Most teams treat AI-augmented development as a tooling upgrade: same process, same roles, same QA — just with an assistant in the editor. That is the smallest possible version of the change, and it leaves most of the value on the table.
When building gets dramatically faster and cheaper, the bottleneck moves. It is no longer typing the code. It is deciding what to build and trusting that what was built is correct. Both of those sit outside the editor, and both have to be redesigned.
Conceiving features differently
When a prototype costs an afternoon instead of a sprint, you stop writing exhaustive specs and start validating ideas in running software. Feature conception becomes faster, more experimental, and more disposable at the edges — which is a different muscle than the requirements-first habits most teams carry.
Quality engineering, rebuilt
This is the part that surprises people. Machine-generated code fails differently from human-written code: it is confidently plausible, often subtly wrong, and produced far faster than traditional review can keep up with. So quality engineering cannot be the old manual gate at the end. It has to be redesigned around catching the specific failure modes of AI output, at the speed AI produces it.
Get this right and AI velocity compounds. Get it wrong and you ship plausible-looking defects faster than ever. The difference is not the tool. It is the lifecycle around it.