How AI Makes You a Super Coder

The Myth of the Overnight Coding Genius

During our college days, we often saw movies portraying brilliant young programmers working nonstop through the night and seemingly building revolutionary applications in a burst of inspiration. It made programming look magical, as if genius and rapid typing were all it took. But real software development has always been more demanding. Even with two decades of experience, I know that turning an idea into working software takes time: drafting the first version, testing, refining logic, improving efficiency, restructuring code, and making sure it performs reliably in real-world scenarios. Even a small automation script can go through multiple iterations before it becomes solid, usable, and ready for production.

How Generative AI Changed the Pace of Development

Then came the rise of Generative AI, turning what once felt slow and meticulous into something incredibly fast and accessible. Tasks that used to take days, such as planning, writing, debugging, and refining, can now be completed in minutes with a well-crafted prompt. I describe what I need, generate the code, run a quick test, and if something doesn’t work, iterate with a few more instructions. Within a handful of cycles, I often end up with clean, working code that would previously have taken much longer to build manually. As the technology improves, the risks of errors or hallucinations are decreasing too. With strong prompting skills, it’s becoming increasingly possible to get high-quality, near-production-ready results on the first attempt.

The Rise of Agentic AI and Vibe Coding

Now we’re entering an era of agentic AI and “vibe coding,” where you can ask AI to assemble an entire application, a website, a full-stack system, or even a complex enterprise solution, and receive a working prototype with minimal effort. Agentic AI systems can plan and execute multi-step tasks independently, while vibe coding treats natural language as the primary interface for building software.

A New Way to Build Software

Vibe coding goes even further, suggesting that someone with no traditional programming background can build a working application simply by expressing their requirements in plain language. It shifts the focus away from how the system works internally and instead emphasizes the outcome. In practical terms, it resembles a black box approach. As long as the solution meets the requirements and behaves correctly across scenarios, the underlying implementation becomes secondary. This mindset allows a much wider audience to create software and bring ideas to life.

Why Human Oversight Still Matters

Of course, that doesn’t mean AI-generated output is flawless. For now, every result still needs rigorous testing, review, and security scrutiny before being trusted in production. The tools are improving rapidly, but they are not perfect, and they may never be. They should be treated as powerful collaborators that accelerate development, not as replacements for engineering judgment and responsibility.

A Real Example of AI Supercharging Productivity

A recent experience reinforced this point for me. A colleague performing performance testing on a switching platform needed to generate 1,000 ISO8583 1200 transaction messages and 1,420 reversal messages, using five different cards and ten terminal IDs for a specific date across multiple permutations. The messages had to follow the exact specifications of the environment, including headers and additional custom fields. Doing this manually would have required a massive effort.

A few years ago, faced with such a requirement, I would have written a script from scratch and spent several days refining and extending it, especially as the requirements continued to evolve. We started with one card, then expanded to three and eventually five. More terminal combinations were introduced, then a rule that amounts must remain under 100 to avoid fraud triggers, then constraints on specific STAN ranges, and so on.

Using Generative AI tools like ChatGPT, Groq, Gemini, and Claude, the entire process took just a few hours. I generated the initial script, tested it, refined it, and added new constraints by simply pasting existing code and explaining the changes needed. Even as the requirements evolved, the turnaround time remained incredibly fast. This would not have been possible manually within the same timeframe.

In that moment, I truly felt like a super coder, achieving in a few hours what would once have taken days. It brought back the excitement of those movie-style coding sessions where ideas take shape at lightning speed.

The Bigger Shift Beyond Small Utilities

But this shift isn’t limited to small utilities or one-off scripts. Today, entire applications can be created the same way. GenAI has made it possible for almost anyone to operate like a seasoned software developer. If you have a compelling idea, you can now build a working application in a remarkably short time and bring it to life far faster than traditional development cycles would allow.

What AI Cannot Replace

Will this replace developers? Certainly not. And can AI-generated code be deployed directly into production? Absolutely not. It still needs thorough review, structured testing, performance checks, security validation, and all the discipline that professional engineering demands. The last thing anyone wants is a vulnerability in AI-generated code leading to a breach or loss of trust. The same caution applies to text, where AI-generated content has occasionally been submitted in academic and legal settings without proper verification, harming credibility.