Over the past two years, the way we write code has changed more than in the previous decade. Instead of typing every line by hand, a developer describes the task in plain words and AI generates working code. The approach came to be called vibe coding, and it is no longer an enthusiast's experiment but an everyday practice. As of 2026, somewhere between 41 and 46 percent of new production code is written with AI involved, and nine out of ten developers use these tools on a regular basis.
The appeal is obvious. A prototype that used to take a week now appears in a day. Routine work, the glue code, the tests, the standard logic, takes less time, and the team frees up its head for the harder parts. For the business it means a shorter path from idea to a working version, and therefore a faster way to test assumptions. That is exactly why the market for these tools is growing at a pace IT has not seen in a long time.
Where the Limits Begin
The catch is that speed and reliability are not the same thing. Code generated by AI looks plausible even when it is wrong. It compiles, it passes a quick glance, and the bug surfaces later in production. Studies show that after teams rolled out AI assistants at scale, bug rates in some of them went up, and technical debt piles up faster, because code gets duplicated more often than it gets refactored cleanly.
Security is a story of its own. A large share of generated code fails basic vulnerability checks, and the tool will happily pull in outdated libraries or dependencies that do not even exist unless you stop it. For an internal prototype that is a trifle. For a product that handles customer data it is a risk the company ends up paying for.
There is a less obvious point too. When an experienced engineer works on a familiar codebase, AI does not always speed them up. In one study developers felt more productive with AI, yet actually spent more time, because they kept checking and fixing what was generated. The feeling of speed and the real speed drifted apart.
What It Means in Practice
Vibe coding is worth neither worshipping nor dismissing. It is a powerful tool for one class of tasks and a poor choice for another. Where the cost of a mistake is low, prototypes, internal tools, one-off scripts, it saves days. Where the code ships to production and touches money, data or security, it stays an assistant rather than an author. The decision and the responsibility behind it still rest with a person.
So in mature teams what changes is not the volume of an engineer's work but its nature. Less manual typing, more reviewing, architectural decisions and the context AI cannot see yet. Code review turns from a formality into a real quality filter instead of a checkbox before the merge. And that is where the line runs. Vibe coding removes the routine, but it does not remove the need to understand what you are sending to production.
For us this is not a reason to fear change but a reason to build processes where the speed of AI meets engineering discipline. The winners are not those who write the most code the fastest, but those who can tell where to trust the machine and where to stop and think.