AI Doesn't Crash. It Lies.

The most dangerous code AI writes is the code that runs perfectly.

We've all been trained to fear the crash. Red text, a stack trace, a build that won't pass. Those are the bugs we know how to handle, because they announce themselves.

The bugs that should actually scare you in the age of AI are the quiet ones. The code that runs. No error. No stack trace. Looks completely reasonable. And is quietly, confidently wrong.

I've started calling these silent failures, and once you learn to see them, you can't unsee them in AI-generated code.

A function that lied to me

Here's a real one. I asked an AI to write a function that fetches a package's dependencies from a registry. This came back:

async function fetchDependencies(packageName) {  
    try {    
        const res = await fetch(`https://registry.example.com/${packageName}`);    
        const data = await res.json();    
        return data.dependencies;  
    } catch (err) 
    {    
        return [];  
    }
}

Read it quickly and it looks fine. It even looks careful, there's error handling. Tests pass. You ship it.

Then one day the network hiccups. The fetch fails. The catch swallows it and returns an empty array. And somewhere downstream, your security scanner reports zero vulnerabilities, because as far as it can tell, this package has zero dependencies.

The code didn't break. It lied. And it lied in the most reassuring way possible: by handing back something that looks like a valid answer.

Here's what it should have done:

async function fetchDependencies(packageName) {  
    const res = await fetch(`https://registry.example.com/${packageName}`);  

    if (!res.ok) {    
        throw new Error(`Registry request failed: ${res.status}`);  
    }  

    const data = await res.json();  
    return data.dependencies;
}

Let the failure be a failure. Let the caller decide what to do with it. An empty array should mean "no dependencies," not "something went wrong and I'm hiding it from you."

Why AI does this constantly

AI is optimizing to complete the task you gave it. You said "fetch the dependencies," so it returns dependencies, and it really doesn't want to hand you back an error, because an error looks like failure. So it reaches for the safe-looking default. Empty array. Null. Zero. false. Anything that makes the function "work."

This is the difference between code that looks correct and code that is correct, and it's the gap that bites you in production. The naming is sensible, the structure is familiar, the happy path works. None of that tells you what happens when something goes wrong, and "what happens when something goes wrong" is most of what senior engineering actually is.

Where to look

Once you know the pattern, silent failures hide in predictable places. When you review AI code, go straight to these:

• Any try/catch that doesn't log or re-throw. Ask: what does this swallow?
• Functions that return null, [], or 0 on a failure path. Ask: can the caller tell the difference between "empty" and "broken"?
• Network or file calls that don't check the response status before using the result.
• Any if with a branch that quietly does nothing.

None of these are exotic. They're the boring, high-frequency ways AI-generated code goes wrong, and they're invisible if you read for "does this look reasonable" instead of "what is this hiding."

The skill nobody's teaching yet

Here's the part that matters. Reading code AI wrote is not the same skill as writing code yourself. When you write it, you hold the failure modes in your head as you go. When AI writes it, you're handed a finished thing that looks done, and your brain wants to approve it and move on.

The developers who'll do well with AI aren't the ones who prompt the fastest. They're the ones who can look at a confident, clean-looking block of generated code and ask the uncomfortable question: where does this lie?

That's a learnable skill. It just isn't the one most "AI for developers" content teaches, because most of it stops at "write better prompts." Prompts are the easy half. Knowing what to do with what comes back is the half that keeps your name off a production incident.

One idea from a bigger system

This silent-failure stuff is covered in more detail in a course I just released, The Developer's AI Productivity Blueprint. The whole thing is about exactly this: using AI to go faster without quietly handing over the judgment that makes you worth hiring. Eight modules, the real workflow I use on production code every day, including a full live build where you watch it all come together on one project, start to finish.

If "where does this lie" is a question you want to get fast at figuring out, the Blueprint is out now, and 30% off for launch week: $139 instead of $199. The discount ends in 1 week, on July 9.

Get the Blueprint for $139