Vibe coding: When AI becomes your junior developer

With AI everywhere in development environments worldwide, developers today are looking to “vibe coding” to save time and effort. Despite entering the AI era, the old development principles still matter.

It sounds almost too good to be true: You describe what you want, and the AI writes the code for you, not as an assistant but as a junior developer improvising their way forward. This is the essence of vibe coding, and it's rapidly gaining traction as LLMs and AIs become better, faster, and more accessible.

The term Vibe coding was coined by Andrej Karpathy, an AI expert who wanted to describe a new method of using AI support to generate code. The methodology began in the early 2010s, when the first generative AI tools emerged. Though more experimental than practical at the time, the technology's potential was already clear.

By 2022, generating code through natural conversation with AI became possible, thanks to the launch of tools like OpenAI’s Codex and GitHub Copilot—the first to market. What started as a code-by-line reference tool has evolved into something different with vibe coding. Now, coding happens more by “feeling the vibe”, where prompts generate suggestions, developers test and iterate, and code is assembled by validating these vibed bits of code.

When done right, it’s productive and creative, offering exciting new possibilities for building applications. But when it’s done without structure or oversight, it quickly results in poor code that’s hard to scale and maintain.

That’s why integrating new methods into established practices is crucial, and combining static code checking with AI will be the new superpower. 

A super-charged junior developer

Vibe coding is like giving instructions to a fresh graduate. When a junior developer builds something without guidance, it might work, but it often becomes a complex, self-referential mess lacking business logic, data structure clarity, and scalability. With AI, this happens more quickly, because the same issues remain: Lack of experience, knowledge, insight, collaboration, maintenance understanding, and discipline.

Just like knowing how to write doesn’t make you an author, generating syntactically correct code doesn’t make you a software developer. Senior developers' real value lies in long-term thinking—building scalable systems that don't break under pressure.

That’s not to put your junior developers down. But just as you wouldn’t let a trainee rebuild a car engine, you shouldn’t expect flawless results from an unsupervised junior writing your code—even if it’s an AI partner.

Taming the chaos

Yes, code is logical, mostly. But the act of coding is chaotic. Software developers have spent years building ecosystems of tools and processes to manage that chaos: Git, pull requests, test coverage, CI/CD, containerization, microservices, domain-driven design, etc. These systems don’t exist to make life more difficult, but to allow teams to work on solutions that must last for years, across time zones and cultures.

Bringing AI into the dev process doesn’t eliminate the need for those tools; it increases it. That’s because when AI generates 90% of your code, things spiral quickly. If the AI improvises instead of thinking strategically, the code becomes impossible to manage later.

In the same way that large codebases need the process to be automated so thousands of developers can work together, you need the same process with AI, which can quickly generate a thousand developers' worth of code. 

Testing and validation are key

AI makes generating code easy. As a result, the true value is shifting away from "writing code" to "ensuring the code does the right thing." In the future, testing, validation, and requirement specification will hold the keys to quality software.

This isn't a step backward—it’s a sign of maturity. If we no longer have to spend half our time fighting syntax or API docs, we can finally focus on what matters: What is this software supposed to do, and how do we know it’s doing it? 

So the ability to define clear requirements, write tests, design interfaces, and understand domains becomes far more critical. Focusing on the next metalevel, how you build and do software will be the new work of developers, building pipelines and processes to control AI. Devops. 

Closing thoughts

Vibe coding is a symptom of how easy it’s become to generate code and how difficult it is to build great software. AI can be a valuable assistant (even a competent co-developer), but without architecture, ownership, and testing, it quickly becomes a source of technical debt and frustration.

The developers of the future aren’t those who write the most code, but those who can define clear goals, understand the bigger picture, and ensure the system fits together. 

AI doesn’t change what it means to develop software. It only changes where we focus our energy.