Almost half (about 41%) of all the code in the world is created using artificial intelligence. The new fashion for programming with AI is called vibe coding.
Vibe coding is a type of development in which a person formulates ideas and checks the result more than he writes code manually.
The speed is really impressive! Teams report a reduction in task completion time of more than 50%, and small teams of 2-5 developers — up to 68%. But with the speed comes a new problem.
AI can quickly generate individual parts of a system, but it does not always understand the architectural context, the long-term consequences of decisions, and the specifics of product scaling. Therefore, in the era of AI development, the role of Senior Engineers is becoming important, and often critically necessary.
In this article, we'll figure out why even the most advanced AI does not eliminate the need for code review by Senior developers.
The material is based on statistical and analytical data from Second Talent, a platform for research on the labor market and technological professions, as well as SHIFTMAG, an international publication on technology, innovation, and digital business transformation.
The data used reflects current trends in AI development, automation, and the use of generative AI in software development.
Today, vibe coding is actively used not only by developers. It is estimated that about 63% of users of AI code generation tools have no professional programming experience. Entrepreneurs, marketers, designers, analysts, and product managers independently create landing pages, interfaces, internal services, and even full-fledged web applications.
The popularity of the approach continues to grow. It is predicted that by 2026, up to 40% of new SaaS MVPs will be created primarily using vibe coding. This allows you to test hypotheses faster, launch prototypes, and reduce development costs in the early stages of a project.
However, creating a working product is only the first step. Support, scaling, security, and long-term development of the system still require the participation of experienced engineers and high-quality technical expertise.
Vibe coding is most actively implemented by startups and digital companies, for which the speed of development is a competitive advantage.
|
Branch |
% of using vibe coding |
Basic scenarios |
|
Technology startups |
73% |
MVP, prototyping, launching new features |
|
Digital agencies |
61% |
Client projects, web development |
|
E-commerce |
57% |
Integration, A/B testing |
|
The financial sector |
34% |
Internal tools are under strict control |
|
Healthcare |
28% |
Administrative systems, documentation |
Companies from the financial sector and healthcare approach AI development much more cautiously. Mistakes are too expensive here. Any code must comply with security requirements, regulatory standards, and undergo additional checks before launch.
AI does an excellent job of generating individual components. At the first stages, such code may look high-quality and fully functional. Problems begin to appear later, as the load increases, new features become available, third—party services integrate, or the system scales.
The State of Vibe Coding 2026 study found that the AI-generated code contains 1.7 times more serious errors and almost 3 times more security vulnerabilities compared to code written manually by experienced engineers.
40% of junior developers admit that they have at least once sent AI-generated code to production that they did not fully understand. And in a survey of 18 CTOs by the end of 2025, 16 of them reported production disasters directly caused by AI-generated code.
The main hidden threat of vibe coding is not bugs and invulnerability, but architectural debt. AI makes hundreds of quiet decisions: which error handling strategy to choose, which serialization format to use, and how to organize streams. These solutions are not documented anywhere — they are just in the code.
When a developer cannot explain how a specific part of the system works, product maintenance becomes much more difficult. The code may work, but the team doesn't understand why it works the way it does or where it might break. As a result, developers spend more time debugging AI-generated code than they expected.
While juniors are increasingly acting as operators of AI tools and authors of projects, seniors are responsible for verifying, cleaning, and bringing the generated code to standards of quality, security, and scalability.
Artificial intelligence works like smart support: it responds quickly, generates blanks, and explains an unfamiliar stack. But the person sets the direction — and he is also responsible for the result.
Let's give an example: our backend developer used AI as a partner when migrating from OpenSearch to TypeSense in an unfamiliar stack and in a short time. AI performed in several roles at once:
Iteration after iteration — to analyze the logic, get an option, test, find a discrepancy, clarify the question. Without AI, the task would not have been completed on time. But without a developer who understood the basic principles and noticed when the AI was wrong, too.
Anton, our front-end developer, used AI as an accelerator for entering an unfamiliar stack: he needed to write a child's game in PhaserJS, which he had never worked with.
AI helped to understand the physics of objects, scenes, and collisions — through specific questions and explanations of logic, not just code examples. But when it came to the specifics of the scene, AI offered working options that did not take into account the context of the project. I had to figure it out myself.
Both situations are about the same thing: AI lowers the entry threshold and saves time — but only if it's in the hands of a person who understands what's going on inside.
Senior developers get the most out of AI tools precisely because they know how to consciously evaluate the result. They use neural networks to automate routine tasks, while they focus on architecture, security, and technical decision-making.
Experienced engineers with 3+ years of experience report a 40-50% increase in productivity when working with AI — against unstable jun results.
Today, engineers who are able to use AI effectively, but at the same time are able to independently evaluate the result of its work, are especially in demand.
Most developers use AI for prototyping and experimentation.
Each AI-generated block must undergo at least one meaningful edit on the part of the person before the merge. It doesn't have to be a simple renaming of a variable — real refinement is needed: adding an edge-case, changing the error handling logic, or choosing a different approach to the solution.
The very fact of changing the code forces the developer to figure out how it works and really understand it.
If a team actively uses web coding, but it does not have an experienced engineer who can take on review and architectural control, this is a risk. Outstaffing Senior developers can help here. Experienced engineers join the project quickly, without long hiring and onboarding, and immediately complete the tasks of business and quality control — leaving the speed of AI development, but adding expertise and responsibility for the result.
No, AI is changing the format of work rather than removing the developer's role. He takes over the routine, but is not responsible for the architecture, risks, and final quality of the product.
It works best where there is repeatability and a clear structure: generating boilerplate code, writing draft solutions, helping with unfamiliar libraries, or speeding up prototyping.
Because they often look correct on the surface, but they only manifest themselves in complex scenarios — under load, integration, or system changes, when it is already more expensive and more difficult to fix them.
