By Siddharth Pai
There was a time when learning to code felt like acquiring a second language, one you had to practice for years before you could build anything meaningful. You needed to understand logic structures, syntax peculiarities, and the unique grammar of whatever language you were working in. And even after all that, translating an idea in your head into working software still required hours of debugging, testing, and Googling obscure error messages. That world hasn’t vanished entirely, but it’s now been joined by a parallel one. In this new world, people who have never written a single line of code can summon working programs with a well-worded prompt and a little patience—welcome to the world of vibe coding.
Vibe coding is what happens when someone uses artificial intelligence (AI) tools like Google’s Gemini, OpenAI’s ChatGPT, or other large language models to create software simply by describing what they want. The user doesn’t know how to write code and sometimes doesn’t even know what coding language would be best for the job. But they can explain, in plain English, that they want a web app to keep track of their workout routine, or a tool that reminds them to drink water every hour, or a piece of software that organises files by date and content. The AI takes that description and writes code that does what the user described, or something very close to it.
How AI seems to write code
What makes this possible is the way these AI models have been trained. Systems like ChatGPT and Gemini have consumed vast amounts of code from public repositories, documentation sites, Q&A forums like Stack Overflow, and textbooks. They’ve seen everything from basic “Hello, World” programs to complex machine learning models written in multiple programming languages. When you describe a task to them, they aren’t writing code from scratch in the same way a human programmer would. Instead, they’re synthesising patterns they’ve seen before, stringing together code fragments, filling in gaps, and choosing likely solutions that match your request.
This process of matching your description to code is powered by algorithms (mathematical instructions for solving a problem) called transformer architectures. These allow the AI to predict the next word—or in this case, the next line of code—based on the context of everything that came before it. When you say “build me a calculator app,” the AI doesn’t reason through the mechanics of math or UI layout the way a human might. Instead, it searches through its statistical model (a compressed representation of its training data) and finds the most likely patterns of code that usually follow that kind of request. In other words, it’s not coding from understanding, but from prediction.
Suppose you ask Gemini to build a simple to-do list app that runs in the browser. You say, “I want an app where I can add tasks, mark them as complete, and delete them.” Gemini will likely respond with a few blocks of code in HTML, CSS, and JavaScript, maybe wrapped together with a friendly explanation of what each part does. If you want it styled nicely, you can ask for a Material Design version or Bootstrap integration. If you want it to store tasks between sessions, you can request local storage or cloud sync, and the model will adapt its answer.
The first version of the app may not be perfect. This is where iteration and feedback become key. You can copy the code into an online editor like Replit or CodeSandbox, run it, and see what happens. You describe the problem to the AI, paste the code, and ask it to fix it. The AI analyses both your description and the code it generated earlier, offering a revised version with the necessary corrections. This feedback loop—describe, generate, test, fix—is how non-programmers can move from vague ideas to working software, without ever needing to master a programming language.
At the heart of this loop are evaluation algorithms (sets of rules the AI uses to decide how well the code it’s about to generate fits your request). The AI keeps track of how semantically aligned your prompt is with the kind of code it knows how to generate. If the alignment is low—say, you ask for a real-time multiplayer game but don’t describe how players interact—the AI may fill in the blanks with assumptions. If it’s high—like when you describe each button’s function clearly—the AI can match your request more precisely. Testing in this context is as much a human experience as it is a technical process.
Intuition-driven testing vs automation
Professional developers write automated test cases that check for edge cases and regression errors. A vibe coder, on the other hand, is more likely to test by clicking buttons and seeing what breaks. It’s experiential quality assurance guided by intuition. But surprisingly often, it works.
However, vibe coding has its limitations. First, there’s a ceiling to what you can build without understanding how the code works. The AI may stitch together libraries, frameworks, or application programming interfaces (APIs) that you’ve never heard of, and if something breaks, you may not know where to start looking. The AI’s explanations can help, but they’re not always perfect. There’s also the problem of hallucination, where the model generates code that looks plausible but references non-existent functions or deprecated features.
Security is another concern. AI models don’t reliably guard against unsafe coding practices. The AI isn’t malicious, but it’s also not infallible. Its goal is to produce something that looks right based on your description, not something that has passed a rigorous security audit.
Then there’s the question of originality. AI-generated code is heavily derivative. It’s a remix of public codebases and known design patterns, which is fine for personal or internal use, but may become murky in commercial settings. Licensing concerns around AI-generated content are still evolving, and someone trying to launch a product built this way might later need a professional developer to vet and rework the foundation.
Yet despite all this, the appeal of vibe coding is growing. It lets people test ideas quickly. It turns abstract concepts into visible, interactive forms. And it provides an educational ladder, where the curious can slowly move from passive user to active builder, learning as they go. A person might start by asking Gemini to build a form, but in the process they’ll see how HTML inputs work, how JavaScript handles events, and maybe even how CSS flexboxes (cascading style sheets used for web page design) behave. The code becomes a textbook, and the AI becomes a tutor who never tires of explaining the same concept five different ways.
The real revolution isn’t that AI can write code. It’s that people who would never have thought of themselves as developers can now create software to solve their own problems, explore their own ideas, and learn by doing. Vibe coding is about expanding access. The bar for entry has dropped far enough that a motivated amateur can now walk in and build something real.
The author is a technology consultant & venture capitalist