Vibe Coding: Why Fast Doesn't Mean Good

If AI can write code faster than any developer alive, why do the best developers still insist you learn the fundamentals first?

It's a fair question, and honestly, a really exciting one to think about. We are living through a remarkable moment. Tools like GitHub Copilot, Cursor, and Claude can generate working code in seconds from a natural language description. For someone brand new to programming, that feels like a superpower. And in many ways, it is.

But there's a difference between having a superpower and knowing how to use it well. In this issue, we're going to talk about a trend called vibe coding, why it's getting people excited, and what you should keep in mind as you start exploring AI-assisted coding tools.

Vibe coding is when you use an AI tool to generate code by describing what you want in your own words, without necessarily writing the code yourself or fully understanding what the AI produces.

Think of it like this: imagine you want to bake a cake, but instead of learning to bake, you describe the cake you want and a robot bakes it for you. The cake shows up. It might even look great! But if someone asks you how it was made, or what to do if it falls flat, you might be stuck.

That's the heart of vibe coding. The output can be impressive. The speed is real. But understanding what was built, and why, is a separate skill entirely.

The phrase was coined by Andrej Karpathy in a post on X on February 2, 2025. If you haven't heard of him, Karpathy is one of the most respected researchers in AI. He was a co-founder of OpenAI and previously led the AI team at Tesla. He now runs his own AI education company called Eureka Labs, and his YouTube tutorials on neural networks are considered some of the best free learning resources on the internet.

In the post, Karpathy described what a typical vibe coding session looked like for him. He barely touched the keyboard, using a voice tool to just talk to the AI instead. He accepted every code change without reading it. When something broke, he pasted the error message straight back in with no explanation and let the AI sort it out. When the AI got truly stuck on a bug, he would just ask for random changes until it somehow went away. He admitted the codebase eventually grew beyond what he could fully understand himself.

His conclusion? It was fine for throwaway weekend projects, and honestly pretty fun. But it was not really coding in the traditional sense.

The post racked up close to 7 million views and the term spread so fast that Collins Dictionary named it their Word of the Year for 2025. Karpathy has since said it was a casual, off-the-cuff thought that happened to put a name to something a lot of people were already feeling.

The important detail to remember though? He already had decades of deep programming experience before he started playing around with it this way.

When people first discover AI coding tools, two ideas tend to come up a lot. Both are understandable. Both are worth looking at a little more closely.

AI coding tools are genuinely fast. They can produce hundreds of lines of code in the time it takes you to type a sentence. That's not hype. It's real.

But here's the thing: developers were never judged on how fast they could type code. They were judged on whether they could solve a problem. A working solution that you understand is worth far more than a fast solution that breaks the moment something unexpected happens.

Think of it like following a recipe step by step. You can produce a great dish without being a chef. But if something goes wrong mid-cook, the recipe won't tell you why or how to fix it. That's where understanding kicks in.

You'll hear people say AI coding tools were "trained on the entire internet," as if that means the AI absorbed all the best programming knowledge in existence.

Here's the reality: the internet is also full of outdated tutorials, copy-pasted Stack Overflow answers from 2011, and examples written by people who were just figuring things out themselves. Volume of information is not the same as quality of judgment.

This doesn't mean the AI is bad. Far from it. It means the AI is a very capable tool that still benefits from a human who knows enough to spot when something looks off.

Here's something that might surprise you: the developers who are most enthusiastic about AI coding tools are often the ones with the most experience. That's not a coincidence.

The best developers use AI to go faster at things they already understand, not to skip the understanding entirely. They use it to handle repetitive boilerplate, explore unfamiliar syntax, or get a first draft going. Then they read it, question it, and shape it into something they can stand behind.

That's the key difference. When you understand the fundamentals, even at a basic level, you can use AI as a genuine accelerator. When you don't, you end up with code that works until it doesn't, and no clear path to fixing it.

The good news? You don't need to be an expert before using these tools. You just need to stay curious about what they're producing. Learn alongside the AI, not instead of it.

Next time you use an AI coding tool, try this five-minute exercise:

This one habit, asking the AI to teach you as it builds, will help you learn faster than almost anything else. You're not just getting an output. You're building understanding.

Vibe coding is exciting, and you should absolutely explore it. These tools are opening doors for people who never thought coding was accessible to them, and that is genuinely wonderful.

Just remember: the goal isn't to generate the most code the fastest. The goal is to build something that works, that you can maintain, and that you understand well enough to improve. A thousand lines of code you can't explain is harder to work with than ten lines of code you know inside and out.

AI is an incredible tool. Use it to learn faster, build more, and go further. Just don't let it be a shortcut around the understanding, because that understanding is exactly what makes the tool so powerful in the first place.

Discussion Question: If your AI-generated project suddenly broke and you had no idea why, what would you do? How would you start figuring it out? Share your thoughts in the comments. There's no wrong answer here.

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