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Samuel Ochaba
Samuel Ochaba

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The "Shallow Developer" Trap: Why AI Code Breaks in Production

#webdev #ai #webperf #javascript

There is a growing sentiment in the industry right now, usually whispered by junior developers or shouted on Twitter:

"I generated an entire dashboard feature in 15 minutes using Cursor. Why do I need to spend weeks mastering the deep internals of JavaScript anymore?"

It’s a valid question. If the output is functional, does the implementation detail matter?

The answer is yes. In fact, deep knowledge matters more now than it did three years ago.

Why? Because AI allows developers to build complex systems much faster than they can understand them. When you treat AI as a "black box" that handles the hard stuff, you aren't becoming more efficient—you are creating a codebase full of subtle, architectural time bombs.

I ran an experiment to demonstrate why AI tools require Senior supervision.

The Experiment: The "Perfect" Memory Leak

I gave a leading AI model (GPT-4o level) a prompt that sounds standard for a utility library:

"Write a generic memoize function in JavaScript. It should cache the results of function calls so we don't re-compute them if the same arguments are passed again."

The AI confidently returned this:

const memoize = (fn) => {
  const cache = new Map();

  return (...args) => {
    // Create a key for the arguments
    const key = JSON.stringify(args);

    if (cache.has(key)) {
      return cache.get(key);
    }

    const result = fn(...args);
    cache.set(key, result);
    return result;
  };
};
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The "Shallow" Review

If you look at this with surface-level knowledge, it looks great:

  1. It uses Modern JS (const, arrows, Map).
  2. It handles multiple arguments.
  3. It works. If you run it, the tests pass.

The "Deep" Reality

If you use this function in a long-running Single Page Application (SPA) where you are passing Objects or DOM Nodes as arguments, you have just introduced a massive memory leak.

Why? Because Map (and the key string) holds a Strong Reference.

If you memoize a function that processes a user profile object, that object will stay in the cache forever—even if the user logs out, even if the component unmounts. The Garbage Collector (GC) cannot touch it because the cache is holding onto it.

"Why didn't the AI use WeakMap?"

You might think the AI is just being "dumb." Why not use WeakMap, which allows garbage collection?

Here is the kicker: The AI was actually being smart—in a generic way.

WeakMap keys must be objects. If you try to use WeakMap and pass a number (e.g., memoize(factorial)(5)), your app will crash.

The AI knows this. It optimized for Generic Safety (making sure the code doesn't crash on numbers) rather than Contextual Performance (preventing leaks on objects).

This is the trap.

  • The AI sees a generic request and provides a generic solution (Map).
  • The Shallow Developer pastes it into a specific context (heavy objects) without realizing the trade-off.
  • The Senior Developer recognizes that for this specific feature, we need a custom solution using WeakMap (and ignoring primitives), or an LRU (Least Recently Used) cache strategy to limit memory usage.

Context is the New Coding

The code above isn't "wrong." It's just dangerous in the wrong hands.

A developer who relies entirely on AI sees code that runs.
A developer with deep knowledge sees code that lives.

They understand the Lifecycle of the application. They ask questions the AI cannot answer:

  • "How long does this application run?"
  • "Are we processing strings or 10MB JSON blobs?"
  • "What happens to this cache when the user navigates away?"

The Future of the "Senior" Engineer

AI has not made knowledge obsolete; it has changed how we apply it. We are shifting from Writers to Auditors.

If you don't understand the Event Loop, Garbage Collection, Closures, and Reference nuances, you cannot audit the AI. You are driving a Ferrari with a blindfold on. You're going fast, but you're eventually going to hit a wall.

Don't let the convenience of "Command+K" rob you of your curiosity. Learn the internals. That deep knowledge is the only thing standing between a working prototype and a production disaster.

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