Technical Context
I took a look at the whitepaper The New SDLC With Vibe Coding on Kaggle, and one thing I liked: the authors aren’t selling magic. They plainly state that AI implementation compresses the development cycle unevenly. You can generate code in hours, but requirements, architecture, and verification don’t disappear.
Simply put, their vibe coding is a mode where you express intent in natural language and quickly get a working prototype. Not a 20-page spec, but a dialogue with the model. For drafts, internal tools, and initial interface versions, this is a truly workable approach.
But then it gets interesting. In the whitepaper, vibe coding sits on a spectrum, and the next stage is agentic engineering: specifications, tests, verification, structured context, deployment loops. And here I fully agree with the authors: without this, any beautiful demo project quickly turns into an expensive pile of random code.
Another strong point: AI compresses implementation the most. The authors write that the journey from idea to prototype can take minutes, and tasks that once dragged on for weeks can drop to hours. Yet they honestly warn that pure vibe coding can cost 3-10 times more per feature if you later have to deal with maintenance, security, and token consumption.
I see the same in my own experiments. As long as the task is small and isolated, a natural-language workflow flies. As soon as you need to maintain context across services, APIs, roles, logging, and access control, without proper AI architecture everything starts to shake.
Impact on Business and Automation
The business takeaway is very grounded. Prototyping accelerates dramatically: you can validate an idea in a day that previously required a team for a week. This is a good opportunity for AI integration into internal processes where the cost of failure is low.
Those who think they can now skip architecture and testing will lose. In the short term it's cheap, but in the long term you get expensive technical debt, unnecessary token spend, and security issues.
The winners are teams that separate the modes: vibe coding for fast hypotheses, agentic engineering for production. At Nahornyi AI Lab, we solve exactly these transitions for clients: where to keep speed, and where to build a system so that automation with AI doesn’t create chaos. If your ideas are now hitting bottlenecks not in code but in misaligned processes, you can calmly unpack the workflow and assemble an AI solution around it without extra noise, together with Vadym Nahornyi and Nahornyi AI Lab.