AI Coding: The Emperor’s New Code

Six months ago, everyone said AI would replace developers.  Today, most AI-generated code needs more debugging than writing from scratch.

The AI coding revolution was supposed to be here by now.  GitHub Copilot would write our applications.  ChatGPT would eliminate junior developers.  Cursor would make senior developers 10x more productive.

Instead... we’re drowning in plausible-looking code that doesn’t quite work.

TL;DR: AI coding tools are powerful assistants, not replacements.  Most organisations don’t understand the difference, leading to slower delivery and security risks masked by faster initial code generation.

The Demo vs Reality Gap

AI coding tools excel at demos.  Watch them generate a React component, write a SQL query or scaffold a REST API.  It looks magical.  Ship it to production and discover the magic was sleight of hand.

The generated code works for the happy path.  It fails on edge cases, it ignores error handling and creates security vulnerabilities that won’t surface until an audit - or worse, a breach.

AI writes code that looks right faster than humans can.  But “looks right” and “is right” are very different things.

Where AI Actually Helps

AI coding isn’t useless - it’s just not what was promised.

It’s brilliant at boilerplate.  Database migrations, API endpoints, test scaffolding - repetitive code where the patterns are well-established and the requirements are clear.  AI saves genuine time here.

It’s helpful for exploring unfamiliar APIs.  Need to use a library you’ve never touched?  AI can generate working examples faster than reading documentation.  Just don’t trust those examples in production without verification.

It’s decent at refactoring within narrow constraints.  “Extract this function” or “convert this to use async/await” - mechanical transformations where the logic doesn’t change.

Where AI Falls Apart

Complex business logic breaks AI.  The moment you need to understand domain-specific requirements, handle intricate state management, or coordinate between multiple systems, AI-generated code becomes a liability.

Security considerations are invisible to AI. It doesn’t understand threat models. It can’t evaluate whether input validation is sufficient. It generates code that passes tests but fails security review.

Performance optimization requires understanding that AI doesn’t possess.  It’ll give you working code with O(n²) complexity when O(n log n) was achievable.  It’ll make database calls in loops.  It’ll load entire datasets into memory.  It will switch SQL dialect from PostgreSQL to MariaDB without you even noticing, until you try to roll out a schema update.

The Real Cost

Here’s what nobody tracks: the debugging time.

You save 30 minutes having AI write a function.  You spend 2 hours figuring out why it fails intermittently.  You spend another hour hardening it against edge cases the AI never considered.  You spend a day refactoring when you realize the AI’s approach doesn’t scale.

The code arrived faster.  The feature took longer.

What This Means for AI Adoption

AI coding tools are assistants, not replacements.  They’re most valuable in the hands of experienced developers who can quickly identify what’s wrong with generated code and fix it efficiently.

Junior developers using AI coding tools learn to rely on code they don’t understand.  Senior developers using AI coding tools get boilerplate written faster so they can focus on the problems that actually require expertise.

The gap between those two outcomes is enormous.

The Lanboss Perspective

At Lanboss AI, we help organisations adopt AI tools effectively - which means understanding both their capabilities and their limitations.  AI coding tools can improve productivity, but only when teams understand what they’re actually good for and what still requires human expertise.

The emperor isn’t naked.  But he’s not wearing the outfit we were promised either.