I've Been Using AI to Code for 8 Months - Here's What Actually Works

I started with GitHub Copilot in January 2024 using VS Code 1.86. My first week was frustrating as hell - it kept suggesting deprecated React patterns like componentDidMount for React 18 functional components and generated a useEffect hook with a missing dependency array that caused infinite loops. That infinite loop hit our staging environment running Node.js 18.17.0 and crashed it twice before I figured out what was happening. Took me 3 hours to trace it back to a Copilot suggestion that looked innocent but was re-rendering our entire dashboard component 50 times per second.

Some Stack Overflow survey shows like 60-something percent of developers are using AI tools now, but those surveys never mention how much it sucks at first. The Anthropic research on developer productivity shows mixed results - some developers see 20-30% gains, others report initial productivity drops.

What Actually Improves vs. What's Still Garbage

After 8 months of real use, here's the truth:

AI is genuinely faster for:

• Boilerplate that follows existing patterns in your codebase (Copilot excels here)
• Converting data structures (JSON to TypeScript interfaces, SQL to models)
• Writing tests when you already know what should be tested (Jest patterns work well)
• Explaining code that someone else wrote (Claude is surprisingly good at this)

AI will waste your time on:

• Complex business logic (it doesn't understand your product or domain-specific requirements)
• Debugging race conditions or async/await issues)
• Performance optimization (it loves generating inefficient code patterns)
• Security-sensitive code (never trust AI with auth or payments)

I was genuinely slower for the first 2 months. You spend more time babysitting AI suggestions than just writing the damn code yourself.

The Tools That Actually Work

GitHub Copilot (Free/Pro): GitHub changed their pricing in 2025. Now there's a free tier with 2,000 completions and 50 chat requests per month, plus paid Pro ($10/month) and Pro+ ($20/month) tiers. Great for autocomplete, terrible for architecture. It's trained on every shitty GitHub repo and will happily suggest vulnerable patterns. But once you learn to ignore its bad suggestions, the autocomplete is genuinely useful. The free tier is perfect for trying it out - I burned through those 2,000 completions in 5 days because I was letting it complete every fucking variable name.

Cursor ($20/month): Game-changer for large refactors. It understands your entire codebase and can make multi-file changes that actually work. Worth every penny if you're maintaining a project with >10k lines of code. Check out their features documentation for the full workflow.

Claude Code ($20/month): The most overhyped. Yes, it can generate entire features, but the code quality is inconsistent. Great for prototyping, dangerous for production without serious review. The Anthropic safety research explains why, but doesn't help when you're debugging their hallucinated functions.

The Hidden Costs Nobody Talks About

Your bills will explode. My Cursor usage went from $20 to $180 in month three when I started using it for everything. Claude Code hit usage limits and surprised me with a $300 bill. Check the Cursor pricing tiers and Anthropic's usage pricing to understand how costs scale. Budget at least $50-100/month per developer if you're using these tools seriously.

Switching back and forth between AI and your brain is exhausting. Going back and forth between AI suggestions and your own thinking breaks flow state. Research on developer productivity shows context switching can reduce efficiency by 25%. Some days I'm more productive just turning everything off and coding like it's 2019.

When AI Actually Saves Time

Large migrations and refactors where you need to touch 50+ files and hate your life. AI excels here. I migrated our Redux store to Zustand in about 2 hours using Cursor. Course, I spent another hour fixing the 3 things it broke (forgot to update two import paths and completely fucked up our async actions), but still faster than doing it by hand. Check out migration patterns that work well with AI assistance.

Documentation and comments. AI writes better docs than most developers (including me). It's good at explaining complex code in simple terms. The Google developer documentation guide shows what good docs look like - AI can generate similar quality.

Converting between formats. Need to turn a CSV into JSON? Perfect AI task. Converting API responses to TypeScript types? AI nails it every time. Tools like Postman and Insomnia now integrate AI for this.

Look, AI makes some stuff faster. But it's not magic - it's good at the boring shit and terrible at anything that requires actual thinking. The GitHub productivity research shows specific use cases where gains are measurable.

The reality after 8 months: AI coding tools are like having an overconfident intern with access to every bad code example on GitHub. They excel at boilerplate and patterns, fail at architecture and edge cases. Use them right, and you'll ship faster. Use them wrong, and you'll waste time debugging their confident bullshit.

Start Simple: Just Add Copilot (Week 1-2)

Don't overthink this. Start with the free GitHub Copilot tier - you get 2,000 completions per month, which is enough to evaluate if it's useful for your workflow. Install the extension from the VS Code marketplace and just use it for autocomplete. That's it.

## Install Copilot - takes 2 minutes
code --install-extension github.copilot
code --install-extension github.copilot-chat

What you'll notice immediately:

• Copilot suggests decent boilerplate (React components, API calls)
• It's terrible at understanding your specific business logic
• It suggests deprecated patterns constantly (especially with React hooks)
• Sometimes it's genuinely helpful, sometimes it's noise - read the Copilot usage patterns research

My advice: Accept good suggestions, ignore bad ones. Don't try to optimize your prompts yet - just get used to the workflow interruption. The Copilot docs have good tips for beginners.

Add One More Tool: Cursor or Claude Desktop (Week 3-4)

Once Copilot feels natural, add one more tool. I recommend Cursor if you work on large codebases (>10k lines), Claude Desktop if you need help with one-off problems.

Cursor ($20/month) - Worth it for refactoring

• Great for changing function signatures across multiple files
• Actually understands your project structure - check their architecture docs
• Crashes on very large files (>5k lines sometimes) - see performance troubleshooting

Claude Desktop (free tier) - Good for explaining code

• Paste confusing code and ask "what does this do?" - works with any programming language
• Great for converting between formats (JSON to TypeScript, etc.)
• Can't see your codebase, so limited for project-specific stuff

Warning: Don't install 5 AI tools at once like I did. You'll spend more time switching between tools than coding and your brain will melt from decision fatigue. The tool fatigue research shows why this is a terrible idea.

Real Workflow Integration (Months 2-3)

After 6-8 weeks, you'll start developing muscle memory. This is when AI tools become actually productive:

When building something new:

1. Write the feature in plain English as a comment - follow clean code principles
2. Ask Cursor to implement based on existing patterns in your codebase
3. Review and test the generated code carefully - use testing best practices
4. Fix the inevitable bugs (AI doesn't understand edge cases)

For refactoring:

1. Use Cursor to rename functions/variables across files - refactoring techniques
2. Let it handle import updates and type changes
3. Run your tests to catch what it broke (it will break something)

For debugging:

1. Turn off AI suggestions - they're distracting when debugging - see debugging strategies
2. Use Claude Desktop to explain complex error messages
3. Never trust AI to fix production bugs without understanding the root cause analysis

After a few months of this shit:

By month 4, you'll know which tasks AI handles well and which ones waste your time.

AI is great for:

• Generating test boilerplate from existing functions
• Converting large data structures (API responses to types)
• Writing documentation that you'd never write manually
• Explaining unfamiliar codebases (paste a complex function, ask for explanation)

AI still sucks at:

• Complex state management (React Context, Redux patterns)
• Performance optimization (it generates inefficient solutions)
• Security-sensitive code (always review auth/payment logic manually)
• Integration with third-party APIs (it hallucinates method names)

Team Adoption Reality

If you're introducing AI tools to a team:

Start with volunteers - Don't mandate tools. Let interested developers experiment first.

Track actual metrics - Before/after feature completion times, not developer happiness surveys.

Budget for the learning curve - Expect 2-6 weeks of reduced productivity while people learn new workflows.

Prepare for pushback - Senior developers especially will resist tools that change their established workflows.

Common team issues I've seen:

• Code style inconsistency when different developers use different AI tools
• Junior developers becoming dependent on AI for basic tasks
• Time wasted in "AI tool selection" debates instead of shipping features

Cost Reality Check

Month 1: $0-20/month (Free Copilot + Claude free tier)
Month 3: $30-60/month (Pro Copilot + Cursor or paid Claude)
Month 6: $80-200/month (heavy usage triggers overages, especially with Claude)

The cost creep is real. Budget accordingly and track whether the productivity gains justify the expense.

What Doesn't Work (Despite What You Read Online)

Voice coding: Tried it for a week because some YouTube guru said it was the future. Background noise breaks it constantly, dictating variable names like "camelCaseMyVariableName" is awkward as fuck, and you still need a keyboard for editing. Went back to typing like a normal human after my dog barked and it generated 500 lines of garbage.

AI-generated deployment scripts: These tools don't understand your infrastructure. They'll generate Docker files that work on their machine, not yours.

Fully autonomous coding: Claude Code can't really work independently. It needs constant babysitting like a drunk intern and generates code that requires more debugging than if you'd written it yourself.

"Prompt engineering" courses: Just talk to the AI like you'd explain something to a junior developer who's had too much coffee. You don't need a $200 course for this shit.

The key is gradual adoption, realistic expectations, and understanding that AI tools are assistants, not replacements for thinking about your code.

Security Reality:

AI Tools Will Try to Kill Your App

The Day AI Almost Broke Our Production Database

Three months into using AI tools, I was feeling confident.

Too confident. Copilot suggested this MongoDB query in our Node.js 18.20.0 app while I was fixing a "simple" user cleanup task:

// AI suggested this nightmare
db.sessions.deleteMany({ userId: { $in: user

Ids } });

The variable userIds was undefined in that context.

If it had run, it would have deleted ALL user sessions in production

• 50,000+ users logged out instantly, probably during peak hours. I caught it during code review while sipping coffee, nearly choked when I realized what almost happened. AI tools give zero fucks about data safety. The OWASP AI Security Guide explains why this happens, but doesn't help when you're debugging their dangerous bullshit at 3am wondering if you should update your resume.

Real Security Issues I've Encountered

API Keys in Generated Code AI loves suggesting hardcoded secrets like it's fucking 2015.

I've seen Copilot suggest:

const API_KEY = \"sk-1234567890abcdef\"; // DON'T DO THIS

Read the secrets management guide for proper practices.

SQL Injection by Default AI-generated SQL queries rarely use parameterized statements:

-- AI suggested this garbage
SELECT * FROM users WHERE id = ${userId}; // Injection central

Deprecated Security Patterns AI training data includes years of insecure code.

It will suggest:

• eval() for JSON parsing (dangerous practice)

• innerHTML for user-generated content

• Unvalidated file uploads

• Weak password hashing (MD5, SHA1)

My Security Workflow That Actually Works

Never let AI touch auth or payments (learned this the hard way) I write all authentication, authorization, and payment processing code myself.

AI can help with boilerplate, but I review every line that touches sensitive data.

Rule 2: Use a Security Linter Install ESLint security plugins that catch AI-generated vulnerabilities:

npm install --save-dev eslint-plugin-security

Also consider Semgrep for more advanced static analysis.

**Rule 3:

The 10-Second Rule**

Before merging any AI-generated code, ask: "What's the worst thing that could happen if this code is malicious?" If the answer is bad, review it more carefully.

Use the security review checklist.

Package Hallucination is Real

AI tools suggest packages that don't exist like they're reading from some alternate universe npm registry.

I've seen:

• react-secure-auth (doesn't exist, wasted 30 minutes)

• express-validator-pro (not a real package, but sounds legit)

• crypto-safe (fake security library that sounds official)

• mongoose-validator-plus (complete fiction)

• jwt-secure-handler (I almost installed this before realizing)

Always check npm/GitHub before installing suggested packages or you'll be debugging phantom imports for hours.

Production Deployment Reality Check

Don't use AI-generated deployment scripts AI has no idea about your infrastructure, networking, or security requirements.

It will generate Docker files that work in development and fail in production.

What AI deployment scripts get wrong:

• Security contexts (runs as root)

• Resource limits (no memory/CPU constraints)

• Health checks (missing or inadequate)

• Secrets management (hardcoded values)

• Networking configuration (exposes wrong ports)

Team Security Guidelines

For Code Reviews:

• Treat AI-generated code like it was written by an untrusted external contractor

• Look specifically for hardcoded secrets, SQL injection, and XSS vulnerabilities

• Never approve auth/payment logic without understanding every line

For Deployment:

• Run security scanners on all AI-generated code

• Use tools like npm audit, safety (Python), or bundler-audit (Ruby)

• Test AI-generated code in isolated environments first

What Works for Security

Good AI use cases:

• Generating test data (but not production data)

• Creating documentation templates

• Boilerplate CRUD operations (with manual security review)

• Converting between safe data formats

Bad AI use cases:

• Authentication logic

• Payment processing

• Database migrations

• Security configurations

• Anything involving user permissions

The Uncomfortable Truth About AI Security

Most security issues with AI-generated code aren't dramatic vulnerabilities

• they're subtle logic errors that cause data corruption, race conditions, or performance problems that only show up in production at 3am on a Friday.

The real danger isn't AI trying to hack you

• it's trusting suggestions you don't understand and having them blow up in production.

Don't be that developer.

My Current Security Setup

1. AI generates code → 2. Security linter scans → 3. I review for logic/security → 4. Test in isolation → 5. Deploy with monitoring

This catches most issues, but I still find bugs that make it to production. AI tools are powerful, but they're also unpredictable.

Bottom line: Use AI tools for productivity, but assume they're trying to write insecure code that will get you fired, because they often are.