What Did AI Actually Write?

You got your first code from AI. The code is in front of you. Before we analyze what's in it, let's talk about what AI actually did — and what it DIDN'T do.

The Code Looks Right — But Is It?

Here's the thing: The code looks like it works. It has clear structure. The indentation is correct. The commands are in English and look like "real programming." A beginner looks at this code and thinks: "That looks professional. That should definitely work."

Sometimes it does. Sometimes it doesn't. And that's exactly the problem with AI and code.

Imagine you hire a ghostwriter with the task: "Write me a screenplay for an action movie." The ghostwriter gives you 50 pages back. The screenplay has dialogue, scenes, camera work — everything looks like a real screenplay. But does it have suspense? Is the story good? You only know when you read it and take time.

Code is similar. AI can make it look like code. But that doesn't mean it works, that it's elegant, or that it does what you want.

Three Things AI Does Very Well in Code

The structure is there. AI organizes code like a professional. Functions are cleanly named. The code is commented (with explanations). There are no syntax errors (typos in the programming language), because AI knows the rules. When a professional writes code, it looks similar.

The boilerplate is fast. These are the repetitive parts — imports, initializations, structure. These are the boring 80% of code that everyone has to write before the interesting logic begins. AI does this in seconds. A programmer would spend hours avoiding mistakes. This is one of the real benefits of AI in code.

The syntax is correct. The code has no obvious errors in the language itself. The program will be able to "compile" (translate) or "execute" (run). That's already a big deal — for a beginner, writing error-free code is the hardest part, and AI takes that burden away.

Three Things AI Fails At in Code

The logic can be wrong. Syntax and logic aren't the same thing. Syntax = the rules of the language. Logic = what the code actually does. AI can write perfect code with perfect syntax — but if the logic is wrong, the program does the wrong thing.

You ask AI: "Create a script that calculates the average size of a list." AI writes code that looks right. But when you test it, you get a number that makes no sense. Why? Maybe AI missed a line, or the formula is off-by-one (a classic beginner trap). AI doesn't know it's wrong. It just added the most likely next character.

The code can be over-engineered. That's the opposite of elegant. You ask for something simple, and AI gives you 200 lines with classes, interfaces, design patterns — all very professional, but way too complicated for the problem. It's like asking for a toast recipe and someone gives you a five-star restaurant concept.

The code doesn't understand your intent. This might be the biggest problem. You think: "I want a script that converts my CSV file to JSON." That sounds clear. But AI doesn't know: Which fields matter? What are your filenames? In what order? Where should the result be saved? AI makes a "best guess" and writes code for an average CSV-to-JSON conversion. But your CSV is special. Now the code doesn't fit.

Three Questions to Trust the Code

Here's a simple checklist you can use to decide whether to trust the code:

1. Did I make my intention clear? The clearer you describe what you want, the higher the chance AI gets it right. If you don't explain in your first prompt that your CSV file has special columns, AI won't ask — it will make assumptions. The more specific you are, the better the code.

2. Can I verify the result? If the code makes a mistake, will you recognize it? Or does the script just run in the background and you don't know if the result is correct? That's crucial. If you can test the result (and understand what "correct" would be), then it's okay to use the code. If not, you need help from someone who understands it.

3. What does an error cost? A bad poem — doesn't matter. A bad database script that overwrites customer data — very bad. If the code does something critical (data loss, security, finances), you need a real programmer to review it. If the code does something harmless (saving a webpage, downloading data), you can try it.

What This Means: The "Autopilot Metaphor"

Think of an autopilot in a car. The autopilot can drive a long stretch without your hands on the wheel. That's amazing and saves time. But you don't fall asleep. You stay awake, and if something looks off — a sharp curve, traffic, an obstacle — you take over.

AI code is exactly like autopilot. It can get you 80% of the way. But you have to stay awake. You have to test the result. You have to be ready to make adjustments. You have to understand where the limits are.

This isn't a weakness of AI. This is normal. Professional programmers do this all the time — they write code, test it, find bugs, fix it. AI speeds up this process by 100x. But human oversight is still needed.