Technical Context
I latched onto this case not because of the catchy name but because I already see the same mechanics live: first a "naive" assembly via AI automation, then an engineering refinement. Not as a theory, but as a real pipeline.
The pattern is simple and very recognizable. People not necessarily from development take Cursor, GitHub, an agent on top of Gemini or another model, throw together a PRD, slice the task into vertical pieces, and get a working prototype. It passes the happy path, looks convincing, and sometimes even reaches the first users.
This is where many confuse "works" with "done". I have seen many times how artificial intelligence integration beautifully assembles UI, CRUD, basic APIs, and connections to external services, but fails on access rights, idempotency, rate limits, logging, and migrations.
That is precisely why the second half of the pattern is more important than it seems at first. SRE or strong platform/backend engineers come in not just to "fix a few bugs" but to rebuild reliability from scratch: monitoring, secret management, rollback, alerts, test environment, CI/CD, basic threat model. And yes, sometimes after such an audit, half of the generated code is easier to rewrite calmly than to heroically patch.
At the same time, I like the pattern itself. I wouldn't call it a new profession, but as a blueprint for AI implementation in teams it already looks quite viable.
Impact on Business and Automation
For business, the win is obvious in two places. First: an idea turns into an interface and scenario in days, not months of coordination. Second: engineers stop spending the initial weeks on empty scaffolding and plug in where their time is truly expensive.
The losers are teams that decide the SRE phase is optional. Then you get classic vibe coding: a demo exists, operations don't.
I would embed such a model right into the process architecture: fast prototype, strict handoff, then hardening by checklist. At Nahornyi AI Lab, we solve exactly these joints for clients: where to keep speed, and where to build a proper AI solutions architecture without illusions, so the product doesn't die after the first success. If you already have a pile of chaotic AI prototypes, I and my team can help turn them into a working system, not an expensive collection of demos.