You've done it. So have I. A rushed comment that says // temporary fix — written three years ago, still running in production. A function named handleData that actually handles five completely unrelated things. A README that describes the architecture you planned, not the one you shipped. These are the lies developers tell — and they compound quietly, long before any AI tool enters the picture.
This isn't a shame piece. It's a diagnostic one. Because if you're reaching for AI coding assistants to clean up your codebase, you need to understand what they can and cannot see. Spoiler: they can autocomplete your lies faster than you can type them.
The Lies Developers Tell (And Why They're Understandable)
Developer dishonesty rarely comes from bad intent. It comes from pressure, deadlines, and the all-too-human habit of telling a story about code that doesn't quite match reality. Here are the most common forms.
1. The "I'll Refactor This Later" Lie
Every developer has written a function they knew was wrong — too long, too tangled, dependent on a global it shouldn't touch — and left a comment promising a future that never comes. The lie isn't the bad code. It's the comment that implies accountability without any mechanism to enforce it.
// TODO: clean this up when we have time
function processUserData(data) {
// 200 lines of spaghetti logic
}AI tools like GitHub Copilot or Cursor will happily add more code inside that function. They have no idea the comment was a promise. They'll just keep building on the unstable foundation.
2. The Variable Name Lie
Names like data, result, temp, info, and stuff tell the reader nothing. They're lies of omission — technically valid identifiers that actively obscure intent. When you name a variable x inside a function that spans 80 lines, you're betting that future-you (or your teammate) will remember what x meant at 2am during a rollback.
AI will propagate these names without hesitation. It doesn't know that data is actually a list of filtered invoices for a specific billing period. It just sees a pattern and continues it.
3. The "It Works on My Machine" Lie
This one shows up in the gap between local development and production environments. Missing environment variables, hardcoded ports, paths that only exist on one machine — these are lies baked into the setup. Documentation says the app "runs with a single command." It takes a new developer four hours and three Stack Overflow tabs to get there.
For a more grounded look at how environment configurations can drift, see how developers handle things like getting env variables in a Laravel React application — a process that breaks silently if the source of truth is never enforced.
4. The Comment That No Longer Matches the Code
Comments go stale. Logic changes. The comment stays. Now the comment is a lie — it describes behavior that was replaced six months ago. This is actually worse than no comment at all, because it actively misleads anyone who reads it.
// Returns the user's full name
function getUserInfo(id) {
// Actually returns full user object including permissions, settings, and billing info
return db.query(`SELECT * FROM users WHERE id = ?`, [id]);
}An AI assistant reading this will generate tests and downstream logic based on the comment. It will assume the function returns a name. You've now automated the propagation of a lie.
5. The Architecture Lie
This is the most expensive lie. The system was designed for clean separation of concerns. Then a deadline hit. Then another. Now the frontend calls the database directly in two places, one service knows too much about another, and the "microservices" are effectively one tangled monolith that can't be deployed independently.
Nobody updated the architecture diagram. The onboarding doc still talks about clean boundaries. New developers spend their first two weeks confused about why reality doesn't match the documentation. This is the kind of context AI tools have absolutely zero access to.
Why AI Makes This Worse Before It Makes It Better
Here's the uncomfortable truth about AI coding tools: they are extraordinarily good at amplifying whatever patterns already exist in your codebase. They learn from context. If your context is full of lies, they'll generate coherent, confident, well-formatted lies.
Consider a developer using an AI assistant to extend a feature in a messy codebase:
- The AI sees the existing pattern and continues it — inconsistent naming and all.
- The AI reads the stale comment and generates logic that matches the comment, not the actual behavior.
- The AI produces code that looks clean in isolation but deepens the technical debt at the systemic level.
None of this is the AI's fault. It's doing exactly what it's supposed to do. The problem is that we treat AI output as a quality signal when it's actually a consistency signal. It's consistent with what you gave it. If what you gave it was wrong, the output is confidently wrong.
AI tools are mirrors, not critics. They reflect the quality of your codebase back at you, amplified and accelerated.
What Actually Fixes Developer Lies
The cure for codebase dishonesty isn't a better AI model. It's engineering discipline — the kind that's been preached for decades and still requires human intention to practice.
Honest Naming as a First Principle
Rename things when you understand them better. A function named handleData that matures into something specific should be renamed to reflect that specificity. Most modern IDEs make refactoring across a codebase fast and safe. There's no excuse for keeping a misleading name after you know better.
This applies to JavaScript especially, where the dynamic nature of the language makes naming one of the few static documentation tools you have. Understanding foundational data handling — like how JavaScript array methods actually behave — helps you name things based on what they genuinely do, not what you vaguely intended.
Delete Comments That Don't Add Value
A comment that restates what the code already says is noise. A comment that contradicts what the code does is a lie. The rule of thumb: comments should explain why, not what. If you need to explain what, the code itself needs to be clearer.
Enforce Contracts, Not Just Conventions
TypeScript is one of the best tools available for making implicit contracts explicit. If a function accepts a specific shape of data, define that shape. Don't let any creep in. Don't return object when you mean Invoice. Types are the closest thing to legally binding documentation that code has.
// Before: a lie in disguise
function processPayment(data: any): any { ... }
// After: honest about its contract
function processPayment(payload: PaymentPayload): PaymentResult { ... }Setting up projects with strict TypeScript from the start — like a React app with TypeScript and TailwindCSS — makes it much harder to accidentally lie about data shapes.
Treat TODOs as Technical Debt Tickets
Every // TODO comment should have an owner and a ticket number. If it doesn't, it's not a TODO — it's a wish. Tools like ESLint can be configured to warn on unlinked TODO comments. Making the invisible visible is the first step to addressing it.
Update Your Docs When You Change Behavior
This sounds obvious. It almost never happens without discipline. The fix is process: make documentation updates part of your definition of done. If a PR changes behavior without updating docs, it's not done. This is a team norm, not a technology problem. No AI can enforce team norms.
The Honest Developer's Advantage
Here's the flip side: when your codebase is genuinely honest — clear names, accurate comments, enforced types, up-to-date docs — AI tools become dramatically more useful. Now the AI is reading accurate context. Its suggestions are grounded in real behavior. Its generated tests reflect actual logic. You get the amplification effect working in your favor instead of against you.
Developers who invest in codebase honesty before adopting AI tooling will outperform those who use AI as a shortcut around honesty. The gap will only widen as AI tools become more deeply embedded in the development workflow.
Understanding the full software development lifecycle — from planning to maintenance — is part of what makes this kind of honesty sustainable. A solid grasp of the SDLC reminds you that the code you write today is the context someone else (or an AI) will work in tomorrow.
Wrapping Up
The lies developers tell aren't villainous — they're symptoms of pressure, speed, and the gap between intention and execution. But they're real, they compound, and they silently undermine everything built on top of them.
AI coding tools are not a remedy for this. They're accelerants. Applied to honest, well-structured code, they're a genuine force multiplier. Applied to a codebase full of stale comments, misleading names, and undocumented assumptions, they scale the problem faster than you can debug it.
The fix is unglamorous: rename the misleading function, delete the wrong comment, add the type, write the real README. Do it before you ask an AI to help you extend the feature. Your future self — and your AI assistant — will both have a much better time.
For further reading on the original framing of this topic, see the source article on dev.to by Sylwia Łask.
