The Fast Food Problem with AI Coding

I am about to compare AI-assisted coding (vibe coding in some sense, but not necessarily all forms of it) to fast food. I know that sounds odd, and it is not an obvious parallel. This is not an AI skeptic's warning or a hot take against using AI to write code. It is a pattern I noticed, and once I saw it, I could not stop thinking about it. Bear with me on this one.

Here is what I see happening around me. People press a button, code appears, they glance at it, maybe skim the first few lines, and hit accept. It works, or at least it seems to, so they move on. This keeps happening, and before you know it, you are no longer reading what the AI wrote or questioning any of it. You just accept and move forward because it is faster and it feels productive.

The real problem is not the code itself. It is that people have stopped thinking. The deep-dive into why something works, the curiosity to understand what is actually going on under the hood, that part is slowly disappearing. And the worst part is, once you lose that habit, going back is incredibly hard. It is like a muscle you stopped using, and now it hurts to lift even the small weights you used to handle without thinking.

So how did we get here? And why does it feel so familiar? To answer that, I need to talk about something completely unrelated. Food.

The food story

For most of human history, food was scarce. People struggled to get enough nutrition, and the diseases of that era reflected it. Malnutrition, deficiencies, starvation. There was not enough food to go around, and people did not always know how to get or prepare what little they had.

This started changing in the mid-20th century. The Green Revolution brought high-yield crops, synthetic fertilizers, and modern irrigation that dramatically increased food production worldwide. Around the same time, the rise of fast food chains in the 1950s and 60s, led by companies like McDonald's and KFC, made calorie-dense meals available at a fraction of the cost and time. Between agricultural breakthroughs and the fast food industry, food went from scarce to abundant almost within a generation. And honestly, it helped. A lot of people who could not afford proper meals now had access to affordable food. Entire populations that were underfed could finally eat enough. These advancements solved a real problem, and it would be unfair to ignore that.

But something shifted. The problem was no longer scarcity. It flipped entirely. Today, most health problems in the developed world are not from a lack of food but from too much of it. Obesity, diabetes, heart disease. People eat more than they need, and they eat the wrong things, not because healthy food is unavailable but because the fast, easy option is right there. It takes real effort to eat well when you can grab a burger in two minutes. The temptation is constant, the discipline required is high, and most people simply give in because it is easier.

Now replace "food" with "code" and read that again.

The parallel

Just like food, code was scarce. There were not enough developers, writing good software took years of practice, and companies struggled to ship fast enough. Most of the world's problems with software were about not having enough of it, the same way most of the world's problems with food were about not having enough to eat.

Then AI-assisted coding tools showed up, and just like the Green Revolution and fast food chains did for food, they made code abundant. Writing code went from something that required years of skill to something anyone could generate in seconds. A person who had never written a line of Python could now build a working app in an afternoon. Just like a family that could not afford a proper meal could now feed everyone for a few dollars at a drive-through, people who were previously locked out of software development could now participate. And just like food, this genuinely helped. Startups could move faster, solo developers could build what previously required a team, and more problems in the world got solved with software.

But also just like food, the problem flipped. Today, the issue with AI-assisted coding is not that there is too little code. It is that there is too much of it, and most of it is being consumed without understanding. People accept AI-generated code the way they accept a fast food meal, without thinking about what is in it, whether it is good for them, or what it is doing to them in the long run.

Why we can't stop

So why is it so hard to stop? Why can't we just decide to review the code properly, think before accepting, and go back to how we used to work? For the same reason people can't just decide to stop eating fast food. It is genuinely addictive.

Fast food is engineered to hit the right spots in your brain. Salt, sugar, fat, all in the perfect combination to make you want more. You know it is not good for you, but your brain does not care in that moment because the reward is immediate and the effort is zero. AI-generated code works the same way. You type a few words, and a complete solution appears. Your brain gets the reward of "I built something" without going through any of the struggle that building usually requires. And just like reaching for a bag of chips instead of cooking a meal, reaching for that accept button is so much easier than sitting down and actually thinking through the problem yourself.

The real issue is that our brains are wired to avoid unnecessary effort. Thinking is expensive, it takes energy, it is uncomfortable, and it is slow. When someone offers you a way to skip all of that and still get the result, your brain will take that deal every single time unless you actively fight it. That is what makes this an addiction and not just a bad habit. The more you skip the thinking, the harder it becomes to go back to doing it, because your brain has learned that there is an easier path and it will resist the harder one.

This is not new

This pattern of scarcity turning into abundance and then into overconsumption is not unique to food or code. It has played out before in other areas, and the outcome looks remarkably similar every time.

Information and social media. There was a time when access to information was genuinely limited. You had to go to a library, find the right book, or know the right person. The internet changed that completely, and at first it was incredible. Anyone could learn anything. But today, the problem is not a lack of information. It is too much of it. People doomscroll for hours, consume content they do not need, and struggle to focus on anything for more than a few minutes. The skill of filtering and thinking critically about what you read has been replaced by an endless feed that your brain cannot stop consuming.

Antibiotics. Before antibiotics, simple infections could kill you. Their discovery was one of the greatest breakthroughs in medicine and saved millions of lives. But overuse and overprescription created antibiotic-resistant superbugs, turning the cure itself into a new and harder problem. The tool that saved us started hurting us because we could not stop reaching for it even when it was not necessary.

Easy credit. Money used to be limited to what you had. When easy credit and loans became widely available, it gave people access to things they could never afford before, homes, education, businesses. But the overconsumption of debt led to financial crises, personal bankruptcies, and an entire generation living beyond its means. The abundance of money that was not really theirs created problems that scarcity never could.

None of this means that antibiotics, the internet, or credit are bad. They are some of the greatest things we have built. It just means that every time we solve a scarcity problem, we create an abundance problem, and we have to figure out a healthy way to live with the new reality. For food, we have somewhat figured it out. Nutrition science, dietary guidelines, and fitness culture exist precisely because we learned the hard way. For AI-assisted coding, we are still in the early days. We have not figured out the healthy way yet, and that is what worries me.

The healthy way

So what does healthy actually look like? Let's go back to food one more time, because most people already know the answer here.

Eating healthy does not mean you never touch fast food again. It is okay to have a cheat meal, enjoy a biryani on a weekend, or grab something quick when you are in a rush. What matters is that on most days, you know what you are eating, why you are eating it, and how much of it your body actually needs. You cook your own meals, you read the labels, you exercise to keep your body strong enough to handle what you put into it. The occasional indulgence is fine because the foundation is solid.

The same applies to code. It is okay to let AI write something for you when you are experimenting, prototyping, or exploring an idea you have never tried before. That is your cheat meal, and there is nothing wrong with it. But on most days, when you are building something that matters, you need to know what is in the code you are shipping. You need to read it, understand why it works, and be able to change it when something breaks. You need to exercise the muscle of thinking through problems yourself, even when the easy button is right there. Using AI wisely means using your judgment about when to let it help and when to do the work yourself, not defaulting to the easiest option every single time.

If you want a more concrete set of rules, here is what I try to follow:

1. Read before you accept. Every line. If you cannot explain what the code does, you should not be shipping it.

2. Think before you prompt. Before asking AI to write something, spend a few minutes thinking about how you would approach the problem yourself. Even if you end up using AI, the thinking matters.

3. Exercise regularly. Write some code by hand, read through a codebase without AI explaining it, learn from documentation instead of asking a chatbot. Keep your skills sharp or they will fade.

4. Know your limits for the day. Fast food makes it easy to overeat, but that does not make it good for you. Just because you can generate hundreds of lines in one go does not mean you should. Take it in chunks you can actually understand.

5. Treat AI like a colleague, not a replacement. You would review a colleague's pull request before merging it. Do the same with AI-generated code.

Before you hit accept

We solved the food scarcity problem, and it gave us the obesity epidemic. We solved the information scarcity problem, and it gave us doomscrolling and misinformation. We are now solving the code scarcity problem, and if we are not careful, we will end up with a generation of developers who can ship everything but understand nothing.

The tools are not the problem. They never were. The problem is us and how we use them. So the next time you are about to hit accept without reading, ask yourself: is this a cheat meal, or has this become my entire diet?