AI Won't Replace Developers

Let's get this out of the way: AI is a tool that makes developers more productive, not a replacement. The developers who embrace it write better code faster. The ones who ignore it do more tedious work than they need to.

Where AI Delivers Real Value

Boilerplate and scaffolding

Setting up a new component, API endpoint, or database migration involves a lot of repetitive code. AI generates the structure in seconds, letting you focus on the actual logic.

"Create a TypeScript Express endpoint that accepts a POST request with a JSON body containing email and password, validates both fields, and returns a JWT token."

Understanding unfamiliar code

Jumping into a new codebase? Paste a function and ask:

"Explain what this function does, what edge cases it handles, and what it misses."

Debugging

Describe the bug, paste the error, and include the relevant code. AI often spots the issue immediately, especially for common mistakes like off-by-one errors, null references, or async/await misuse.

Writing tests

Nobody loves writing tests. AI generates test scaffolding, edge case suggestions, and boilerplate assertions so you can focus on the scenarios that actually matter.

Documentation

Paste a function or module and get clear documentation:

"Write JSDoc comments for this function including parameter descriptions and return value."

What AI Gets Wrong in Code

• Complex architecture decisions: AI can suggest patterns but shouldn't make design decisions for your system
• Security-critical code: Always review AI-generated code for vulnerabilities
• Performance optimization: AI knows common patterns but doesn't know your specific bottlenecks
• Business logic: AI doesn't understand your domain requirements; you do

The Right Model for Code

Different models have different coding strengths. Some are better at Python, others at TypeScript, others at system-level languages. Having access to multiple models means you can use the one that's strongest for your current language and task.