AI for Coding Is Overhyped Garbage
AI-for-coding tools, as they stand today, are massively overhyped. Big tech wants you to believe we're on the brink of replacing engineers with neural network magic. Spoiler alert – we're not. Not even close.
Sure, GitHub #Copilot, #ChatGPT, Cody, and all the other shiny #LLM-powered toys can spit out code. But let's not confuse autocomplete on steroids with actual engineering.
“Smart” Autocomplete ≠ Intelligence
Most of what #AI coding tools do is glorified autocomplete trained on billions of lines of code scraped from #GitHub and Stack Overflow. They don't understand your business logic, #architecture, or the edge cases that actually matter. They generate code that looks right. That's not intelligence-that's pattern regurgitation.
Ask it for something non-trivial-like a thread-safe memory cache, a zero-downtime deploy strategy, or a nuanced permission model-and you'll get half-working boilerplate riddled with assumptions. But hey Sparky, it compiles! Damn, it compiles.
Hallucinations Are a Feature, Not a Bug!
AI will confidently give you broken code, made-up APIs, or solutions that subtly break when it matters most. Even Copilot's own disclaimers admit it might produce insecure or incorrect results.
Worse, it wraps bad code in syntactic sugar-clean, elegant formatting that gives you a false sense of correctness. It's like a con man in a tailored suit.
The “Productivity Boost” Myth
Proponents love to throw around claims like “developers are 2x more productive!”-based on cherry-picked metrics like lines of code or time-to-prototype. But here's what they don't tell you:
You'll spend more time #debugging garbage AI code than writing it yourself.
You still need to understand and own the codebase. AI doesn't refactor intelligently or track architectural decisions.
Code generated by AI often rots faster, because it wasn't built with real understanding.
If you don't know what you're doing, AI will dig your hole faster. If you do know what you're doing, you'll spend time double-checking everything it spits out.
It Can't Scale with You
AI tools operate in small windows-one function, one file, maybe a few thousand tokens of context. That's fine for toy projects, but real systems span hundreds of files, services, and design trade-offs.
Until LLMs can hold entire project context and reason about interdependent modules, they'll remain glorified interns with memory loss.
Engineers Still Think, AI Just Predicts
The real work of engineering is design, debugging, reasoning, and trade-off analysis. Not typing.
AI doesn't understand:
- Latency budgets
- Distributed systems complexity
- Regulatory compliance
- Concurrency pitfalls
- Systemic risks or ethical tradeoffs
It can't make judgment calls. It can only remix what it's seen.
So What's It Good For?
Let's be fair-it's not completely useless.
- Scaffolding boilerplate?
- Rewriting code in another language?
- Quick regex or one-off scripts?
- Copy-pasting common patterns?
But these aren't the things that make or break software engineering. They're the crumbs at the edge of the problem.
Real Examples
Now let me show you what I've just described above based on real-world examples.
Insecure Code: Hardcoded Secrets and Broken Auth
Copilot repeatedly generates #AWS keys, passwords, and API tokens in code. Why? Because it’s trained on open-source code-including the bad stuff.
NYU Research Study (2021): When prompted with common dev patterns, Copilot generated code with security vulnerabilities in 40% of cases.
Examples:
- Hardcoded cryptographic keys
- SQL injections via unsafe string interpolation
- Broken authentication checks (e.g., if (user.isAdmin) with no null-check)
These vulnerabilities are especially dangerous because:
- They look syntactically valid.
- They pass linting and even compile.
- They give developers a false sense of trust.
Semantic Hallucination: Made-up Functions That Don’t Exist
Ask #ChatGPT for a Python script to send SMS using #Twilio, and you might get this:
from twilio.client import TwilioClient
Except… that module doesn’t exist. The correct import is:
from twilio.rest import Client
This isn’t a typo. It’s a hallucination based on plausible code. And the scary part? Many devs-especially juniors-won’t catch it immediately.
Subtle Logic Errors That Look Correct
A common trap: asking for a binary search implementation or an algorithm. You get this:
def binary_search(arr, target):
low = 0
high = len(arr)
while low < high:
mid = (low + high) // 2
if arr[mid] < target:
low = mid + 1
else:
high = mid
return low
Looks good, right? Except this is off-by-one in several cases depending on whether you want the first occurrence, existence check, or insertion point. And guess what-LLMs never clarify intent unless you do. This means you could deploy silently broken behavior into a critical function and not notice until production.
Copilot Leaked GPL Code! Oh My...
Not just insecure-potentially illegal.
Reproduced:
- Full function bodies from curl, FFmpeg, and GPL-licensed drivers.
- With variable names, comments, and formatting intact.
Implication: If you use #Copilot without care, you may unknowingly ship code that violates licensing, exposing your project or company to lawsuits.
Zero Context Beyond Current File
Try asking an AI to refactor a function that’s referenced across a codebase with side effects in three modules. It will:
- Rename the function locally
- Forget to update calls elsewhere
- Ignore async or exception context
- Skip type changes in interfaces
In a real dev environment, this leads to:
- Uncaught runtime errors
- Broken builds
- Test failures, or worse, silent logic corruption
Copilot’s newer workspace tools try to help here, but the underlying #LLM still lacks deep multi-file reasoning.
Enough! I Stop on This. Let's wrap up!
Treat AI coding tools like a calculator-useful, but only if you already understand the problem. Otherwise, you're just automating confusion. If you expect them to write reliable, maintainable, secure code at scale-you're in for a brutal awakening.