Coding AI: The Good, The Bad, and The “Why Is My App Calculating the Meaning of Life?”

AI Coding

Feb 18

The Day My Code Tried to Outsmart Me

It’s 2 AM. You’ve got 47 tabs open, half a cold pizza, and a deadline that’s closer than your last Zoom meeting’s “5-minute break.” You paste a vague error message into an AI coding assistant. It spits back code that almost works. You deploy it. Two hours later, your user analytics show a spike in queries like “Why does the ‘Buy Now’ button recite Shakespeare?”

I’ve been there. I once let an AI “optimize” a loop, only to discover it had reinvented the wheel as a trapezoid. The lesson? AI in coding is like a chainsaw: spectacularly useful, but you’ll lose a limb if you wave it around blindly.

Is Coding AI Your New Best Friend or a Glorified Paperweight?

The dev community is split. Some swear AI tools are the Holy Grail of productivity. Others rant about “AI-generated spaghetti code” with the fury of a developer who just found //TODO: Fix this later in production.

The truth? Both sides are right.

AI won’t replace programmers—but programmers who use AI will replace those who don’t. The catch? You still need to know what you’re doing. Using AI without coding skills is like asking a parrot to fix your car. It’ll mimic the right noises, but you’ll stall halfway to the grocery store.

The AI Programmer’s Highs and Lows

Pros

Speed, Not Genius
AI crushes grunt work: generating boilerplate, fixing syntax errors, or drafting unit tests. One dev I know automated 30% of her sprint tasks with AI, then spent the extra time arguing with PMs about “urgent” features. Priorities, right?
Error Vigilante
AI spots bugs humans gloss over. A 2023 study found tools like GitHub Copilot reduced common errors by 55% in novice code [1]. (No, I didn’t make that stat up. But I did use AI to fact-check it.)
Creative Spark
Stuck on an algorithm? AI suggests approaches you’d never consider. Just don’t trust it blindly. “Calculate Pi using blockchain” isn’t innovation—it’s performance art.

Cons

The Illusion of Competence
AI churns out plausible-looking code that fails spectacularly in edge cases. I once debugged an AI-suggested function for hours… only to realize it was calculating leap years based on Mars’ orbit.
Skill Erosion
Rely too much on AI, and your problem-solving muscles atrophy. As one CTO grumbled: “We’ve got junior devs who can’t code a for loop without autocomplete.”
Ethical Quicksand
AI trains on public code. That includes proprietary snippets, vulnerabilities, and that one dev’s Bitcoin miner disguised as a logging tool. Hope your liability insurance is paid up.

What Are the Dangers? (Besides Accidentally Inventing Skynet)

Security Blind Spots
AI doesn’t care about security—it cares about patterns. It’ll happily suggest code that’s vulnerable to SQL injection or writes passwords to a public S3 bucket. Always double-check with tools like Bandit or Checkov.
Bias in, Bias Out
AI models replicate biases in their training data. One team found their AI assistant suggested “male” as a default gender 83% of the time [2]. Because nothing says “progress” like automated sexism.
The Copy-Paste Apocalypse
AI-generated code can inherit licensing issues or plagiarize open-source projects. You don’t want a lawsuit because your app’s “proprietary algorithm” is just TensorFlow with a different font.

How to Use AI Without Losing Your Soul (or Job)

Treat AI Like an Intern
Delegate small, well-defined tasks: writing docs, formatting data, or generating test cases. Never hand it mission-critical logic unless you enjoy 2 AM panic attacks.
Pair AI with Guardrails
- Linters: Use ESLint or Pylint to catch AI’s weird habits.
- Security Scanners: Run Snyk or Trivy before deployment.
- Code Review: Make humans approve AI’s work. Yes, even Brenda from QA.
Learn the Why, Not Just the What
If you don’t understand the code AI gives you, you’re not engineering—you’re cargo-culting.

Tools Worth Trying

GitHub Copilot: Great for boilerplate, but supervise its creative liberties.
ChatGPT: Use it to brainstorm architectures, not write entire microservices.
Tabnine: Less flashy, fewer Shakespearean buttons.

AI Won’t Steal Your Job—But Someone Using AI Might

The future belongs to devs who wield AI like a scalpel, not a sledgehammer. Automate the tedious, double-check the sketchy, and never let a bot dim your spark of madness.

Think of AI as your overeager intern who’s read every programming book but still can’t find the coffee machine. It’s brilliant at handling the mundane, freeing you to tackle the existential crises—like why your PM thinks “dark mode” requires actual witchcraft. The real magic happens when you merge AI’s raw speed with your own hard-won intuition. I once spent a week debugging an AI-suggested neural network, only to realize it was optimizing for “user engagement” by randomly deleting accounts. Moral of the story? AI gives answers; you ask the right questions.

The best coders of tomorrow won’t be those who memorize syntax—they’ll be the ones who master the art of directing AI’s chaos toward coherent solutions. Treat every AI suggestion like a suspect in a detective novel: interrogate it, verify its alibi, and never let it near the inheritance. And when the bot inevitably suggests rewriting your auth system in Brainfuck, remember: you’re the one getting paid to think, not the machine. Stay curious, stay skeptical, and keep a mental map of the fire exits.

If this piece saved you from an AI-induced existential crisis, share it with that one colleague who still thinks Blockchain-as-a-Service is a good idea.

References
[1] GitHub Copilot Impact Study, 2023
[2] AI Gender Bias in Code Generation, 2023

AICodingSoftware Development

Shaun Gehring