I’m an lifelong learner. I love to delve into new things. I’m captivated by knowledge. That is part of the reason why I became fascinated with data. It has not always been that way. Once upon a time, I had no desire to learn anything. But somewhere along the way, there was a switch, and now all I want to do is learn. From a learning perspective, data is a two-edged sword. You learn both from the statistics and the research that drive the work, and from the results themselves. There is an unmatchable feeling of being armed with knowledge. Knowledge that you attained through research and doing things. But tides are changing. With the technological advances, I can’t help but feel that AI is taking it all away from me. Who wants to spend hours researching something when they can just click one button and have it all?

Using AI for Data Analysis and Beyond

My primary work is data. But data is, for lack of better terms, the raw material. Standing alone, it is nothing more than mumble jumble. It requires tools to extract and process to generate a digestible output. And for that, I use Python.

When I first started using the programming language, I struggled. I spent hours combing through StackOverflow and Medium trying to find something remotely close to what I was doing. I would spend hours rewriting the same loop till it worked.

In my early days of coding, it was some of the most challenging and most rewarding times in my career. After hours of labor, making something work was a very satisfying feeling.

Since AI burst onto the scene in late 2022, the hours spent putting the code puzzle together came to an abrupt end. Further, within months, in the business world, companies everywhere suddenly found themselves in a mad dash to stay relevant. AI wasn’t just a cool tool to use; it had become central to business strategy.

Immediately, I picked up on the cue and began to familiarize myself with it. In the early days, the tool was still a diamond in the rough with lots of allusion and information I couldn’t do much with. But it did help me smooth out some of my data analysis.

As early as January 2023, I was using AI to help me figure out a number of issues—from personal to professional projects.

Gradually, my daily progress began to improve. I was writing sustainable code at a fraction of the time.

In the back of my mind, I couldn’t help but wonder if using AI that much was doing me a disservice. My work no longer felt like chiseling marble. The hustle was different. With AI, it felt more like... vibe coding

Who Came Up With the Idea of Vibe Coding?

In February 2025, Andrej Karpathy came up with the term “vibe coding.” Here is what he said:

There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like “decrease the padding on the sidebar by half” because I’m too lazy to find it. I “Accept All” always, I don’t read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I’d have to really read through it for a while. Sometimes the LLMs can’t fix a bug so I just work around it or ask for random changes until it goes away. It’s not too bad for throwaway weekend projects, but still quite amusing. I’m building a project or webapp, but it’s not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.

To some extent, I agree. It is fun to start a project where little to no thought is required. All you do is copy and paste.

But what does vibe coding mean exactly? In this day and age, the term is in a gray area. Simon Willison explained in his blog that if LLMs write the code for you and you are able to explain how it works, that is not considered vibe coding.

Months since the term was coined, AI has made some pretty good advances, and in some cases, vibe coding has become the norm.

Non-Technical People are Building and Monetizing Apps

Last month, Forbes reported on new startups created in unconventional ways. They had no budget, no venture capital war chest, and no traditional team of engineers. Just a room full of Gen Zers willing to take a chance on something they had no idea how to do. But they knew how to use AI.

AI is not only redefining entrepreneurship, it is leveling the playing field in ways that most of us can’t even comprehend.

This is where things are getting hairy for me. I have a love-hate relationship with AI. But the truth is, it is here to stay, and unless I’m willing to give up our old ways and embrace it, I will find myself alone in the train station.

My Beef with AI

But LLMs are not exactly precise in their output. The longer you work with it, the more likely it is that they will change function names and variables depending on the iteration, making it very difficult to maintain coherence. Even with recent advances, I am still very skeptical of just copying and pasting.

Instead, type out every single line, checking for discrepancies and attempting to learn in the process. I want to understand every line. Every variable. Every function. More often than not, I make changes to whatever AI spat out.

But now, almost in the third year since AI took us by storm, I wonder if my conservative methods might be holding me down.

Most of my AI usage was not for fun. It was to help me improve my coding skills and productivity.

Almost three years later, I have found that AI has put me in a tough predicament. If I use it, I am doing a disservice to myself and my brain. If I don’t use it, I am doing a disservice to myself and my career.

Future Concerns

While most people are concentrating on capitalizing on the new technology, very few are looking at the possible long-term unintended consequences. I don’t mean machines taking over the world. I don’t think that is ever going to happen.

But MIT conducted a recent study showing that AI can lead to cognitive debt and decrease learning skills. In just three years, we are already seeing the negative effects of AI usage.

Over the years, we have seen the overuse consequences of certain technologies—constant digital connectivity, information overload, dependency on search engines, and so on—all of which have negatively affected us in one way or another.

Now imagine the upcoming Gen Alpha and Beta, and all the subsequent generations. They would not have experienced life without these technological advances. They will have no idea how to back up a car without cameras. Or go to a library, gather multiple books for a school assignment.

We have entered uncharted territories where the repercussions of AI on humanity are completely unknown. In just three years, we have already seen a decline in human intelligence. What would the Earth look like in another 10 years?

Conclusion

I find myself between a rock and a hard place. Not using AI will jeopardize my career as a data scientist. But using AI will affect me in the long run on my cognitive and creative side.

But I feel for my children who will never get the chance to learn the basics, such as doing calculations without a calculator, or being able to write eloquently without the assistance of AI.

I hope I am wrong on this, but long gone are the days of the trailblazers like Thomas Edison, Nikola Tesla, and even Steve Jobs or Tim Berners-Lee. The upcoming generations might not be able to think clearly without AI assistance.

The question isn’t whether we use AI—it’s how we use it without losing ourselves in the process.