Is Vibe Coding Bad? Risks Everyone Should Know

What is Vibe Coding?

In short, vibe coding is a method of creating software using various AI tools. The term was first used by Andrej Karpathy in February 2025 and immediately became a trend. You describe a task for the LLM, and it generates code based on your request. It seems like a great way to create perfectly functioning software and save on development costs. However, this method only works for small projects. Want something more serious? Well, you've come to the wrong place.

The Advantages of Vibe Coding

There are several advantages to vibe coding that are hard to ignore. We will list them based on our personal experience:

• Prototyping

  AI can quickly generate a prototype that takes all your wishes into account, which you can then quickly present to the client.

• Easy writing of UI components and UI-kit

  Many modern AI solutions, such as Figma make, lovable, and v0 simplify the process of working with UI components.

• Tab feature

  It allows you to significantly save time when working with code, thanks to autocomplete.

• Unit testing

  It is really easy to check the correctness of the written code using AI.

The Disadvantages of Vibe Coding

There are many more problems when developing with AI than might appear at first glance:

• Bad for scalability

  If the project has and want to expand its functionality over time, you may encounter many difficulties, as you will need to completely rebuild it from scratch.

• Expectations from the prototype

  Wait, didn't we mention this in the previous section? Actually, yes, but there is one thing to keep in mind. As soon as the client sees the wonderful prototype completed in two days, they want it implemented as soon as possible. And sometimes it is difficult to explain why coding takes much longer than creating a prototype. Especially if not all the features generated with AI will be as easy to implement in the future.

• Coding from scratch

  This is where the biggest problem with vibe coding lies. If you don't guide and double-check it, there is a high probability that the application may simply not work during production. This leads to the following problems:

• Time spent on testing

  Sometimes it is easier to write the code yourself than to create a bunch of prompts and correct every wrong step.

• Poor optimization of program code

  AI is not very good at this. Sometimes, where 4,000 lines would be enough, it can generate 30,000 lines using its own logic. And then it starts -

  

• Debugging

  Here's the most interesting part — fixing bugs for AI is a real nightmare. Any developer would rather write the code from scratch than try to figure out what the AI has generated based on logic that no one understands. Since someone must verify the code's functionality, AI debugging services have become increasingly common, albeit at very high prices.

• Limits of AI models with bigger codebases

  Vibe coding breaks down when working with large code bases, where architectural decisions are paramount. LLM does not adhere to the logic of general construction, disregarding the compatibility of previously built functions with future modules. Dependencies become disordered, unnoticeable errors appear, and the lack of human-level thinking about system design worsens over time.

• Security risks

  AI may not take into account all security risks. This can be a serious problem if you collect user data or handle payments.

• Integrations don’t work correctly

  Several integrations may break because LLM may not understand how to combine them, especially in scalable projects.

• Hallucinations

  All AI suffer from the same problem—they start inventing non-existent methods to please you as a user. If you lack coding expertise, you may not understand the error until the launch stage.

• Decomposing tasks takes a long time

  LLMs often encounter difficulties in decomposing complex tasks or systems into smaller, more manageable units.

• Hard to implement in DevOps

  Poor code optimization has a detrimental effect on DevOps. Tokens can be mixed up due to the large code, and dependencies are not always determined.

• May break SEO

  If you don't carefully check all the conditions of the site's operation, SEO can suffer seriously. For instance, we encountered a case where AI-generated code rendered the site on the client side rather than the server side.