding still matter.
The realistic shift isn't "AI writes the software." It's that a larger share of a developer's time moves from typing syntax to reviewing, specifying, and testing — skills that were always part of the job, just less visible than the code itself.