A lot of arguments about AI-assisted development collapse three different questions into one: Who or what generated the implementation? How much faster was the implementation produced? Does the resulting system meet an engineering quality bar? Those are related, but they are not interchangeable. AI can increase implementation throughput while decreasing average quality if verification does not scale with it. That is a legitimate concern. But “generated by AI” is not itself a measurement of correctness, security, maintainability or usefulness. The stronger model is to treat generative coding systems as high-throughput, error-prone producers operating inside an engineering control loop. As generation cost falls, the scarce functions become: - specification - architecture - decomposition - constraints - test design - security review - observability - failure analysis - regression control - final accountability This suggests a labor shift rather than a simple replacement story. Experienced engineers who adopt the tools may become substantially more productive because their prior knowledge lets them detect bad output and set better constraints. Less experienced users may gain the ability to create systems they could not previously create, while also being exposed to failure modes they cannot recognize. That is why “AI slop” is sometimes accurate but often analytically useless. It names the origin of the artifact instead of the failure mechanism. A better debate would be: what verification stack is required before AI-generated implementation deserves the same trust as conventionally authored implementation? submitted by /u/OGMYT
Originally posted by u/OGMYT on r/ArtificialInteligence